摘要
气体吸收光谱的不连续性使得精确的红外辐射计算需要采取逐线计算(LBL)方式,其算力需求在三维工程问题中难以满足。多尺度多线组宽带(MSMGWB)k分布模型在非等温非均匀的远程红外辐射探测计算场景中能够大幅减少辐射传输方程的求解个数并保持与LBL相似的精度。研究了分组策略、高斯求积节点数量、参考温度3个因素对MSMGWB模型计算效率和精度的影响,以56个航空发动机排气系统的典型一维工况评价优化效果,采用非支配遗传算法能够快速得到模型计算精度与效率的Pareto前沿。对三维喷管远程红外辐射探测的结果表明,利用基于该遗传算法得到的MSMGWB模型参数能够有效提升模型的计算精度与效率。
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 nonuniformity disrupts the correlatedk(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 nonuniformity.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 nonuniform 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 multiscale multigroup wideband kdistribution(MSMGWB)model integrates the multigroup multiscale method with the kdistribution approach,achieving a favorable balance between computational cost and accuracy when predicting longrange 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 nonunique 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 nondominated 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 biobjective 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 preoptimized version.In the 3‒5μm band,the preoptimized 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 8‒14μm band,the preoptimized 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 threedimensional scenario involving supersonic aircraft engine exhaust and longrange 3‒5μm 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 linebyline 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 CO_(2) grouping combinations,Gaussian quadrature points,and the performance of reference temperatures is evaluated.A trifactor biobjective optimization method based on a nondominated sorting genetic algorithm is then proposed,introducing iterative scanning and dualpopulation size techniques to improve computational efficiency.In 56 onedimensional test cases,the optimized model demonstrates an 8.6%reduction in computational cost and a 62.4%decrease in error metrics for the 3‒5μm band.For the 8‒14μm band,it shows a 24.4%reduction in computational cost and a 51.4%decrease in error metrics compared to the preoptimized model.In realistic threedimensional scenarios,such as aircraft engine exhaust systems and longrange infrared imaging of jets,the optimized model achieves an error margin of less than 5%when compared to linebyline calculation results.
作者
李一涵
胡海洋
王强
Li Yihan;Hu Haiyang;Wang Qiang(School of Energy and Power Engineering,Beihang University,Beijing 100206,China)
出处
《光学学报》
CSCD
北大核心
2024年第24期308-318,共11页
Acta Optica Sinica
基金
国家科技重大专项(J2019-III0009-0053)。
关键词
红外辐射
气体辐射
遗传算法
宽带模型
k分布模型
infrared radiation
gas radiation
genetic algorithm
wideband model
kdistribution model
