WO2014085081A1 - Système et procédé d'analyse de rayonnement - Google Patents

Système et procédé d'analyse de rayonnement Download PDF

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Publication number
WO2014085081A1
WO2014085081A1 PCT/US2013/069682 US2013069682W WO2014085081A1 WO 2014085081 A1 WO2014085081 A1 WO 2014085081A1 US 2013069682 W US2013069682 W US 2013069682W WO 2014085081 A1 WO2014085081 A1 WO 2014085081A1
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Prior art keywords
radiation
analysis
optimization
efficiency
model
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PCT/US2013/069682
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English (en)
Inventor
Frazier Lester BRONSON
Ramkumar Venkataraman
Andrey Leonidovich BOSKO
Nabil Menaa
William Robert Russ
Valery Vasilyevich ATRASHKEVICH
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Canberra Industries, Inc.
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Publication date
Priority claimed from US13/674,649 external-priority patent/US8666711B2/en
Application filed by Canberra Industries, Inc. filed Critical Canberra Industries, Inc.
Priority to EP13858849.6A priority Critical patent/EP2917849A4/fr
Publication of WO2014085081A1 publication Critical patent/WO2014085081A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T7/00—Details of radiation-measuring instruments
    • G01T7/005—Details of radiation-measuring instruments calibration techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/16—Measuring radiation intensity
    • G01T1/167—Measuring radioactive content of objects, e.g. contamination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/36—Measuring spectral distribution of X-rays or of nuclear radiation spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V5/00—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity
    • G01V5/20—Detecting prohibited goods, e.g. weapons, explosives, hazardous substances, contraband or smuggled objects
    • G01V5/281—Detecting prohibited goods, e.g. weapons, explosives, hazardous substances, contraband or smuggled objects detecting special nuclear material [SNM], e.g. Uranium-235, Uranium-233 or Plutonium-239

Definitions

  • the present invention relates generally to radiation measurement techniques and systems/methods associated with optimizing efficiencies associated with these techniques.
  • the present invention may be applied advantageously to the automated computation efficiencies of a quantitative radiation measurement coming from the calibration process when the calibration method is an imprecise representation of the actual sample measurement conditions. While not limitive of the invention teachings, the present invention may in some circumstances be advantageously applied to categories including U.S. Patent Classifications 438/14; 250/252.1; 250/363.09; 702/8; and 850/63.
  • the quantification of the amount of radioactivity measured by a radiation detector/sensor is not an exact process. There is always an uncertainty in the quantity that has been determined as "measured" by the radiation detector/sensor .
  • counting statistics One contribution to the total uncertainty is commonly called “counting statistics" and arises from the fact that the measurement process counts discrete events that occur in a random manner from the decay of the radioactive atoms.
  • counting statistics One contribution to the total uncertainty is commonly called “counting statistics" and arises from the fact that the measurement process counts discrete events that occur in a random manner from the decay of the radioactive atoms.
  • the evaluation of the uncertainty from this process is well known and can be determined by mathematical techniques.
  • Calibration factors are necessary to relate the measured quantity to the quantity emitted from the radioactive source. Calibration factors are also referred to as interaction probabilities or detection efficiencies. These calibration factors may be determined by the measurement of well-known radioactive sources that have been prepared in a manner to closely mimic the unknown sample being measured. Alternatively, calibration factors can be determined by a mathematical process whereby the radiation physical parameters of the detector/sensor and the source/sample are defined, and the physics of radiation interaction with materials is defined, and where the probability of radiation from the sample interacting with the sensor is computed mathematically.
  • One such example of mathematical computation method for efficiency calibration is described in the U.S. Patent 6,228,664, issued on 5/8/2001 to Frazier Bronson and Valerii V. Atrashkevich for "CALIBRATION METHOD FOR RADIATION SPECTROSCOPY. "
  • either the source-based calibration factor or the mathematically computed calibration factor may be used to convert the measurement instrument output into a radioactivity quantity value for the sample being measured.
  • the radioactive calibration source or the mathematical calibration model perfectly represents the sample being measured. This is due to the random factors involved in the radioactive decay and measurement process. The method of computation of this portion of the uncertainty in the calibration factor is also well known.
  • the traditional method of optimizing the efficiency curve includes an expert investigation of the measured data for consistency with the efficiency calibration. It is currently a manual process, which strongly relies on the expert judgment and requires multiple iterations. During the review process the expert can use different benchmarks to optimize the efficiency calibration.
  • the benchmarks could be results from isotopic codes such as Multi -Group Analysis Code (MGA) , Multi -Group Analysis Code for Uranium (MGAU) , or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) , activities from multi- line nuclides, and multiple counts of the same item taken in different geometries (from the side, bottom, top etc.).
  • MCA Multi -Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • efficiency calibration is commonly carried out by measuring a representative source standard or using a suitable mathematical method.
  • certain simplifying assumptions are made with regards to the homogeneity of the sample matrix, the uniformity of radioactivity distribution, dimensions of sample, etc.
  • the source dimensions, the matrix material and density, and the radioactivity distribution may not be well known.
  • the efficiencies may not be very accurate since the calibration source geometry may not be very representative of the measured sample geometry.
  • the prior art teaches a modus operandi that manufactures a standard source or builds a mathematical model for efficiency calibration, using an educated guess with regards to the sample dimensions and homogeneity. These educated guesses are often simplifying assumptions that do not adequately represent the sample that may be encountered in the field. As a result, the activity results are assigned a large uncertainty in order to bound the conditions that make the sample different from the calibration standard.
  • the objectives of the present invention are (among others) to circumvent the deficiencies in the prior art and affect the following objectives:
  • (1) provide for a radiation analysis system and method that permits automatic optimization of absolute efficiencies of a gamma ray detector for any source shape or size (based on the available data in the measuremen (s) ) ; (2) provide for a radiation analysis system and method that permits nuclide identification and quantification based on the results of automatic optimizations of absolute efficiencies;
  • (6) provide for a radiation analysis system and method that permits a calibration method to be used to automatically optimize efficiencies at gamma ray energies from any radionuclide of interest in gamma spectrometry applications (special nuclear materials, activation products, fission products, etc . ) ;
  • (7) provide for a radiation analysis system and method that permits determination of the optimum value of the full energy peak efficiency at a gamma ray energy emitted by a radioactive source whose physical and radiological characteristics are not- well-known;
  • the present invention teaches an automated system/method to optimize full energy peak efficiencies.
  • the NOT-WELL-KNOWN parameters are varied in an automated fashion and the optimal efficiency shape and magnitude are determined based on available benchmarks in the measured spectra.
  • the benchmarks could be results from isotopic codes such as Multi -Group Analysis Code (MGA) , Multi -Group Analysis Code for Uranium (MGAU) , or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) , activities from multi-line nuclides, and multiple counts of the same item taken in different geometries (from the side, bottom, top, etc.).
  • MGA Multi -Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • the efficiency optimization is carried out using either a random search based on standard probability distributions, or using numerical techniques that carry out a more directed (smart) search.
  • the optimized efficiencies, along with the associated uncertainties, are used in the analysis of the measured radiation spectrum from the given sample, and the activities or masses of radionuclides.
  • the present invention uses benchmarks available in the measured data to automatically optimize the calibration factor or efficiency of a radiation sensor for that portion of the uncertainty arising from imprecise knowledge of the exact measurement conditions. This is generally accomplished by the following:
  • the present invention may also be embodied in a radiation analysis system/method that incorporates an optimization technique that obtains consistent results between spectroscopic measurements and dose measurements.
  • the mathematical model is adjusted based on the available benchmarks in the spectral data and dose rate measurements using a non- spectroscopic detector such as a Geiger-Miiller (GM) tube.
  • GM Geiger-Miiller
  • FIG. 1 illustrates an exemplary system application context in which the present invention is typically incorporated and depicts some of the basic components of a radiation measurement system
  • FIG. 2 illustrates an exemplary system block diagram of a preferred embodiment of the present invention depicting a typical invention application setup context
  • FIG. 3 illustrates an exemplary system block detail diagram of a preferred embodiment of the present invention
  • FIG. 4 illustrates an exemplary system block detail diagram of a preferred embodiment of the present invention incorporating the use of model parameters that are WELL- KNOWN (WNP) and NOT-WELL-KNOWN (NWP) ;
  • WNP WELL- KNOWN
  • NWP NOT-WELL-KNOWN
  • FIG. 5 illustrates an exemplary system block detail diagram of a preferred embodiment of the present invention incorporating smart parameter searching as well as random statistical parameter searching;
  • FIG. 6 illustrates an exemplary method detail flowchart of a preferred method embodiment of the present invention implementing an automated operation of advanced ISOCS using dose rate detector data only;
  • FIG. 7 illustrates an exemplary method detail flowchart of a preferred method embodiment of the present invention implementing an automated operation of advanced ISOCS using dose rate detector data and spectrometer data;
  • FIG. 8 illustrates an exemplary method overview flowchart of a preferred embodiment of the present invention implementing an exemplary radiation analysis method
  • FIG. 9 illustrates an exemplary method detail flowchart of a preferred embodiment of the present invention implementing an exemplary radiation analysis method
  • FIG. 10 illustrates an exemplary GUI dialog used to execute the automated optimization and analysis routines described herein;
  • FIG. 11 illustrates an exemplary method flowchart of a preferred embodiment of the present invention implementing an exemplary automated best random fit efficiency optimization method
  • FIG. 12 illustrates an exemplary method flowchart of a preferred embodiment of the present invention implementing an exemplary automated best random fit efficiency optimization method
  • FIG. 13 illustrates an exemplary method flowchart of a preferred embodiment of the present invention implementing an exemplary automated smart routine efficiency optimization method
  • FIG. 14 illustrates an exemplary method flowchart of a preferred embodiment of the present invention implementing an exemplary automated smart routine efficiency optimization method
  • FIG. 15 illustrates an exemplary method flowchart of a preferred embodiment of the present invention implementing an exemplary automated smart routine efficiency optimization method
  • FIG. 16 illustrates an exemplary method flowchart of a preferred embodiment of the present invention summarizing the invention methodology
  • FIG. 17 illustrates an exemplary GUI used to define initial Geometry Information Setup (*.GIS) files describing the physical geometry of the radiation source ;
  • FIG. 18 illustrates an exemplary GUI used to input the initial geometry parameters and create a *.GIS data file
  • FIG. 19 illustrates an exemplary ISOCS geometry data file exported by a typical geometry composer utility
  • FIG. 20 illustrates an exemplary GUI dialog to create a new IUE project file or load an existing IUE project file
  • FIG. 21 illustrates an exemplary GUI dialog used to create new materials entry
  • FIG. 22 illustrates an exemplary GUI dialog used to define variable parameter values
  • FIG. 23 illustrates an exemplary GUI dialog used to select benchmarks to be used in efficiency optimization
  • FIG. 24 illustrates an exemplary GUI dialog used to select and setup an optimization routine
  • FIG. 25 illustrates an application example of the present invention using a measurement setup from uranium carbide items and depicts a typical counting geometry for Uranium Carbide items;
  • FIG. 26 illustrates known conditions for a Uranium 1 test item used in an application example of the present invention
  • FIG. 27 illustrates known conditions for a Uranium 2 test item used in an application example of the present invention
  • FIG. 28 illustrates known conditions for a Uranium 3 test item used in an application example of the present invention
  • FIG. 29 illustrates optimization results for Uranium 1/2/3 test item sources used in an application example of the present invention
  • FIG. 30 illustrates a plot of the convergence of the FOM using the Simplex Routine in an application example of the present invention for a Uranium 1 test sample
  • FIG. 31 illustrates a plot of the convergence of the FOM using the Simplex Routine in an application example of the present invention for a Uranium 2 test sample.
  • FIG. 32 illustrates a plot of the convergence of the FOM using the Simplex Routine in an application example of the present invention for a Uranium 3 test sample. DESCRIPTION OF THE PRESENTLY PREFERRED EXEMPLARY
  • radiation detector means any spectroscopic radiation detector used or capable of use when conducting radiation analysis of radionuclides. This includes, but is not limited to, organic and inorganic scintillation detectors (Nal, LaBr, Sri, Csl, BGO, YSO, LSO, and the like) , semiconductor detectors (HPGe, CZT, Pbl, PIN diodes, PIPS, surface barrier, and the like) as well as gaseous detectors (proportional counters) suitable for spectroscopy.
  • organic and inorganic scintillation detectors Nal, LaBr, Sri, Csl, BGO, YSO, LSO, and the like
  • semiconductor detectors HPGe, CZT, Pbl, PIN diodes, PIPS, surface barrier, and the like
  • gaseous detectors proportional counters
  • Some embodiments of the present invention may make use of non- spectroscopic detectors such as Geiger- Miiller (GM) tubes and the like to make dose rate measurements, and thus these types of radiation detectors are encompassed by the scope of the present invention.
  • radiation detector may also refer to combinations of the aforementioned detectors including hybrid systems of different component detector types.
  • the present invention may be applied to a wide variety of contexts and used in conjunction with the measurement of a wide variety of radiation types associated with a wide variety of radiation samples (RSAM) .
  • the RSAM group selections described below are only exemplary of those anticipated by the present invention.
  • the RSAM in some preferred embodiments may be selected from a group consisting of Special Nuclear Materials (SNM) including radionuclides that belong to the Actinide series. Prominent among these are Uranium and Plutonium isotopes such as 235 U, 238 U, 239 Pu, 238 Pu and the like. The inventory of SNM is closely monitored by agencies such as the IAEA that ensure that countries around the world comply with nuclear safeguards.
  • the RSAM may also be selected from a group consisting of other radioactive nuclides that are not SNM. Prominent among them are the fission products and activation products such as 60 Co, 152 Eu, and the like. These nuclides are typically generated in nuclear reactor operations, and end up in radioactive waste generated by nuclear reactor operations .
  • WNP Well-Known
  • NWP Not-Well-Known
  • NWP Well-Known parameters
  • NWP Not-Well -Known parameters
  • source / "sample” are considered synonyms for a radiation source and the terms “detector” / "sensor” are considered synonyms for a radiation detection device.
  • ISOCS In Situ Object Counting System
  • GIS Geometry Information Setup
  • GIS Geometry Information Setup
  • the previously detailed methods may all utilize the mathematical model to compute the calibration factor for each of several energies, in order to evaluate the calibration factor and the uncertainty in the calibration factor versus energy response of the measurement apparatus .
  • efficiency calibration is commonly carried out by measuring a representative source standard or using a suitable mathematical method.
  • certain simplifying assumptions are made with regards to the homogeneity of the sample matrix, the uniformity of radioactivity distribution, dimensions of sample, etc.
  • the source dimensions, the matrix material and density, and the radioactivity distribution are not- well -known. Under such circumstances, the efficiencies may not be very accurate since the calibration source geometry may not be very representative of the measured sample geometry.
  • the present invention teaches an automated system/method to optimize full energy peak efficiencies.
  • the NOT-WELL-KNOWN parameters are varied in an automated fashion and the optimal efficiency shape and magnitude are determined based on available benchmarks in the measured spectra.
  • the benchmarks could be results from isotopic codes such as Multi -Group Analysis Code (MGA) , Multi -Group Analysis Code for Uranium (MGAU) , or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) , activities from multi-line nuclides, and multiple counts of the same item taken in different geometries (from the side, bottom, top, etc.) .
  • MGA Multi -Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • the efficiency optimization is carried out using either a random search based on standard probability distributions, or using numerical techniques that carry out a more directed (smart) search.
  • the optimized efficiencies, along with the associated uncertainties, are used in the analysis of a radiation (gamma ray, etc.) spectrum from the given sample, and the activities or masses of radionuclides.
  • FIG. 1 A typical application context for the present invention is generally illustrated in FIG. 1 (0100) , wherein the present invention is utilized in a radiation measurement context comprising a radiation source/sensor/environment (RSSE) (0110) further comprising a radiation source (0111) , radiation detector (0112) and associated environmental conditions .
  • This RSSE (0110) interfaces to a computer system (0121) typically controlled by computer software read from a computer readable medium (0122) .
  • This computer software (0122) typically incorporates a graphical user interface (GUI) (0123) that permits an operator (0124) to monitor and/or control the RSSE (0110) computer controlled interface.
  • GUI graphical user interface
  • automated calibration software (0125) operates to enable measurements from the RSSE (0110) to be calibrated to known standards and produce measured radiation values and measurement uncertainty values (0129) .
  • the focus of the present invention is the automated calibration software (0125) that permits both a calibrated measurement from the RSSE (0110) to be obtained, but also an estimate of the uncertainty of this calibrated measurement.
  • FIG. 2 a typical example is illustrated in FIG. 2 (0200) wherein a container with unknown distribution of radioactive material (0201) is to be characterized in terms of absolute efficiencies, nuclide identification, and radionuclide mass determination.
  • the system utilizes a radiation source detection subsystem (RSDS) (0210) typically comprising a radiation sensor, high voltage supply, collimator / shield, preamplifier, amplifier, and A/D converter to collect digital radiation detection values (DRDV) that are then processed by a batch definition processor (BDFP) (0220) running automated analysis software.
  • RSDS radiation source detection subsystem
  • DRDV digital radiation detection values
  • BDFP batch definition processor
  • the analysis system utilizes a pre-analysis processor
  • EOPT efficiency optimization processor
  • WNP WELL-KNOWN- PARAMETERS
  • NWP NOT-WELL- KNOWN- PARAMETERS
  • FIG. 3 A typical implementation for the present invention is generally illustrated in FIG. 3 (0300), wherein the present invention is utilized in a radiation measurement context comprising a radiation source/sensor/environment (RSSE) (0310) further comprising a radiation source (0311) , radiation detector (0312) and associated environmental conditions.
  • This RSSE (0310) interfaces to a computer system (0321) typically controlled by computer software read from a computer readable medium (0322) .
  • This computer software (0322) typically incorporates a graphical user interface (GUI) (0323) that permits an operator (0324) to monitor and/or control the RSSE (0310) computer controlled interface.
  • GUI graphical user interface
  • contextual models (0313) describing the RSSE (0310) have associated with them model parameters (0314) that may vary based on source/sensor/environment (RSSE) (0310) configurations.
  • the automated optimization software (0325) makes a selection of model parameters (0314) for the radiation contextual model (0313) to define a model set describing the RSSE (0310) .
  • This model set is then used to calculate a set of calibration factors for the model and their associated calibration uncertainty values (0326) .
  • the radiation contextual model (0313) is integrated
  • the automated optimization software operates in conjunction with the RSSE (0310) and the computer system (0321) to enable measurements from the RSSE (0310) to be customized to the given sample and produce the measured radiation values and measurement uncertainty values
  • the focus of the present invention is the automated optimization software (0325) that permits contextual models (0313) describing the RSSE (0110) to be directly applied to measurements obtained from the RSSE (0310) to obtain a measurement value and its associated uncertainty.
  • the present invention generally augments the overview structure detailed in FIG. 3 (0300) with additional features as is generally depicted in FIG. 4 (0400) , wherein the model parameters (0414) incorporate WELL-KNOWN (fixed) parameters (0415) and NOT-WELL-KNOWN (variable) parameters (0416) .
  • the NOT-WELL-KNOWN (variable) parameters (0416) are modified by the automated optimization software (0425) in a random statistical fashion and used to generate calibration factors and associated calibration factor uncertainties (0426) that are stored in a database (0429) for later integration with the radiation contextual model (0413) and actual radiation measurement data.
  • the present invention generally may utilize a variety of methods to determine the value of NOT-WELL-KNOWN calibration parameters as generally detailed in FIG. 5 (0500) .
  • the overall processing of NOT-WELL-KNOWN parameters (0516) as a function of overall model parameters (0514) by the automated calibration software (0525) can be handled via the use of a "smart" search engine (0532) or a random statistical search engine (0531) .
  • Either of these engines can determine calibration factors and their uncertainty factors (0526) which when used in the context of a radiation measurement (0533) produce measured and calibrated radiation values and associated uncertainty values (0528) .
  • the automated optimization process described in this patent application may be used with dose rate measurement data as benchmarks.
  • dose rate data When dose rate data is used, the flow of the optimization process is identical to the process flow when spectroscopy detector data is used.
  • the optimization may be performed using dose rate data only, or the dose rate data may be used in combination with the spectroscopy data. Details of two exemplary implementation methodologies for these processes are described below.
  • the present invention system described above may be utilized in conjunction with a method providing for optimization using dose rate measurement data only as generally described in the flowchart illustrated in FIG. 6 (0600) .
  • the steps in this general radiation analysis method generally comprise:
  • the optimum geometry model is the one that gives the best FOM. For example, if the product sum of the ratios of calculated to measured dose rates is chosen as the FOM, the "best" value of the FOM will be the one closest to unity. Report optimized dose rates. ( 0609 )
  • the present invention system described above may be utilized in conjunction with a method providing for optimization using dose rate measurement data and spectroscopy measurement data as generally described in the flowchart illustrated in FIG. 7 ( 07 00 ) .
  • the steps in this general radiation analysis method generally comprise:
  • An appropriate dose rate FOM can be defined. For example, the product sum of the ratios of calculated to measured dose rates at the various dose rate detector locations.
  • the optimum geometry model is the one that gives consistent results with the dose rate detector and spectroscopy detector measurements. Report optimized dose rates.
  • ( 07 09 ) (10) Once the optimization of the geometry model of the radiation source is accomplished, reliable measurement values can be calculated at any location with respect to the source. This can be a real advantage since it will help in minimizing the number of measurements to be done by human beings, thus reducing the radiation exposure to personnel, as well as the cost of the measurement campaign. (0710)
  • This general method may be modified heavily depending on a number of factors, with rearrangement and/or addition/deletion of steps anticipated by the scope of the present invention. Integration of this and other preferred exemplary embodiment methods in conjunction with a variety of preferred exemplary embodiment systems described herein is anticipated by the overall scope of the present invention .
  • the present invention system described above may be utilized in conjunction with a method as generally described in the flowchart illustrated in FIG. 8 (0800) .
  • the steps in this general radiation analysis method generally comprise:
  • setup files Before launching the automated efficiency optimization process, the user will typically configure one or more setup files to be used by the analysis process.
  • setup files may take many forms and include a wide variety of information.
  • a preferred exemplary embodiment of the present invention may make use of the following exemplary set of files and their contents as follows:
  • the IUE project file consisting of information on variable parameters in the model, their ranges of variation, the benchmark FOMs to be used in the optimization process, the type of optimization routine (Best Random Fit or one of the Smart routines) along with its setup parameters;
  • the Genie2K Pre-Analysis Sequence File definition This includes the steps of Peak Locate, Peak Area, Applying Initial Efficiency (using initial model) , Initial Nuclide Identification, MGA/MGAU isotopic analysis (for U/Pu items) ; and
  • the user will setup the gamma ray detector and the associated signal processing electronics (RSDS) .
  • the signal processor is connected to the system PC. Installed on the system PC is the software that will acquire the gamma spectra and will run the automated analysis. The user will also collimate and shield the detector as needed by the particular measurement scenario. (0901)
  • FIG. 2 A block diagram of a typical system used to implement this method is generally illustrated in FIG. 2 (0200) with a corresponding overview method flowchart generally depicted in FIG. 8 (0800) .
  • FIG. 11 Flowcharts illustrated in FIG. 11 (1100) - FIG. 15 (1500) generally illustrate these method steps in greater detail.
  • FIG. 12 generally illustrate the automated algorithm flow when the Best Random Fit Routine is selected.
  • the flowcharts illustrated in FIG. 13 (1300) - FIG. 15 are the flowcharts illustrated in FIG. 13 (1300) - FIG. 15
  • the present invention may be generalized in the summary flowchart of FIG. 16 (1600) , wherein the radiation analysis method is generally accomplished by the following steps: (1) Locate a set of radiation detectors at various pre- selected measurement geometry locations with respect to a radioactive item. Record the radiation data at the pre-selected locations. Transfer the data to a computer. (1601) (2) Launch the A-ISOCS automated optimization process from the computer. (1602)
  • the optimum geometry model is the one that gives consistent results with all measurements. Report optimized radiation measurement values. ( 1609 )
  • This general method may be modified heavily depending on a number of factors, with rearrangement and/or addition/deletion of steps anticipated by the scope of the present invention. Integration of this and other preferred exemplary embodiment methods in conjunction with a variety of preferred exemplary embodiment systems described herein is anticipated by the overall scope of the present invention .
  • the present invention described herein performs the same function as constructing a very large number of radioactive calibration sources for physical analysis, and manually optimize by trial and error. Potentially hundreds or thousands of such source measurements may be needed to optimize which is clearly impractical if done on a manual basis.
  • the present invention achieves the same result but by using mathematical modeling and numerical calculations.
  • the present invention is optimally implemented as software running within the context of a computer system wherein the software is read from a computer readable medium.
  • This computer software program is either part of another software program that computes the calibration factor, such as the one covered under U.S. Patent 6,228,664, or incorporates the portions of such program, or uses software techniques to call and utilize the required elements of this or another calibration factor software program.
  • GUIs graphical user interfaces
  • A- ISOCS Advanced ISOCS Efficiency Optimization
  • the results of this analysis including peak areas, line activities, and nuclear data for the isotopes of interest are stored in the spectral file and can be accessed by A- ISOCS when performing optimization.
  • the purpose of this preliminary NID analysis is to make calculations more efficient and not go through a full Genie2K analysis for each ISOCS model as the A-ISOCS searches for the optimum.
  • the user may also use an isotopic code such as Multi -Group Analysis Code (MGA) , Multi -Group Analysis Code for Uranium (MGAU) , or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) to analyze the spectrum, if results from these codes are desired to be used as benchmarks for efficiency optimization.
  • MMA Multi -Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • An initial ISOCS geometry file (*.GIS) is needed for the initial efficiency calibration, and as a seed for the preliminary NID analysis.
  • the GIS file is created by entering the geometry input parameters in the ISOCS Graphical User Interface (GUI) called the Geometry Composer (GC) .
  • GUI Graphical User Interface
  • GC Geometry Composer
  • the user interface of the software allows the definition of the material compositions and a distribution range for all of the variable parameters of the sample geometry.
  • the GIS file is a text file and contains all relevant information needed by the ISOCS software to run the efficiency computation. Exemplary GUIs associated with the GC are depicted in FIG. 17 (1700) and FIG. 18 (1800) .
  • the ISOCS software creates the *.GIS file.
  • An exemplary GIS file is depicted in the Table illustrated in FIG. 19 (1900) .
  • the GIS file consists of keywords (preceded by the " ⁇ " symbol) that define each geometry parameter input by the user in GC GUI .
  • the input parameters include source dimensions, source-to-detector distance, environmental parameters to calculate air density, the detector response characterization to be used etc.
  • the input parameters defined in the GIS file are used by the ISOCS software to perform the efficiency computation for the given geometry.
  • the A-ISOCS software creates a desired number of geometry models.
  • the efficiencies are calculated using the generated input models. Optimization can be performed either by using the "Best Random Fit” or “Smart” methods. In the first case, a large number of random geometries is created and evaluated against the selected benchmark (s) . Geometries that best satisfy the optimization criteria are then used to generate the optimized efficiency curve. In case of the Smart method geometries are not randomly generated, but rather iteratively defined each time using results from the previous optimization step, thus reducing the overall number of generated models and shortening the optimization time.
  • FIG. 20 (2000) depicts an exemplary GUI dialog that prompts the user to create a new IUE project file or open an existing file to make changes.
  • FIG. 21 The A-ISOCS GUIs for defining the material composition and variable parameters are depicted in FIG. 21 (2100) and FIG. 22 (2200) respectively.
  • the benchmark FOMs to be used in the optimization are defined using the setup screen generally depicted in FIG. 23 (2300) .
  • Optimization can be performed either by using the "Best Random Fit” or “Smart” methods.
  • the user indicates the optimization methodology and the associated setup information needed to be used in the automated process.
  • the GUI for selecting and setting up the optimization method is shown in FIG. 24 (2400) .
  • Multi -Group Analysis Code MAA
  • Multi -Group Analysis Code for Uranium MGAU
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • LACE Line Activity Consistency Evaluation
  • a Figure-of-Merit (FOM) corresponding to each of the above benchmark methods may be defined. These benchmark FOMs can be used either individually or in combination.
  • efficiencies are computed using the ISOCS models, and the FOM calculations are performed for the user selected benchmark method(s) .
  • the efficiency model (s) that yield the best FOM are determined and is deemed to be the optimum.
  • the user can select the top "X" number of efficiency candidates (that yielded the best FOMs) , in which case, the optimum will be determined by averaging the efficiencies from the top "X" candidate efficiencies.
  • Analyzing the measured gamma ray spectrum using an isotopic computer code such as Multi -Group Analysis Code (MGA) , Multi -Group Analysis Code for Uranium (MGAU) , or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) , yields measured isotopic ratios and relative efficiency data that is used in optimizing the shape of the ISOCS based efficiency curve.
  • MCA Multi -Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • Uranium Measurement MGAU. FRAM. User-Defined FOM
  • a benchmark for efficiency optimization is the Uranium enrichment result that is available from the analysis using one of the isotopic codes (Multi -Group Analysis Code for Uranium (MGAU) or Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) ) . Or if the user has independent knowledge of the Uranium enrichment, he/she can use that information as the benchmark for optimization.
  • the form of the Figure-of -Merit (FOM) is the same when MGAU or FRAM or User-defined enrichment benchmark is used and is given by Equation (1) .
  • the FOM with MGAU enrichment is given as an example.
  • the equations for FRAM and User-defined are obtained by simply replacing MGAU in the superscript and subscript with FRAM or User-Defined.
  • Equation (1) 7] is the Uranium enrichment and /yMGAU is the Uranium Enrichment from MGAU analysis of the gamma ray spectrum.
  • Equation (2) the quantity (A) is the weighted average of line activities from 235 U or 238 U determined using Genie2K peak areas and gamma ray yields of respective gamma lines, and the ISOCS efficiencies for each model.
  • the quantities n SA" are the specific activities of 235 U or 238 U.
  • the weighted average activity and its uncertainty are given by Equations (3) -(6) :
  • L and M are the number of 238 U and 235 U gamma lines used in the optimization.
  • a benchmark for optimization is the relative efficiency output by the isotopic code MGA.
  • the relative efficiencies are calculated for each ISOCS model, by taking the ratio of the computed efficiency at a given energy with respect to the efficiency at a fiducial energy.
  • the fiducial energy is 208 keV, which is an intense gamma- line that is always present in plutonium spectra.
  • the set of parameters that give the minimum FOM M Q A corresponds to the best model.
  • the same form of the Figure- of-Merit (FOM) is used with FRAM.
  • the total uranium mass used as input for ISOCS is compared to the total uranium mass obtained from the gamma spectrometry analysis software Genie2K after applying the modeled efficiency to the spectrum.
  • the modeled uranium mass should correspond to the measured uranium mass.
  • the A-ISOCS code retrieves the initial line activities with uncertainties and initial efficiencies (from the initial GIS model) stored as internal spectral parameters. Using these initial values, the weighted average activities 235 238
  • Equations (11) -(12) and used to determine the uranium mass. Then, for each batch model created by A-ISOCS during optimization, the line activities and corresponding uncertainties are calculated using the following formulae: j235Uinitial
  • Equation (10) to determine U Mass for each batch model. This mass is subsequently used to calculate the FOM using Equations (8) and (9) .
  • the best model has the minimum
  • the Figure-of-Merit (FOM) for plutonium samples is calculated using the formula below.
  • the weight 23 9 Pu and corresponding uncertainty obtained using MGA or FRAM are used in the following FOM.
  • the A-ISOCS code retrieves the initial line activities with uncertainties and initial efficiencies (from the initial GIS model) stored as internal spectral parameters. Using these initial values the weighted average activity > 239
  • Equation (25) A) for the initial model is calculated using Equation (25) and used to determine the uranium mass. Then, for each batch model created by A-ISOCS during optimization, the line activities and corresponding uncertainties are calculated using the following formulae:
  • ⁇ 3 ⁇ 4 efficiency for the i-th line of239Pu (calculated by A-ISOCS) (29)
  • Equation (25) The average activity (A ⁇ is then calculated using Equation (25) , and used in the Equation (24) to determine
  • the best model has the minimum FOM p u Mass ⁇
  • plutonium samples contain a large fraction of 239 Pu ( ⁇ 60 - 95 wt%) , but in some cases (e.g., heat sources) the 238 Pu content can be as high as 80 wt% or more. In this case the total plutonium mass will be best calculated using the isotopic results for 238 Pu.
  • the FOM for plutonium samples with high Pu content is calculated using the formula given below.
  • Pu Mass ISOCS ⁇ Density Matrix ⁇ Wt%ofPu ( 3 i ;
  • the A- ISOCS code retrieves the initial line activities with uncertainties and initial ef f iciencies ( from the initial GIS model ) stored as internal spectral parameters . Using these initial values the weighted average activity of
  • Equation ( 33 ) Equation ( 33 ) and then used to determine the uranium mass . Then, for each batch model created by A- ISOCS during optimization, the line activities and corresponding uncertainties are calculated using the following formulae : ⁇ h%Puinitial
  • Equation (33) The weighted average activity of 238 Pu, is then calculated using Equation (33) , and used in the Equation
  • the radioactive sample may contain several nuclides, each of which may emit gamma lines with multiple energies. In this case, the optimization will be performed based on all such nuclide and gamma line data. Examples of nuclides emitting gamma lines at multiple energies are: 235 U, 238 U, 239 Pu, 238 Pu, 60 Co, 152 Eu, etc.
  • A-ISOCS code retrieves the initial line activities with uncertainties and initial efficiencies (from the initial GIS model) stored as internal spectral parameters. Using these NCL
  • the weighted average activity (A for the initial model is calculated using Equation (40) for each nuclide specified to be used during optimization. Then, for each model created by A-ISOCS during optimization, the line activities and corresponding uncertainties of each nuclide are calculated using the following formulae:
  • NCLimtial ⁇ ⁇ [nQ activity of me i_ m line 0 f a specific nuclide (NCL) obtained
  • NCL specific nuclide
  • NCL nuclide
  • Equation (40) and used along with line activities A ⁇ in the Equation (39) to determine FOM ⁇ CE for eacn model.
  • the best model has the minimum FOM ⁇ E ⁇ Composite FOM for Best Random Fit Routine
  • a Composite FOM is calculated based on the FOMs for the individual benchmark methods. It has been found that the absolute values of the FOMs obtained using different benchmark methods could differ by several orders of magnitude. Moreover, the FOMs behavior is different for different methods, i.e., the ratio of the FOMs calculated for the best and the worst models using one method could be several orders of magnitude large compared to the same FOM ratio, but calculated using a different method. As a result, development of a single FOM, combining several individual optimization methods, is rather a difficult task. Therefore for the Best Random Fit optimization routine, a ranking approach, independent of the absolute FOM values, was chosen to combine FOMs obtained using individual benchmark methods .
  • a rank is assigned from 1 to n for all models based on their performance, where n is the total number of models (e.g., 50) . That is, the best model in each approach is assigned #1, the second best - #2, and so on. For ties, the same median integer value is assigned.
  • the FOM for multiple counts is determined as follows.
  • the models are generated for each of the different detector configurations.
  • For the multi-count option it is assumed that the same sample is being measured from different perspectives.
  • the multi-count optimization is implemented by requiring that the output Nuclide Activity of all counts of the item should be as close as possible to each other (i.e., the relative standard deviation of the Nuclide Activity of the different counts is as small as possible) .
  • the relative standard deviation of the Nuclide Activity obtained with each count for each specified nuclide is calculated. If there are more than one nuclide specified, then a ranking scheme described below is used to combine the results.
  • the weighted average activity for a specific nuclide for each individual count is calculated using the following formula :
  • A-ISOCS code retrieves the initial line activities with uncertainties and initial efficiencies (from the initial GIS model) stored as internal spectral parameters. Using these initial values the weighted average activity for the initial model is calculated using Equation (47) for each nuclide specified to be used during optimization. Then, for each model created by A-ISOCS during optimization for each count, the line activities and corresponding uncertainties of each nuclide are calculated using the following formulae:
  • NCL specific nuclide
  • NCL specific nuclide
  • NCL specific nuclide
  • NCL nuclide
  • NCL specific nuclide
  • All models are ranked from 1 (best) to n (worst) based on the SD j l cL for each nuclide, with the best rank assigned to the model having the smallest standard deviation.
  • the multi-count FOM for each model index i ( ) is then calculated by adding up the individual ranks for each nuclide .
  • the Multiple Count FOM takes the form of the following equation:
  • the following method is used to include the Multi Count FOM in case of the Best Random Fit optimization approach.
  • All models are ranked again based on the .
  • the model that corresponds to the least rank is the best model.
  • A-ISOCS A-ISOCS
  • Several numerical routines are presently anticipated by the present invention for implementation of the efficiency optimization. These include: ⁇ Sequential Optimization;
  • the Sequential Optimization Method treats each free parameter separately, in an iterative sequence of one dimensional grid samples.
  • One free parameter is sampled evenly across the current bounds while the other free parameters are held constant.
  • the best value of the varied parameter is set and then the next free parameter is treated. If the best line sample is not at an edge, parabolic interpolation is used to improve the estimation. This continues until all of the parameters have been optimized. Then the sequence is repeated with bounds that have been reduced and re-centered on the new best values. This reduction in range forcibly sets the rate of convergence, which must be balanced against the risk of falling into a local minimum because of an inadequate search.
  • the Sequential method maintains all sampled points inside the parameter bounds by always maintaining the iteration limits inside the parameter bounds.
  • the Downhill Simplex (Nelder-Mead) Method involves continuously improving the FOMs of models represented by points in the solution space at the vertices of a multidimensional form, or simplex.
  • An initial simplex is established with one vertex more than the number of free parameters, and all of these point models are evaluated.
  • the points are sequentially improved by simultaneously adjusting all of the free parameters in the point with the worst FOM. After the worst point is improved and is no longer the worst point, the new worst point is improved. Improvements are performed by reflecting, expanding or contracting the worst point through the centroid of the other points. If none of these three trials improves the worst point to be better than the second worst point, all of the points are contracted halfway towards the point with the current best FOM.
  • the Simplex method maintains all sampled points inside the parameter bounds by truncating any parameter values attempting to extend beyond the bounds.
  • the vertices are initialized with one point at the center of each parameter range and the other points randomly located.
  • the Particle Swarm Optimization Method involves a group of models or particles simultaneously sampling all of the free parameters within bounds in an iterative manner. Each model remembers the point with the best personal FOM that it has experienced, and all of the models are aware of the global best point any particle has ever experienced. These memories determine where each point should next sample. Each point has a velocity that includes some inertia from the last step as well as stochastically sampled vectors from the current position to the best personal and best global positions.
  • the Swarm method maintains all sampled points inside the parameter bounds by truncating any parameter values attempting to extend beyond the bounds, with a small stochastic offset.
  • the number of particles is set to one more than the number of free dimensions.
  • the particles are initialized with one point at the center of each parameter range and the other points randomly located. Initial velocities are set randomly in proportion to the range of parameter bounds . Ouasi-Newton
  • the Quasi -Newton Method is an inverse Hessian approach that progressively determines the inverse curvature matrix using only gradient and location vectors without explicitly determining or inverting the curvature matrix.
  • the trial point starts at the center of each parameter range.
  • the gradient vector is determined via finite difference (forward difference) and the inverse curvature matrix is initialized to the identity matrix.
  • the step vector is determined by multiplying the current inverse curvature matrix by the negative gradient.
  • a scaling factor is applied to the step vector that is either one or a fraction if needed to keep inside all parameter bounds.
  • the FOM is determined at the full step and half step. If neither improves over the original point, the step size is reduced by orders of magnitude until a test point achieves an improved FOM.
  • the new step is taken. If the best FOM is a full step, the new step is taken. If the best FOM is a half-step, a trial is made at the parabolic interpolation and the best FOM becomes the new location. At the new location, the gradient is determined by finite difference. The old and new location gradients and parameter values are used to directly update the inverse curvature matrix using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula. The new step vector is calculated and the iterations progress, with the scaling factor reset to one or the current bounding fraction. Each gradient determination for N free parameters costs N + 1 calculations of the FOM. The scaling factor maintains all parameters within the specified limits.
  • BFGS Broyden-Fletcher-Goldfarb-Shanno
  • LMA Levenberg-Marquardt
  • DLS damped least-squares
  • the LMA interpolates between the Gauss-Newton algorithm (GNA) and the method of gradient descent.
  • GNA Gauss-Newton algorithm
  • the LMA is more robust than the GNA, which means that in many cases it finds a solution even if it starts very far off the final minimum. For well-behaved functions and reasonable starting parameters, the LMA tends to be a bit slower than the GNA. LMA can also be viewed as Gauss-Newton using a trust region approach .
  • the LMA is a very popular curve- fitting algorithm used in many software applications for solving generic curve- fitting problems. However, the LMA finds only a local minimum, not a global minimum.
  • the Downhill Simplex method was faster than the Particle Swarm method even though both showed comparable convergence/accuracy behaviors. • The Downhill Simplex method is best suited based on speed, accuracy, ability to handle discrete variables, and simplicity of usage.
  • the Best Random Fit routine When run on a computer with CPU speed of 2.8 GHz and a 3.5 GB RAM, the Best Random Fit routine may take several tens of hours to converge to a solution especially for highly attenuating source geometries. In many cases, the routine could reach the maximum number of models before reaching the desired level of convergence. By contrast, Smart routines such as Simplex converge to a solution within tens of minutes or an hour for the same geometries. There is a distinct advantage in using the smart routines because of the much shorter computation times. A disadvantage of using smart routines is the possibility of the solution falling into a local minimum during the optimization.
  • the combined U enrichment and U mass FOM is based on the quadrature sum of the U enrichment and U mass FOMs.
  • the uranium content in the reference uranium/carbon mix was not precisely known. Therefore the uranium WT% was one of the variables used during optimization.
  • the total matrix mass has been known for all measurements. Since it is a quite common case when the total material mass is known during the measurement (gross mass of the item minus the mass of an empty container, which is usually known in advance for the standard container types) , the total matrix mass was set to be constant during the optimization.
  • FIG. 26 ( 2 600 ) , FIG. 27 ( 27 00 ) , and FIG. 28 ( 2 800 ) provide details regarding the different uranium carbide items that were measured.
  • the gamma ray spectrum from each uranium carbide item was analyzed following the processes shown in the flow charts shown in Figures 8, 9, and 10.
  • the benchmarks (FOM) selected for the optimization were the Uranium enrichment from the MGAU isotopic code analysis, and the Uranium mass.
  • the FOM is given in Equations (1) and (8) (for Best Random Fit) and Equation (57) (for Downhill Simplex) .
  • the optimization was carried out in turn by using the Best Random Fit routine and the Downhill Simplex smart routine.
  • the relevant setup information for the downhill simplex method was as follows: • Convergence criterion set to 0.1%
  • Results shown in the table of FIG. 29 (2900) indicate that the Uranium masses determined using the A-ISOCS method are in good agreement with the declared values. If the user did not use the A-ISOCS optimization and guessed at the geometry parameters, the results could potentially be biased by a factor of two (2) or more. If no reliable declared values are available for the unknown items, then the user has no way of checking the accuracy of the results when using a best guess efficiency. The optimization routine eliminates the guessing game and provides the user with reliable convergence of the FOM results. The above data set also shows that the accuracy of the Simplex and Best Random Fit based optimizations are very comparable. The major difference is that the computation times using the Simplex are faster by an order of magnitude.
  • FIG. 30 depict plots of the convergence of the Figure-Of -Merit (FOM) convergence with the number of iterations of Simplex routine, for Uranium Carbide 1, 2, and 3 respectively.
  • FOM Figure-Of -Merit
  • the present invention system anticipates a wide variety of variations in the basic theme of construction, but can be generalized as a radiation analysis system comprising:
  • RSDS radiation source detection subsystem
  • the RSDS is configured with a radiation sensor (RSEN) to detect radiation emitted from a radiation sample (RSAM) and output digital radiation detection values (DRDV) associated with the emitted radiation;
  • RSEN radiation sensor
  • RARM radiation sample
  • DRDV digital radiation detection values
  • the BDFP is configured to accept user input from a graphical user interface (GUI) to define radiation analysis parameters (RAP) to be used in analyzing the RSAM;
  • GUI graphical user interface
  • RAP radiation analysis parameters
  • the PREP is configured to read the DRDV and perform a preliminary analysis to define a RSAM efficiency estimate (RSEE) ;
  • the EOPT is configured to analyze the DRDV and perform an automated efficiency value optimization (AEVO) to generate absolute efficiency values (AEV) for the RSAM using the RAP and the RSEE as a starting point for the analysis;
  • AEVO automated efficiency value optimization
  • AEV absolute efficiency values
  • the EOPT is configured to rank the AEVO with a Figure- Of-Merit (FOM) based on the correlation of the DRDV to model functions comprising WELL-KNOWN- PARAMETERS (WNP) and NOT-WELL-KNOW -PARAMETERS (NWP) ; and
  • FOM Figure- Of-Merit
  • the POST is configured to generate reports of the AEV to the GUI .
  • some system embodiments may incorporate optimization of dose rate and/or spectroscopy measurements.
  • This present invention alternate system anticipates a wide variety of variations in the basic theme of construction, but can be generalized as a radiation analysis system comprising:
  • RSDS radiation source detection subsystem
  • MOPT measurement optimization processor
  • POST post-analysis processor
  • the RSDS is configured with a radiation sensor (RSEN) to detect radiation emitted from a radiation sample (RSAM) and output digital radiation detection values (DRDV) associated with the emitted radiation;
  • RSEN radiation sensor
  • RARM radiation sample
  • DRDV digital radiation detection values
  • the BDFP is configured to accept user input from a graphical user interface (GUI) to define radiation analysis parameters (RAP) to be used in analyzing the RSAM;
  • GUI graphical user interface
  • RAP radiation analysis parameters
  • the PREP is configured to read the DRDV and perform a preliminary analysis to define a RSAM measurement estimate (RSME) ;
  • the MOPT is configured to analyze the DRDV and perform an automated measurement value optimization (AMVO) to generate absolute measurement values (AMV) for the RSAM using the RAP and the RSME as a starting point for the analysis;
  • AMVO automated measurement value optimization
  • AMV absolute measurement values
  • the MOPT is configured to rank the AMVO with a Figure- Of -Merit (FOM) ;
  • the POST is configured to generate reports of the AEV to the GUI .
  • the present invention method anticipates a wide variety of variations in the basic theme of implementation, but can be generalized as a radiation analysis method, the method operating in conjunction with a radiation analysis system comprising :
  • RSDS radiation source detection subsystem
  • BDFP batch definition processor
  • the RSDS is configured with a radiation sensor (RSEN) to detect radiation emitted from a radiation sample (RSAM) and output digital radiation detection values (DRDV) associated with the emitted radiation;
  • the BDFP accepts user input from a graphical user interface (GUI) to configure radiation analysis parameters (RAP) to be used in analyzing the RSAM;
  • the PREP reads the DRDV and performs a preliminary analysis to define a RSAM efficiency estimate (RSEE) ;
  • the EOPT ranks the AEVO with a Figure-Of-Merit (FOM) based on the correlation of the DRDV to model functions comprising WELL-KNOWN-PARAMETERS (WNP) and NOT-WELL-KNOWN-PARAMETERS (WNP) and NOT-W
  • step (12) optionally recalculating step (11) for each of several energies to evaluate the SDME, then proceeding to step (5) .
  • some embodiments may incorporate an overall evaluation of energy response within the RSEE via reevaluation of the test mathematical model using a number of energy values as indicated in step (12) .
  • This general method may be modified heavily depending on a number of factors, with rearrangement and/or addition/deletion of steps anticipated by the scope of the present invention. Integration of this and other preferred exemplary embodiment methods in conjunction with a variety of preferred exemplary embodiment systems described herein is anticipated by the overall scope of the present invention .
  • some embodiments may incorporate dose rate and/or spectroscopy optimization procedures.
  • the present invention method anticipates a wide variety of variations in the basic theme of implementation, but alternatively can be generalized as a radiation analysis method comprising:
  • step (12) determining with the computer system if the same geometry model gives consistent results for all measurements at all measurement locations, and if so, proceeding to step (12) ;
  • This general method may be modified heavily depending on a number of factors, with rearrangement and/or addition/deletion of steps anticipated by the scope of the present invention. Integration of this and other preferred exemplary embodiment methods in conjunction with a variety of preferred exemplary embodiment systems described herein is anticipated by the overall scope of the present invention .
  • this generalized method integrates the teachings of FIG. 6 ( 0600 ) , FIG. 7 ( 07 00 ) , and FIG. 16 ( 1600 ) , in that the set of radiation detectors may comprise any combination of dose rate detectors and spectroscopy detectors.
  • the present invention anticipates a wide variety of variations in the basic theme of construction.
  • the examples presented previously do not represent the entire scope of possible usages. They are meant to cite a few of the almost limitless possibilities.
  • BDFP further comprises a configuration function selected from the group consisting of: energy calibration; peak shape calibration; spectral file physical sample/container definition; variable geometry parameter selection; benchmark FOM selection; optimization routine selection; and analysis setup parameter selection.
  • PREP further comprises an analysis function selected from the group consisting of: Peak Locate; Peak Area; Initial Geometry File Efficiency Correction; Nuclide Identification; Nuclide Line Activities; and Isotopic Analysis.
  • An embodiment wherein the EOPT further comprises execution of an efficiency optimization algorithm selected from the group consisting of: Best Random Fit and Smart Routine.
  • An embodiment wherein the EOPT further comprises execution of an optimization benchmark algorithm selected from the group consisting of: Multi-Group Analysis Code (MGA) ; Multi -Group Analysis Code for Uranium (MGAU) ; Fixed energy, Response function Analysis with Multiple efficiencies Code (FRAM) ; Line Activity Consistency Evaluation (LACE) ; User Defined Isotopics; U Mass; Pu Mass; and Multiple Count.
  • MAA Multi-Group Analysis Code
  • MGAU Multi -Group Analysis Code for Uranium
  • FRAM Fixed energy, Response function Analysis with Multiple efficiencies Code
  • LACE Line Activity Consistency Evaluation
  • An embodiment wherein the EOPT further comprises execution of an efficiency optimization Smart Routine algorithm selected from the group consisting of: Sequential Optimization; Downhill Simplex; Particle Swarm; Quasi-Newton; and Marquardt .
  • POST further comprises execution of a process selected from the group consisting of: efficiency correction with optimized efficiencies; nuclide identification; activity quantification; and measurement uncertainty estimation.
  • RSAM comprises a Special Nuclear Material (SNM) selected from the group consisting of: 235 U, 238 U, 239 Pu, and 238 Pu.
  • SNM Special Nuclear Material
  • RSAM comprises a radioactive nuclide other than SNM.
  • GUI further comprises a geometry composition editor configured to graphically define the Geometry Information Setup (GIS) measurements used to measure the DRDV associated with the RS.
  • GIS Geometry Information Setup
  • the set of radiation detectors comprises one or more dose rate detectors.
  • the set of radiation detectors comprises one or more spectroscopy detectors.
  • the set of radiation detectors comprises one or more dose rate detectors and one or more spectroscopy detectors.
  • the present invention may be implemented as a computer program product for use with a computerized computing system.
  • programs defining the functions defined by the present invention can be written in any appropriate programming language and delivered to a computer in many forms, including but not limited to: (a) information permanently stored on non-writeable storage media (e.g., read-only memory devices such as ROMs or CD-ROM disks) ; (b) information alterably stored on writeable storage media (e.g., floppy disks and hard drives); and/or (c) information conveyed to a computer through communication media, such as a local area network, a telephone network, or a public network such as the Internet.
  • non-writeable storage media e.g., read-only memory devices such as ROMs or CD-ROM disks
  • writeable storage media e.g., floppy disks and hard drives
  • information conveyed to a computer through communication media such as a local area network, a telephone network, or a public network such as the Internet.
  • the present invention system embodiments can incorporate a variety of computer readable media that comprise computer usable medium having computer readable code means embodied therein.
  • One skilled in the art will recognize that the software associated with the various processes described herein can be embodied in a wide variety of computer accessible media from which the software is loaded and activated.
  • the software associated with the various processes described herein can be embodied in a wide variety of computer accessible media from which the software is loaded and activated.
  • the present invention anticipates and includes this type of computer readable media within the scope of the invention.
  • Pursuant to In re Nuijten, 500 F.3d 1346 (Fed. Cir. 2007) U.S. Patent Application S/N 09/211,928)
  • the present invention scope is limited to computer readable media wherein the media is both tangible and non-transitory. CONCLUSION
  • a radiation analysis system/method that automatically optimizes the efficiency calibration of a counting system based on benchmark data and variable parameters associated with radiation source/sensor/environment (RSSE) combinations is disclosed.
  • the system/method bifurcates RSSE context (SSEC) model parameters into WELL-KNOWN (fixed) parameters (WNP) and NOT-WELL-KNOWN (variable) parameters (NWP) .
  • the NWP have associated lower/upper limit values (LULV) and a shape distribution (LUSD) describing NWP characteristics.
  • SSEC models are evaluated using randomized statistical NWP variations or by using smart routines that perform a focused search within the LULV/LUSD to generate model calibration values (MCV) and calibration uncertainty values (UCV) describing the overall SSEC efficiencies.
  • Sensor measurements using the MCV/UCV generate a measurement value and uncertainty estimation value.
  • An exemplary embodiment optimizes geometry models of radiation sources by benchmarking with respect to measurement data from spectroscopy detectors and/or dose rate detectors.

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Abstract

La présente invention porte sur un système/procédé d'analyse de rayonnement qui optimise de manière automatique l'étalonnage de rendement d'un système de comptage sur la base de données de référence et de paramètres variables associés à des combinaisons de source/capteur/environnement de rayonnement (RSSE). Le système/procédé bifurque des paramètres de modèle de contexte RSSE (SSEC) en paramètres BIEN-CONNUS (fixes) (WNP) et paramètres NON-BIEN-CONNUS (variables) (NWP). Les NWP ont des valeurs de limite inférieure/supérieure associées (LULV) et une distribution de forme (LUSD) décrivant des caractéristiques NWP. Des modèles SSEC sont évalués à l'aide de variations NWP statistiques randomisées ou à l'aide de routines intelligentes qui réalisent une recherche focalisée dans les LULV/LUSD pour générer des valeurs d'étalonnage de modèle (MCV) et des valeurs d'incertitude d'étalonnage (UCV) décrivant les rendements SSEC globaux.
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CN110780338A (zh) * 2019-11-13 2020-02-11 中国原子能科学研究院 一种自动分析放射性样品中总γ的方法及系统
CN111239797A (zh) * 2020-02-10 2020-06-05 成都理工大学 一种基于辐射粒子事件的采集器及快速核素识别方法
FR3090128A1 (fr) * 2018-12-17 2020-06-19 Commissariat A L'energie Atomique Et Aux Energies Alternatives Procede de caracterisation d’un spectrometre, produit programme d’ordinateur et calculateur associes
CN112633790A (zh) * 2020-12-02 2021-04-09 中国辐射防护研究院 放射性物质运输火灾事故情景下事故响应边界的划定方法
CN115480284A (zh) * 2022-09-26 2022-12-16 重庆建安仪器有限责任公司 一种人体核素分析测量装置及方法
CN117473799A (zh) * 2023-12-28 2024-01-30 苏州泰瑞迅科技有限公司 一种基于超算平台的辐射探测器点源效率计算方法及系统

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WO2016131852A1 (fr) * 2015-02-19 2016-08-25 Commissariat à l'énergie atomique et aux énergies alternatives Procede de quantification des dimensions intrinseques des capteurs de rayonnements, notamment des capteurs de rayonnements ionisants, et dispositif pour la mise en oeuvre du procede
FR3033039A1 (fr) * 2015-02-19 2016-08-26 Commissariat Energie Atomique Procede de quantification des dimensions intrinseques des capteurs de rayonnements, notamment des capteurs de rayonnements ionisants, et dispositif pour la mise en œuvre du procede
US10466374B2 (en) 2015-02-19 2019-11-05 Commissariat A L'energie Atomique Et Aux Eneriges Alternatives Method for quantifying the intrinsic dimensions of radiation sensors, particularly ionizing radiation sensors, and device for implementing same
EP3385759A1 (fr) * 2017-04-07 2018-10-10 Comecer Netherlands B.V. Système de surveillance réelle de la libération de particules radioactives, installation de construction équipée de celui-ci et procédé associé
NL2018665B1 (en) * 2017-04-07 2018-10-17 Comecer Netherlands B V Monitoring system for real-type monitoring release of radioactive particles, building facility provided therewith, and method therefor
CN108470079A (zh) * 2017-10-26 2018-08-31 北京特种工程设计研究院 航天发射场涉核操作辐射安全评估仿真方法
CN108470079B (zh) * 2017-10-26 2023-04-07 北京特种工程设计研究院 航天发射场涉核操作辐射安全评估仿真方法
FR3090128A1 (fr) * 2018-12-17 2020-06-19 Commissariat A L'energie Atomique Et Aux Energies Alternatives Procede de caracterisation d’un spectrometre, produit programme d’ordinateur et calculateur associes
WO2020128284A1 (fr) * 2018-12-17 2020-06-25 Commissariat A L'energie Atomique Et Aux Energies Alternatives Procede de caracterisation d'un spectrometre, produit programme d'ordinateur et calculateur associes
CN110780338B (zh) * 2019-11-13 2021-04-02 中国原子能科学研究院 一种自动分析放射性样品中总γ的方法及系统
CN110780338A (zh) * 2019-11-13 2020-02-11 中国原子能科学研究院 一种自动分析放射性样品中总γ的方法及系统
CN111239797A (zh) * 2020-02-10 2020-06-05 成都理工大学 一种基于辐射粒子事件的采集器及快速核素识别方法
CN112633790A (zh) * 2020-12-02 2021-04-09 中国辐射防护研究院 放射性物质运输火灾事故情景下事故响应边界的划定方法
CN112633790B (zh) * 2020-12-02 2022-05-20 中国辐射防护研究院 放射性物质运输火灾事故情景下事故响应边界的划定方法
CN115480284A (zh) * 2022-09-26 2022-12-16 重庆建安仪器有限责任公司 一种人体核素分析测量装置及方法
CN117473799A (zh) * 2023-12-28 2024-01-30 苏州泰瑞迅科技有限公司 一种基于超算平台的辐射探测器点源效率计算方法及系统
CN117473799B (zh) * 2023-12-28 2024-04-09 苏州泰瑞迅科技有限公司 一种基于超算平台的辐射探测器点源效率计算方法及系统

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