WO2006081581A2

WO2006081581A2 - Atomistic model for particle inception

Info

Publication number: WO2006081581A2
Application number: PCT/US2006/003559
Authority: WO
Inventors: Gregory A. Voth; Angela Violi
Original assignee: University Of Utah Research Foundation
Priority date: 2005-01-28
Filing date: 2006-01-27
Publication date: 2006-08-03
Also published as: WO2006081581A3

Abstract

Systems for simulating the chemical and physical properties of soot precursors in a combustion environment are disclosed. The systems provide information on the physical and chemical structure of the nanoparticles formed in a combustion environment as part of the transition from gas-phase precursors to soot particles. The systems combine Kinetic Monte Carlo and Molecular Dynamics to model soot inception.

Description

ATOMISTIC MODEL FOR PARTICLE INCEPTION

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application No.

60/648,357, filed January 28, 2005.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR

DEVELOPMENT

[0002] This invention was made with Government support under subcontract number

B341493 awarded by the United States Department of Energy. The Government has certain rights in the invention.

TECHNICAL FIELD

[0003] The present invention relates generally to methods for modeling chemical processes. More specifically, the present invention relates to methods for modeling molecular transformations that occur in combustion conditions during the transition from gas-phase precursors (a few angstroms in size) to soot particles (hundreds of nanometers).

BACKGROUND OF THE INVENTION

[0004] Understanding the kinetics of soot formation in combustion environments is important in evaluating the deleterious effects that soot and soot precursors have on human health and the environment. From an environmental perspective, it has been demonstrated that particles produced in a combustion environment and released into the atmosphere may effect global warming and contribute to reduced visibility (haze) in the atmosphere. Soot and soot precursors can also damage sensitive forests, crops, lakes and streams, and the diversity of ecosystems. Soot may stain and damage stone and other materials, including culturally important objects such as monuments and statutes. From a human health perspective, soot and soot precursors are associated with increased respiratory disease, decreased lung functioning, and even premature death. Some of these particles also may be carcinogenic in character.

[0005] Currently there is an increased number of chemical kinetics studies on the formation and growth of Polycyclic Aromatic Hydrocarbons ("PAH") and soot nuclei. The essence of models of soot formation known to those having skill in the art is based on an increase in particle mass of sub-nanometer PAH seeds by chemical reactions with gaseous precursors simultaneously with the growth of particle size by collision among PAH molecular species and clusters resulting in particles that may be hundreds of nanometers in size.

[0006] According to one model of nucleation, this transition was assumed to take place as a result of chemical growth and inter-PAH bonding to form PAH dimers and PAH trimers eventually leading to PAH clusters evolving into solid particles. Soot was defined as accumulated particle mass above a certain size. According to another model the evolution of gaseous species is presumed to occur by the formation of high-molecular-mass compounds formed of two to three aromatic rings interconnected by aliphatic chains that graphitize afterward.

[0007] Various mathematical methods have been applied to reduce the size of these models needed to handle high-molecular weight species. The methods reduce the size of the mathematical formulation without distorting the physical nature of the detailed model. According to one method, the entire particle ensemble is divided into multiple sections and the particle properties are averaged into each section. This yields information about soot particle distribution but is limited by the number of sections that can be treated with acceptable computing times. According to an alternative method, the description of particle dynamics is reformulated in terms of moments of the particle size distribution function. This offers mathematical simplicity but does not yield chemical detail on the higher molecular weight species.

[0008] Consequently, it would be an advancement in the art to have a tool for examining the molecular transformations that occur in a combustion environment during the transition from gas-phase precursors to soot particles in a chemically specific way. It would further be an advancement in the art to have a tool that provides information on the physical as well as chemical structure of the nanoparticles formed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present embodiments will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only typical embodiments and are, therefore, not to be considered limiting of the invention's scope, the embodiments will be described with additional specificity and detail through use of the accompanying drawings in which:

[0010] Figure 1 is a block diagram illustrating a system for simulating the chemical and physical properties of soot precursors in a combustion environment;

[0011] Figure 2 is a flow diagram of one embodiment of a method for simulating the chemical and physical properties of soot precursors in a combustion environment;

[0012] Figure 3 is a flow diagram of one embodiment of a Kinetic Monte Carlo process used to simulate the chemical and physical properties of soot precursors in a combustion environment;

[0013] Figure 4 is a flow diagram of one embodiment of a Molecular Dynamics process used to simulate the chemical and physical properties of soot precursors in a combustion environment; and

[0014] Figure 5 is a block diagram illustrating the major hardware components typically utilized in a computing device used in conjunction with a system for simulating the chemical and physical properties of soot precursors in a combustion environment.

DETAILED DESCRIPTION OF THE INVENTION

[0015] It will be readily understood that the components of the embodiments as generally described and illustrated in the Figures herein could be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of the embodiments of the systems and methods of the present invention, as represented in the Figures, is not intended to limit the scope of the invention, but is merely representative of the embodiments of the invention.

[0016] The word "exemplary" is used exclusively herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0017] Several aspects of the embodiments described herein will be illustrated as software modules or components stored in a computing device. As used herein, a software module or component may include any type of computer instruction or computer executable code located within a memory device and/or transmitted as electronic signals over a system bus or network. A software module may, for instance, comprise one or more physical or logical blocks of computer instructions, which may be organized as a routine, program, object, component, data structure, etc., that performs one or more tasks or implements particular abstract data types.

[0018] In certain embodiments, a particular software module may comprise disparate instructions stored in different locations of a memory device, which together implement the described functionality of the module, indeed, a module may comprise a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across several memory devices. Some embodiments may be practiced in a distributed computing environment where tasks are performed by a remote processing device linked through a communications network. In a distributed computing environment, software modules may be located in local and/or remote memory storage devices.

[0019] Note that the exemplary embodiment is provided as an exemplar throughout this discussion; however, alternate embodiments may incorporate various aspects without departing from the scope of the present invention.

[0020] The order of the steps or actions of the methods described in connection with the embodiments disclosed herein may be changed by those skilled in the art without departing from the scope of the present invention. Thus, any order in the Figures or detailed description is for illustrative purposes only and is not meant to imply a required order. [0021] Figure 1 is a block diagram illustrating a system 110 for simulating the chemical and physical properties of soot precursors in a combustion environment. The present system 110 may utilize computer code, such as executable instructions executable by a processor within a computing device (not shown) to perform a method for modeling soot inception. The system 110, according to one embodiment, combines the strengths of Kinetic Monte Carlo ("KMC") for long-time sampling and Molecular Dynamics ("MD") for relaxation processes. This may be referred to as the KMC/MD process 112. The time duration between KMC events may be arbitrarily long depending upon the kinetics, model, etc. The time step for a single KMC iteration may be real time, determined by the kinetics system. Whereas in MD, time steps are a small fraction of a vibrational period. The combination of the KMC and MD methods allows a user of the methods of the invention to span a much wider range of time-scales, while retaining full atomistic detail. [0022] The inputs to the system 110 for modeling soot inception may include the chemical species 114 present in the gas phase that contribute to the growth process, and their corresponding number 118 and concentration 116. The choice of the particular input chemical species 114 may be due to their presence in high concentrations in the PAH inventory and to the importance that PAH with peripherally fused five-membered rings have in the flame formation chemistry of soot. The species concentration 116 may be calculated using chemical reaction modeling software, such as the CHEMKIN™ software suite, together with kinetic models known to those having skill in the art. The CHEMKIN™ software suite is distributed by Reaction Design, Inc., San Diego, California, a licensee of Sandia National Laboratories. The CHEMKIN™ software suite facilitates the formation, solution, and interpretation of problems involving gas-phase and heterogeneous (gas- surface) chemical kinetics.

[0023] The temperature profile 120 may also be included as well as the reaction rates 122 that describe the interactions between gas-phase species 114 and the growing seed and the reactions that can occur on the seed itself, such as cyclodehydrogenation reactions. The reaction rates 122 may be calculated using electronic structure methods, and are specified as probabilities.

[0024] MD is a direct approach among atomistic simulations. In MD, one may choose an appropriate interatomic potential to describe the forces between atoms and then integrate the classical equation of motion. However, one limitation of MD is the accessible simulation time, making it difficult to obtain useful predictions with MD alone. Resolving individual atomic vibrations requires a time step of approximately femtoseconds in the integration equations of motion. Consequently, reaching times of even microseconds may be difficult. Consequently, in the present system 110, MD methodology has been coupled to KMC to solve this timescale problem by allowing the extension of the accessible timescales by orders of magnitude relative to direct MD while retaining full atomistic detail. [0025] During the KMC portion of the KMC/MD process 112, a reaction is chosen randomly from a list of possible transition events that are weighted by the respective reaction rate constant. The seed molecule is then advanced to a new intermediate state. The clock may then be incremented in a way that is consistent with the average time for escape from that state, which can be determined from the rate constants for the possible escape paths available. The ability of the KMC portion of the KMC/MD process 112 to make nonphysical moves enhances its capacity to explore phase space in appropriate cases. Furthermore, its capability to generate states directly allows it to bypass configurations that may be difficult to escape dynamically.

[0026] The MD portion of the KMC/MD process 112 is used to equilibrate the intermediate structure produced after the KMC steps yielding a new state of the compound 124. MD may not be able to cross the barriers between the conformations sufficiently often to ensure that each conformation is sampled according to the correct statistical weight. However, MD may be useful for exploration of the local phase space, whereas the KMC portion may be more effective for conformational changes that jump to a completely different area of phase space.

[0027] The present system 110 provides the microscopic detail of molecule growth in combustion environments which are typically experimentally inaccessible. Consequently, the present system provides information on the physical as well as chemical properties of the carbonaceous nanoparticles formed, such as particle morphology and free radicals. The present system 110 further provides long-term potential for information on particle characteristics such as density, porosity, and other physical properties. [0028] Figure 2 is a flow diagram of one embodiment of a method 230 for simulating the chemical and physical properties of soot precursors in a combustion environment. The method 230 includes the entering of inputs 232 as described in conjunction with the description of Figure 1. According to the method 230, in a typical simulation an aromatic seed molecule has a first configuration 234 and is placed in a combustion environment defined by temperature and concentrations of the species present in the gas phase that can contribute to the growth process. During the KMC process 236 one reaction, from a list of all possible events, is executed at one site on the growing aromatic structure. The probability of choosing a reaction is equal to the rate at which the reaction occurs relative to the sum of the rates of all of the possible reactions. This results in a second configuration 238, which is an intermediate configuration of the growing seed molecule. [0029] The next step in the method 230 includes the MD process 240 in order to equilibrate the structure produced after the KMC process 236. This produces a third configuration 242 of the growing soot precursor, that is relatively similar to the second configuration 238 after having undergone energy minimization. On a general level of abstraction the computations performed for the KMC process 236 and the MD process 240 are known to those having skill in the art and are not described in great detail here. However, the applicability of these computations to combustion environments will be discussed in greater detail in conjunction with Figure 3 and 4.

[0030] Once the compound has undergone one iteration of both the KMC 236 and MD 240 processes the process starts again alternating between the KMC 236 and MD 240 components. Consequently, the third configuration 242 of the growing compound undergoes another iteration of the KMC process 236, where, as discussed above, one reaction, from a list of all possible events, is executed at one site on the growing aromatic structure. The probability of choosing a reaction is equal to the rate at which the reaction occurs relative to the sum of the rates of all of the possible reactions. This results in a fourth configuration 244 of the growing seed molecule and the method 230 continues. [0031] Figure 3 is a flow diagram of a Kinetic Monte Carlo ("KMC") process 350 that is used to simulate the chemical and physical properties of soot precursors in a combustion environment. According to this method, the possible reaction sites are identified 352 on the seed molecule. The definition of sites where reactions can occur may include among others, atoms on 5-, and 6-membered rings, dangling bonds on 5-, and 6-membered rings, sp³ hydrogen, chains prone to close to form 5-, and 6-membered rings, sites on generic aliphatic chains, and rings containing a number of carbons different from five or six. According to this step every atom with a local environment which fits the definition of each site is identified 352 and is checked to determine if there is enough room next to the site for a gas- phase species to penetrate and react.

[0032] The kinetic rate constants and reaction rates are also evaluated 354. Microscopic reversibility and fragmentation reactions are included in the methods for modeling soot inception. Fragmentation reactions are reactions where a radical breaks apart to form either a stable species and a new radical or two radicals.

[0033] By way of example, given the environment defined by the gas-phase species - number and concentration profiles - and temperature, two main reaction sequences were incorporated in this method to describe the growth of aromatic compounds. First, growth of aromatics beyond three-ring PAH may be modeled by a radical-molecule sequence of reactions involving five membered-ring compounds, e.g., acenaphthylene. The sequence is: [0034] R, -H + H = R/ + H₂ (Rl)

[0035] R_; + Ry - H + H = R,R/H + H₂ (R2) where R-H is an aromatic molecule with i periocondensed rings, and R, is its radical. [0036] An initiation step is represented by H abstraction reactions, which activate the aromatic structures. This may be accomplished in many different ways, but under typical combustion conditions it is the H abstraction by an H radical that dominates. Aromatic radicals (R₁) may then add to the conjugated double bond of five-membered PAH (R/-H) and they grow until termination reactions occur by coupling of two growing radical intermediates and/or H-atom addition to a radical.

[0037] The second sequence may be represented by the H Abstraction — C₂H₂ Addition ("HACA") mechanism, which involves abstraction of hydrogen from hydrocarbons, thereby activating the aromatic molecules (Rl), and addition of acetylene (C₂H₂) to the radical site formed, which propagates molecular growth and cyclization to form a ring to the parent PAH. The two steps can be expressed as:

[0038] R, + C₂H₂ = R₁C₂H₂ (R3)

[0039] R₂C₂H₂ + C₂H₂ → R_m + H (R4)

[0040] The rates for reactions (Rl) and (R2) are computed using an ab initio technique and the Reaction Class Transition State Theory. The Reaction Class Transition State Theory provides a methodology for estimating thermal rate constants of a reaction in a class from the principal reaction in the class, using the relative classical barrier and reaction energy.

[0041] Rates for the fragmentation reactions are evaluated by analogy with gas-phase reactions. The potential surfaces of these reactions may be explored using isodesmic BLYP/DZVP calculations and the B3LYP/cc-pVDZ method. Rate constants are obtained by means of transition state theory. These results are used for the transition event of the addition of acetylene to aromatic compounds.

[0042] Referring still to Figure 3, a transition event is selected 356, with its weight proportional to its kinetic rate. A list of all the possible events, i.e. reactions, is also provided to the code. During the KMC step one reaction 358 is executed at one site on the growing aromatic structure during each time step. The probability of choosing a reaction is equal to the rate at which the reaction occurs relative to the sum of the rates of all of the possible reactions. To choose one reaction, a list is constructed which contains a running sum of the rates of each of the possible events, and each entry in the list is normalized by the sum of the rates of all the possible events. At each time step, one event denoted by m is randomly chosen from all of the M events that can possibly occur at that step, as follows:

where 77 is the rate at which the event i occurs and ζι is a random number in the range (0,1). Once a reaction is chosen, the molecular system is altered appropriately 360 and the list of relative rates is updated to reflect the new configuration. Since one event occurs at each simulation step and different events occur at different rates, the time increment, dt, associated with each simulation step is dynamic and stochastic

where ζ₂ is a random number uniformly distributed in the range (0,1), and the denominator is the sum of the rates of all of the events that can occur at the simulation step for which dt has been evaluated. The use of variable time increment allows the consideration of reactions that occur on widely disparate time scales.

[0043] The timestep for a single KMC iteration may be determined in "real time" by the kinetic system. The reaction rates among the compounds present in the system are specified as probabilities and the surface configuration over time is then given by a master equation, describing the time evolution of the probability distribution of system configurations. [0044] Figure 4 is a flow diagram of one embodiment of a Molecular Dynamics ("MD") process 470 used to simulate the chemical and physical properties of soot precursors in a combustion environment. Unlike the KMC process described which deals with intermolecular interactions, the MD process 470 deals with intramolecular dynamics. The MD process 470 is used to equilibrate the intermediate configuration of the seed compound that is determined 472 after the KMC process.

[0045] The interatomic potential used for these calculations may be the adaptive intermolecular reactive empirical bond order potential ("AIREBO"). The bond energy takes into account the local environment via a many-body term that depends not only on bond length and angle but also on the coordination of the atoms making the bond and of their nearest neighbor. Other interatomic potentials may be used instead, depending on the system of interest.

[0046] Referring still to Figure 4, the individual atomic velocities in the system are periodically sampled 474 from the Boltzmann distribution at the flame temperature so that the entire MD system may be described within a weak collision model. The temperature resampling allows a user to equilibrate the system to a specified temperature, i.e., the flame temperature. The atoms are randomly picked throughout the molecule at a user-defined collision rate.

[0047] The intermediate compound is then relaxed 476 to a new state, whereupon the KMC/MD process may begin again on the new compound. KMC/MD allows for sampling long timescales, where the time duration between KMC events depends upon the kinetics, model, etc. In MD the time steps are a small fraction of the atomic vibrational period. After the application of the MD process 470, new potential reactions and reaction sites are identified, and the KMC module is applied for a given interval to calculate the new structures formed.

[0048] One characteristic of soot precursors is their morphology. The morphology of the soot precursors as determined through the algorithms described above, show the presence of elongated shapes for young nanoparticles. Ellipticity parameters have been used to characterize particle shape. The aspect ratio ("AR") of a compound is computed: AR = alb, where a and b are the axes of the ellipse that has as its center in the object's centroid. [0049] A series of diameters of the particles may be sampled. The average of these diameters is then used as the spherical diameter r_s of the configurations, and an average of the diameters longer than r_s may be used as α, and an average of the shortest ones may be used as b. Alternatively, an evaluation of a and b can be obtained by using the Legendre ellipse with its center in the particles centroid and having the same geometrical moments up to the second order as the original object area. Often the Legendre ellipse is used instead of the original object.

[0050] The AR of a circle is 1 and of an ellipse with the ratio of axes 2:1 is equal to 2. The AR for the ellipse increases with an increase in the ratio of the major and secondary axes. The AR tends to increase with increasing particle size. [0051] This approach also provides information on the unpaired electrons (free radicals) in the soot precursors. These free radicals are of concern because they can persist for a long time, are reactive, and are a health concern if they escape into the atmosphere. Another reason for analyzing the radicals is due to the interest in the conductivity (derealization of the electrons over the structure), which impacts the optical properties of soot. [0052] Figure 5 is a block diagram illustrating the major hardware components typically utilized in a computing device 580 used in conjunction with a system for simulating the chemical and physical properties of soot precursors in a combustion environment as described herein. Computing devices 580 are known in the art and are commercially available. A computing device 580 typically includes a processor 582 in electronic communication with input components 584 and/or output components 586. The processor 582 is operably connected to input 584 and/or output components 586 capable of electronic communication with the processor 582, or, in other words, to devices capable of input and/or output in the form of an electrical signal. Embodiments of computing devices 580 may include the inputs 584, outputs 586 and the processor 582 within the same physical structure or in separate housings or structures.

[0053] The computing device 580 may also include memory 588. The memory 588 may be a separate component from the processor 582, or it may be on-board memory 588 included in the same part as the processor 582. The processor 582 is also in electronic communication with a communication interface 590. The communication interface 590 may be used for communications with other computing devices, servers, etc. The computing device 580 may also include other communication ports 592. In addition, other components 594 may also be included in the computing device 580. [0054] Of course, those skilled in the art will appreciate the many kinds of different devices that may be used with embodiments herein. The computing device 580 may be a one-board type of computer, such as a controller, a typical desktop computer, such as an IBM-PC compatible, a PDA, a Unix-based workstation, or any other available computing device that is capable of operating the algorithms and methods disclosed herein. Accordingly, the block diagram of Figure 5 is only meant to illustrate typical components of a computing device 580 and is not meant to limit the scope of embodiments disclosed herein. [0055] Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0056] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. [0057] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array signal (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0058] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0059] The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the present invention. In other words, unless a specific order of steps or actions is required for proper operation of the embodiment, the order and/or use of specific steps and/or actions maybe modified without departing from the scope of the present invention.

[0060] While specific embodiments and applications of the present invention have been illustrated and described, it is to be understood that the invention is not limited to the precise configuration and components disclosed herein. Various modifications, changes, and variations which will be apparent to those skilled in the art may be made in the arrangement, operation, and details of the methods and systems of the present invention disclosed herein without departing from the spirit and scope of the invention.

Claims

CLAMS:

1. A method for modeling soot inception in a combustion environment, comprising: inputting information regarding the combustion environment; processing the information through a Kinetic Monte Carlo process to determine an intermediate configuration of a soot precursor; processing the information through a Molecular Dynamics process to simulate relaxation of the intermediate configuration to a new configuration of the soot precursor, wherein the new configuration is relaxed to a temperature of the combustion environment; updating the information regarding the combustion environment based upon the new configuration of the soot precursor; and repeating the Kinetic Monte Carlo process, the Molecular Dynamics process, and the updating step.

2. The method of claim 1, wherein the step of processing information through a Kinetic Monte Carlo process comprises: identifying possible reaction sites; evaluating kinetic rate constants and reaction rates; selecting a transition event having a probability proportional to the respective reaction rate; and randomly selecting a reaction site for the selected transition event.

3. The method of claim 1, wherein the step of processing information through a Molecular Dynamics process comprises: sampling atomic velocities at the temperature of the combustion environment; and relaxing the intermediate configuration to the new configuration.

4. The method of claim 1 , wherein information is processed through the Molecular Dynamics process after the information is processed through the Kinetic Monte Carlo process.

5. The method of claim 4, wherein the Kinetic Monte Carlo process, the Molecular Dynamics process, and the updating step are repeatedly processed one after the other.

6. The method of claim 1, wherein information regarding the combustion environment includes chemical species that are present in the combustion environment.

7. The method of claim 6, wherein information regarding the combustion environment further includes concentration profiles of the chemical species in the combustion environment.

8. The method of claim 6, wherein information regarding the combustion environment further includes a number of species of the chemical species in the combustion environment.

9. The method of claim 6, wherein information regarding the combustion environment further includes reaction rates among the chemical species present in the combustion environment.

10. The method of claim 6, wherein information regarding the combustion environment further includes a temperature profile of the combustion environment.

11. The method of claim 1, wherein information regarding the combustion environment includes chemical species that are present in the combustion environment, concentration profiles of the chemical species in the combustion environment, a number of species of the chemical species in the combustion environment, reaction rates among the chemical species present in the combustion environment, and a temperature profile of the combustion environment, and wherein the step of processing information through a Kinetic Monte Carlo process comprises: identifying possible reaction sites; evaluating kinetic rate constants and reaction rates; selecting a transition event having a probability proportional to the respective reaction rate; and randomly selecting a reaction site for the selected transition event; and wherein the step of processing information through a Molecular Dynamics process comprises: sampling atomic velocities at the temperature of the combustion environment; and relaxing the intermediate configuration to the new configuration.

12. A computing device configured for modeling soot inception in a combustion environment, the computing device comprising: a processor; memory in electronic communication with the processor; and executable instructions executable by the processor, wherein the executable instructions are configured to implement a method comprising: receiving input information regarding the combustion environment; processing the information through a Kinetic Monte Carlo algorithm to determine an intermediate configuration of a soot precursor; and processing the information through a Molecular Dynamics algorithm to simulate relaxation of the intermediate configuration to a new configuration of the soot precursor, wherein the new configuration is relaxed to a temperature of the combustion environment.

13. A computer-readable medium for storing program data, wherein the program data comprises executable instructions for implementing a method in a computing device for modeling soot inception in a combustion environment, the method comprising: receiving input information regarding the combustion environment; processing the information through a Kinetic Monte Carlo algorithm to determine an intermediate configuration of a soot precursor; processing the information through a Molecular Dynamics algorithm to simulate relaxation of the intermediate configuration to a new configuration of the soot precursor, wherein the new configuration is relaxed to a temperature of the combustion environment; updating the information regarding the combustion environment based upon the new configuration of the soot precursor; and . repeating the Kinetic Monte Carlo process, the Molecular Dynamics process, and the updating step.

14. The computer-readable medium for storing program data of claim 13, wherein the step of processing information through a Kinetic Monte Carlo process comprises: identifying possible reaction sites; evaluating kinetic rate constants and reaction rates; selecting a transition event having a probability proportional to the respective reaction rate; and randomly selecting a reaction site for the selected transition event.

15. The computer-readable medium for storing program data of claim 13, wherein the step of processing information through a Molecular Dynamics process comprises: sampling atomic velocities at the temperature of the combustion environment; and relaxing the intermediate configuration to the new configuration.

16. The computer-readable medium for storing program data of claim 13, wherein the information regarding the combustion environment includes chemical species that are present in the combustion environment, concentration profiles of the chemical species in the combustion environment, a number of species of the chemical species in the combustion environment, reaction rates among the chemical species present in the combustion environment, and a temperature profile of the combustion environment.

17. The computer-readable medium for storing program data of claim 13, wherein the information regarding the combustion environment includes chemical species that are present in the combustion environment, concentration profiles of the chemical species in the combustion environment, a number of species of the chemical species in the combustion environment, reaction rates among the chemical species present in the combustion environment, and a temperature profile of the combustion environment, and wherein the step of processing information through a Kinetic Monte Carlo process comprises: identifying possible reaction sites; evaluating kinetic rate constants and reaction rates; selecting a transition event having a probability proportional to the respective reaction rate; and randomly selecting a reaction site for the selected transition event; and wherein the step of processing information through a Molecular Dynamics process comprises: sampling atomic velocities at the temperature of the combustion environment; and relaxing the intermediate configuration to the new configuration.