Wide-Band k-Distribution Gas Radiation Transfer Model Optimized Using Non-Dominated Genetic Algorithm
Objective The temperature and pressure of gas jets,along with the molar ratios of the primary radiative components(carbon dioxide and water vapor),differ significantly from atmospheric conditions.This non-uniformity disrupts the correlated-k(CK)properties of gas absorption spectra,resulting in substantial errors in radiation models that depend on CK properties.Research has shown that"hot lines"in the absorption spectra of radiative components significantly contribute to CK property disruption caused by temperature non-uniformity.Existing solutions to address this issue fall into two categories:the multiple line group(MLG)method and the spectral mapping method(SMM).These approaches divide the absorption spectrum or absorption lines into subsets to preserve CK properties under various thermodynamic states.CK property disruption caused by non-uniform molar ratios stems from differences in the absorption spectra of radiative components.Current methods to address this include joint distribution functions,multiple integration,and convolution techniques,all of which increase computational demand,especially when combining solutions to manage multiple disruption mechanisms simultaneously.The multi-scale multi-group wide-band k-distribution(MSMGWB)model integrates the multi-group multi-scale method with the k-distribution approach,achieving a favorable balance between computational cost and accuracy when predicting long-range infrared radiation signals of hot gas jets.This balance arises from addressing both CK property disruption mechanisms using a unified approach.However,the MSMGWB method's random initialization of groupings results in non-unique outcomes,requiring optimal selection.In addition,determining suitable reference temperatures and Gaussian quadrature points is computationally challenging due to the vast combination space,making exhaustive optimization impractical.To overcome these limitations,we propose an improved non-dominated sorting genetic algorithm that rapidly identifies optimal grouping schemes,reference temperatures,and Gaussian quadrature points by using computational efficiency and accuracy as dual objective functions.Methods A genetic model was developed for the bi-objective genetic algorithm,addressing the number of Gaussian quadrature points and reference temperatures.The algorithm's iteration process includes selection,crossover,mutation schemes,and termination criteria.Two objective functions are defined to measure computational accuracy and efficiency.We validate the algorithm by comparing its performance against exhaustive optimization within a smaller sample space.The genetic algorithm demonstrates superior efficiency and accuracy.In addition,we analyze the influence of different grouping strategies for water vapor and carbon dioxide on the objective functions.Based on this analysis,four iterative schemes for selecting suitable grouping strategies are proposed,validated,and analyzed.To enhance efficiency,we examine the influence of generation population size in the genetic algorithm on computational outcomes and design an iterative process that begins with a smaller population and gradually scales up.This approach leads to the development of a comprehensive framework for aligning Gaussian quadrature points,reference temperatures,and grouping strategies for water vapor and carbon dioxide.Results and Discussions The MSMGWB model shows significant improvements in computational accuracy after optimization compared to its pre-optimized version.In the 3-5 μm band,the pre-optimized model achieves an error metric of feer=5.59 with a computational cost of fN=70.After optimization,the error metric is reduced to feer=2.10,and the computational cost decreases to fN=64,representing an 8.6％improvement in computational efficiency and a 62.4％reduction in error(Fig.14).In the 8-14 μm band,the pre-optimized model has feer=7.01 and fN=95,while the optimized model reduces feer to 3.40 and fN to 72,representing a 24.4％reduction in computational cost and a 51.4％decrease in error(Fig.15).In a realistic three-dimensional scenario involving supersonic aircraft engine exhaust and long-range 3-5 μm infrared detection,optimized MSMGWB model shows high computational efficiency with minimal error(Fig.16).The nozzle has a maximum outer diameter of 1220 mm and a wall emissivity of 0.8.At a flight altitude of 7 km,with an infrared imaging device 20 km away,the model closely matches line-by-line calculation results.Slightly higher errors are observed in the jet region compared to solid wall surfaces.Conclusions In this study,we first analyze the MSMGWB model's grouping strategy,addressing the uncertainties from random initialization.The influence of H2O and CO2 grouping combinations,Gaussian quadrature points,and the performance of reference temperatures is evaluated.A tri-factor bi-objective optimization method based on a non-dominated sorting genetic algorithm is then proposed,introducing iterative scanning and dual-population size techniques to improve computational efficiency.In 56 one-dimensional test cases,the optimized model demonstrates an 8.6％reduction in computational cost and a 62.4％decrease in error metrics for the 3-5 μm band.For the 8-14 μm band,it shows a 24.4％reduction in computational cost and a 51.4％decrease in error metrics compared to the pre-optimized model.In realistic three-dimensional scenarios,such as aircraft engine exhaust systems and long-range infrared imaging of jets,the optimized model achieves an error margin of less than 5％when compared to line-by-line calculation results.
infrared radiationgas radiationgenetic algorithmwide-band modelk-distribution model
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