CN117113883A - Design method for nuclear energy Brayton cycle optimization and evaluation - Google Patents
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Abstract
The application provides a design method for nuclear energy brayton cycle optimization and evaluation, which comprises the following steps: firstly, determining a scheme to be evaluated, and then establishing a corresponding model by using system simulation software; determining an optimization target and an evaluation index according to the constructed model; then, based on computational fluid dynamics simulation and a multi-objective optimization algorithm, carrying out optimization design on the Brayton cycle, and obtaining a corresponding optimization solution set; next, obtaining trade-offs from the solution set using four multi-attribute decision methods; evaluating the four trade-offs with a taylor diagram to determine a unique optimal result; then, quantifying, calculating and normalizing each evaluation index to obtain a corresponding index value; weighted average is carried out according to the weight of each evaluation index, and the score of each evaluation index is calculated; finally, integrating the scores of all the evaluation indexes to obtain an overall evaluation result, and adjusting the optimization scheme according to the evaluation result; the method can improve the nuclear energy Brayton cycle performance and simultaneously realize comprehensive evaluation and optimization of the scheme.
Description
Technical Field
The application relates to the technical field of power cycle, in particular to a design method for nuclear energy Brayton cycle optimization and evaluation.
Background
With the continued development of global economy and the rapid growth of the population, the increase in energy demand has become an unavoidable trend. Meanwhile, environmental problems caused by the use of traditional fossil fuels are also increasingly prominent, so that the development of clean and efficient alternative energy sources is particularly important. Nuclear energy is of great interest worldwide as a clean energy source of great potential. As a clean and efficient energy form, nuclear energy has great potential and application prospect. However, conventional nuclear power generation technologies have many drawbacks and challenges in terms of fuel utilization and energy conversion efficiency. For these problems, the brayton cycle technique has been developed.
The brayton cycle is a thermodynamic cycle based on a turbine, which generates high-temperature and high-pressure steam through a nuclear reactor, driving the turbine to generate electricity. The brayton cycle may use a wider range of fuel formats and improve fuel utilization and system compactness compared to conventional nuclear fuel rod technology. In addition, the technology can realize high-efficiency, multi-loop and low-temperature difference thermodynamic cycle, and improves the power output and the economy of the nuclear power station.
Currently, nuclear brayton cycle technology has been widely developed and used. For example, countries such as the united states, russia, france, etc. have built or have multiple brayton cycle plants built in the planning. Meanwhile, with the continued development and application of computer simulation and computational fluid mechanics techniques, modeling, optimization and evaluation of the brayton cycle have also become finer and more reliable. In the future, nuclear brayton cycle technology will continue to be of interest and support and become an important component of the clean energy field. However, as an important technology for realizing carbon neutralization in the future, a set of systematic modeling, optimizing, deciding and evaluating methods is lacking.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide a design method for nuclear energy brayton cycle optimization and evaluation, which utilizes modeling, selection index, multi-objective optimization, multi-attribute decision and evaluation according to the type of a known nuclear reactor and the arrangement mode of the cycle, and realizes optimization of a scheme and selection of an optimal scheme. In order to achieve the above object, the present application provides a design method for nuclear brayton cycle optimization and evaluation, comprising: the system comprises a scheme layer, a model layer, an index layer, an optimization layer, a decision layer and a target layer.
The scheme layer comprises: the reactor type comprises a sodium-cooled fast reactor, a gas-cooled fast reactor, a molten salt reactor and a lead-cooled fast reactor, and the system layout mode comprises a simple backheating cycle, a recompression cycle, a reheating cycle and an intercooling cycle; the model layer comprises: thermodynamic model, hydraulic heat exchange model, technical economy model; the index layer comprises: safety, compactness, thermal and economy; wherein safety includes natural preventative ability and natural palliative ability; compactness comprises unit net output work heat exchange area and turbine characteristic size; thermodynamic properties include net work output, thermodynamic efficiency, and exergy efficiency; economics include investment costs, net output power cost per unit, life cycle power generation costs, and internal profitability; the optimization layer comprises: determining an objective function, optimizing by a non-dominant sorting algorithm, and acquiring an objective solution set of each optimized scheme; the decision layer comprises: shannon entropy, correction TOPSIS, LINMAP and AHP four decision schemes, taylor diagram evaluation scheme; the target layer comprises a G1+TOPSIS comprehensive evaluation model. Meanwhile, through system simulation and experimental verification, the design scheme is further checked and improved, and the problems are extracted from the power plant and practical guidance is provided for the power plant.
In some embodiments, the solution layer considers different scenarios through theoretical analysis and practical application, and determines the final system layout and nuclear reactor type.
In some embodiments, the model layer models the brayton cycle in detail based on computational fluid dynamics simulation and molecular dynamics simulation, and yields corresponding performance parameters.
In some embodiments, the index layer defines and quantifies the optimization objective and the evaluation index, and the indexes are obtained by the model layer.
In some embodiments, the optimization layer improves the system by a non-dominant order algorithm, and the objective function is thermodynamic efficiency and life cycle average power cost.
In some embodiments, the decision layer makes decision strategies based on the importance and relevance of different factors to support the overall optimization process.
In some embodiments, the target layer obtains an overall evaluation result by comprehensively analyzing each evaluation index, and adjusts the optimization scheme according to the result.
The beneficial effects of the application are as follows:
(1) Through multi-level design and optimization, a plurality of key indexes can be comprehensively considered, and a complete and accurate optimization result is obtained through a non-dominant sorting algorithm and a comprehensive evaluation model.
(2) A plurality of decision schemes are comprehensively adopted, taylor diagram evaluation is carried out, the reliability and transparency of decisions can be improved, and the risk of decisions is reduced.
(3) Thermodynamic model, hydraulic heat exchange model and technical economy model are comprehensively considered, and system performance and economic benefit are comprehensively reflected.
(4) The method combines theoretical calculation and experimental verification, considers the actual demands and problems of the power plant, and can provide practical guidance for practice.
Drawings
FIG. 1 is a flow chart of a design method for nuclear Brayton cycle optimization and evaluation according to the present application.
Fig. 2 shows four brayton cycle arrangements in an embodiment of the application.
Fig. 3 is a pareto front graph and four multi-attribute decision points in an embodiment of the application.
FIG. 4 is a schematic diagram of Taylor diagram evaluation of four trade-offs to obtain an optimal solution in an embodiment of the present application.
Fig. 5 is a flowchart of g1+topsis multi-index comprehensive evaluation in an embodiment of the present application.
Detailed Description
The application is further explained in the following description with reference to the accompanying drawings, wherein it is necessary to note that the following detailed description is for the purpose of further explanation only and is not to be construed as limiting the scope of the application, as many insubstantial modifications and adaptations of the application are possible in light of the above teachings. It should be understood that all applications utilizing the concepts of the present application are well within the scope of protection.
Examples
In this embodiment, the first question to extract from the power plant is how to select an efficient and economical nuclear power generation system solution. Then a solution layer in the design is determined, which includes: the reactor type comprises a sodium-cooled fast reactor, a gas-cooled fast reactor, a molten salt reactor and a lead-cooled fast reactor, and the system layout mode comprises a simple backheating cycle, a recompression cycle, a reheating cycle and an intercooling cycle.
As shown in fig. 2, the four brayton cycle systems in the embodiment are arranged, and the most basic cycle structure is a simple regenerative cycle, which is composed of a steam turbine (Expander), a Compressor (Compressor), a heat exchanger (heat), a regenerator, a precooler (cooler), and a generator. The working medium is compressed near the critical point and then entersThe regenerator is heated and then reheated in a heat exchanger by high temperature reactor coolant. At this time, the working medium with higher enthalpy value at the outlet of the heat exchanger expands in the steam turbine to do work so as to drive the generator to generate electricity. Expanded CO 2 Will be cooled on the low pressure side of the regenerator and will be further cooled in the precooler in heat exchange with cooling water before being compressed again. The recompression cycle has one more recompressor and regenerator than the simple regenerative cycle. In the recompression circulation, the working fluid is split at the outlet of the low-temperature heat regenerator, one part of the working fluid enters the precooler, and the other part of the working fluid enters the recompression machine and finally merges at the high-pressure side inlet of the high-temperature heat regenerator. Because the specific heat capacity of the low-pressure side fluid of the heat regenerator is smaller than that of the high-pressure side fluid, the specific heat capacity of the two sides of the heat regenerator can be balanced by improving the mass flow of the low-pressure side fluid, the heat recovery is enhanced, and the problem of pinch points is avoided. The other two circulation arrangement modes are respectively a reheating circulation and an inter-cooling circulation. The working fluid undergoes two heating and expansion work in the reheat cycle, which means that more expansion work can be generated under the same compression work input condition, so that the cycle has higher net output work and cycle efficiency. The intercooling circulation is realized by two-stage compression and intercooling, and the required compression power is reduced by adjusting the minimum pressure and the intermediate pressure of the circulation, so that the purpose of improving the circulation efficiency is achieved.
The model layer in this embodiment is determined as: thermodynamic models, hydraulic heat exchange models, and technical economy models. The thermodynamic model mainly uses a first law of thermodynamics and a second law of thermodynamics to calculate the state parameters of each node working medium, and then calculates the thermal efficiency, exergy efficiency and net output power of the cycle. The hydraulic heat exchange model refers to a model of a heat exchanger in a circulation layout and comprises the heat exchanger, a heat regenerator and a precooler. In the embodiment, a printed plate heat exchanger (PCHE) is adopted as a heat exchanger model, a thermodynamic model of the heat exchanger is established by using a heat transfer theory, and meanwhile, a pressure drop formula of the heat exchanger is fitted, so that a basis is provided for thermodynamic parameter calculation of each node. The technical economy model comprises the system investment cost, the unit net output power cost, the life cycle average power generation cost of the power plant and the internal yield. The unit net work output investment cost is defined as the ratio of the total cost of investment to the net work output, and the internal yield refers to the ability of the investment project to withstand expansion of the currency, the greater the ability to resist risk of investment.
In this embodiment, various models are built in the model layer, and the model layer is implemented by simulation software, and operation parameters of each node are obtained after the model layer is built and operated. And then comparing and verifying with experimental data to perfect a mathematical model.
The index layer in this embodiment covers 11 indexes of four levels, specifically, four levels of safety, compactness, thermal property and economy. Wherein safety includes natural preventative ability and natural palliative ability; compactness comprises unit net output work heat exchange area and turbine characteristic size; thermodynamic properties include net work output, thermodynamic efficiency, and exergy efficiency; economics include investment costs, net output power costs per unit, life cycle power generation costs, and internal profitability.
The optimization layer in this embodiment adopts NSGA-II optimization algorithm to perform parameter optimization on 16 schemes formed by combining four nuclear reactors and four brayton cycle arrangements. Optimizing two contradictory target thermal efficiencyη t ) And life cycle power generation costLCOE). Selecting turbine inlet temperatureT max Outlet pressure of main compressorP max Turbine efficiency, compressor efficiencyη C Ratio of split flowSRTemperature difference of confluenceΔT cf And a primary compression pressureP pr As an optimization decision variable. Thus, the multi-objective optimization model can be expressed as:
the decision layer in this embodiment includes Shannon entropy, correction TOPSIS, LINMAP and AHP decision schemes, and taylor diagram evaluation schemes. The Shannon entropy method is based on information theory and is suitable for handling problems related to data uncertainty, measurement errors or information imperfections. It focuses on quantifying and maximally utilizing the information obtained from the different alternatives. The LINMAP (multidimensional preference analysis Linear programming technique) approach is used to address decision-making problems involving multiple attributes and preferences. It allows decision makers to compare alternatives according to various criteria and their preferences. The TOPSIS (ideal solution distance method) method aims at determining an alternative that is at the same time most satisfactory for all criteria, while minimizing the distance to the ideal solution. The AHP (analytic hierarchy process) method allows a decision maker to decompose a problem into manageable parts and make pair-wise comparisons based on subjective judgment. As shown in fig. 3, the pareto solution set obtained by optimizing the sodium cooled fast reactor in combination with the recompression brayton cycle system and the tradeoff obtained by using the four decision methods in this example are shown. The lower right hand corner of the figure is the Ideal point (Ideal point) where the system has the highest cyclic thermal efficiency and lowest life cycle average power generation cost, while the upper left hand corner is the worst state point. The parameters of each trade-off can be obtained by optimizing and deciding the result.
In this embodiment, each tradeoff solution is compared by the taylor diagram method to obtain the optimal solution. Taylor diagrams can visualize root mean square differences of each tradeoffR rmsd ) Correlation coefficient [ ]C coef ) Sum standard deviation of%S std ). The smaller the root mean square difference is, the smaller the error between the predicted value and the true value is, and the better the fitting degree of the model is. An absolute value of the correlation coefficient close to 1 represents a higher correlation of the evaluation results. The smaller the standard deviation, the smaller the degree of deviation of the decision point from the average value. As shown in fig. 4, each tradeoff is represented in the taylor diagram using one color, and the blue dotted line, the green dotted line, and the black circular arc represent the correlation coefficient, the root mean square difference, and the standard deviation, respectively. The black dot in the lower right corner is the ideal state dot. The best solution for the sodium cooled fast reactor in combination with the recompression brayton cycle scheme is the best solution selected by the TOPSIS method, as determined by taylor plot. The optimal operation parameters of 16 schemes and each evaluation index in the index layer can be obtained by the optimization layer and the decision layer. Finally, judging the optimal system side by the multi-index comprehensive evaluation method in the target layerAnd (3) a case.
In this embodiment, the objective layer considers the aspects of safety, thermodynamic, technical economy, compactness, and the like, and comprehensively evaluates each solution to obtain an optimal solution. The evaluation scheme adopts a G1+TOPSIS multi-index comprehensive evaluation model, and is specifically shown in FIG. 5. Firstly, summarizing the data of all the optimal schemes obtained in the decision layer, and then screening out indexes for evaluation. The data needs to be preprocessed before the index data is used, and the purpose of the preprocessing is to eliminate the dimensional influence of the data and forward the data. The weight of the evaluation needs to be determined between the indexes of each layer. In this embodiment, G1 (sequential analysis method) is adopted to perform weight distribution, and then a TOPSIS method is adopted to aggregate all indexes, so as to obtain a final evaluation value, wherein the higher the evaluation value is, the better the scheme is. And finally, checking whether the result is credible. The step of determining the weight by the G1 method is as follows:
(1) Determining the ordering relation among the indexes, and determining the ordering relation of the indexes according to the importance degree of each evaluation index in the study object. The ordering determined herein is B1> B2> B3> B4.
(2) And (3) providing comparison judgment of relative importance degree between adjacent indexes, and carrying out assignment:
wherein,nis the total number of evaluation indexes.
Calculating index weightsSubjective giving of the rational assignment->Then:
finally, the weight set obtained by the G1 method is
In the embodiment, the optimal nuclear energy brayton cycle scheme is finally determined to be a molten salt reactor combined with recompression cycle scheme through multi-index comprehensive evaluation. Thereby providing guidance for the design of the power conversion system of the nuclear brayton cycle power plant.
The above examples merely represent preferred embodiments of the present application and are merely for convenience of illustration and are not intended to limit the present application in any way. It should be noted that, for those skilled in the art, it is possible to make partial changes or modifications of the equivalent embodiments using the technical content disclosed in the application without departing from the technical characteristics of the application, and it is still within the protection scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (7)
1. A design method for nuclear brayton cycle optimization and evaluation, characterized by: the system comprises a scheme layer, a model layer, an index layer, an optimization layer, a decision layer and a target layer;
the scheme layer comprises: the reactor type comprises a sodium-cooled fast reactor, a gas-cooled fast reactor, a molten salt reactor and a lead-cooled fast reactor, and the system layout mode comprises a simple backheating cycle, a recompression cycle, a reheating cycle and an intercooling cycle;
the model layer comprises: thermodynamic model, hydraulic heat exchange model, technical economy model;
the index layer comprises: safety, compactness, thermal and economy; wherein safety includes natural preventative ability and natural palliative ability; compactness comprises unit net output work heat exchange area and turbine characteristic size; thermodynamic properties include net work output, thermodynamic efficiency, and exergy efficiency; economics include investment costs, net output power cost per unit, life cycle power generation costs, and internal profitability;
the optimization layer comprises: determining an objective function, optimizing by a non-dominant sorting algorithm, and acquiring an objective solution set of each optimized scheme;
the decision layer comprises: shannon entropy, correction TOPSIS, LINMAP and AHP four decision schemes, taylor diagram evaluation scheme;
the target layer comprises a G1+TOPSIS comprehensive evaluation model;
meanwhile, through system simulation and experimental verification, the design scheme is further verified and improved, and the problem extraction from the power plant can be realized and practical guidance can be provided for the problem extraction. .
2. A design method for nuclear brayton cycle optimization and evaluation according to claim 1, wherein the solution layer considers different situations through theoretical analysis and practical application and determines the final system layout and nuclear reactor type.
3. A design method for optimization and evaluation of the brayton cycle according to claim 1, characterized in that the model layer models the brayton cycle in a refined way based on computational fluid dynamics simulation and molecular dynamics simulation and yields corresponding performance parameters.
4. A design method for nuclear brayton cycle optimization and evaluation according to claim 1, characterized in that the index layer defines and quantifies the optimization objective and the evaluation index, and the indexes involved are obtained by the model layer.
5. A design method for nuclear brayton cycle optimization and evaluation according to claim 1, wherein the optimization layer improves the system by a non-dominant ranking algorithm, and the objective function is thermodynamic efficiency and life cycle average power cost.
6. A design method for nuclear brayton cycle optimization and evaluation according to claim 1, wherein the decision layer makes decision strategies depending on the importance and relevance of different factors to support the whole optimization process.
7. The design method for nuclear brayton cycle optimization and evaluation according to claim 1, wherein the objective layer obtains an overall evaluation result by comprehensively analyzing each evaluation index, and adjusts the optimization scheme according to the result.
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