Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm

Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Optimal proportioning of iron ore in sintering process based on improved multi-objective beluga whale optimisation algorithm

Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥/t,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.

Data-driven placemaking: Public space canopy design through multi-objective optimisation considering shading, structural and social performance

In the context of ongoing densification of cities and aging urban populations,public spaces are a crucial infrastructure to support the physical and mental wellbeing of urban residents.The design of public space furniture elements is often standardised,and not considered in relation to environmental conditions and mechanisms of social interaction.This article presents a digital workflow to generate site-specific designs for shaded public seating,considering the relationships of local public places to their surroundings.A strategy for customised and site-specific design is developed through the use of multiple software tools,employing evolutionary algorithms and multi-objective optimisation.The method is applied to a small public space canopy prototype installed within a public housing estate in Hong Kong,incorporating additional criteria to achieve a low-cost and light-weight structure.Through multiple stages of refinement and optimisation,a material,structural and social performance-driven outcome was achieved that creates a shaded space for public seating,people watching and social interaction.As part of a larger research agenda exploring architectural form-finding and environmental psychology,the project represents potential new applications in the emerging field of socially driven computational design.

基于多目标蝗虫优化算法的全国棉花产量预测

随着全球气候变化和农业发展的影响,棉花产量的预测分析对于农业规划和资源配置至关重要。为了对全国棉花产量进行更精确的预测,本文提出了一种多目标蝗虫优化(MOGOA)组合预测方法。首先运用ARIMA时间序列模型、最小二乘支持向量机LSSVM模型、循环神经网络RNN模型3种单项模型对2009-2023年全国棉花产量数据进行预测。然后,通过多目标蝗虫迭代优化过程,得到了一组最优解,并将单项模型预测结果与组合预测方法预测结果相对比。通过实例验证,运用多目标蝗虫优化的组合预测方法预测结果误差更小、拟合程度更高,证明了该模型在实际应用中具有良好的价值,更好地反映出棉花产量的实际变化情况。最后使用该方法对2024—2026年的全国棉花产量进行预测,为棉花产业发展提供参考。