Multidisciplinary Feasible Method Research Articles

Trajectory/propulsion integrated design optimization of the manned lunar lander propelled by hybrid rocket motors using analytical target cascading

Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future.

Optimal design of blade in pump as turbine based on multidisciplinary feasible method.

In order to make the pump as turbine (PAT) run efficiently and safely, a multidisciplinary optimization design method for PAT blade, which gives consideration to both the hydraulic and intensity performances, is proposed based on multidisciplinary feasibility (MDF) optimization strategy. This method includes blade parametric design, Latin Hypercube Sampling (LHS) experimental design, CFD technology, FEA technology, GA-BP neural network and NSGA-II algorithm. Specifically, a parameterized PAT blade with cubic non-uniform B-spline curve is adopted, and the control point of blade geometry is taken as the design variable. The LHS experimental design method obtains the sample points of training GA-BP neural network in the design space of variables. The hydraulic performance of each sample point (including the hydraulic pressure load on the blade surface) and the strength performance analysis of blades are completed by CFD and FEA technology respectively. In order to save calculation time of the whole optimization design, the multi-disciplinary performance analysis of each sample in the optimization process is completed by single-coupling method. Then, GA-BP neural network is trained. Finally, the multi-disciplinary optimization design problem of PAT blade is solved by the optimization technology combining GA-BP neural network and NSGA-II algorithm. Based on this optimization method, the PAT blade is optimized and improved. The efficiency of the optimized PAT is improved by 1.71% and the maximum static stress on the blade is reduced by 7.98%, which shows that this method is feasible.

Aerodynamic–structural missile fin optimization

The aim of the paper is to establish and demonstrate the significant capacity and performances of a well predictive numerical environment, developed for multidisciplinary design optimization (MDO) purposes, which presents one more contribution in cases of fluid–structure interaction (FSI) numerical modeling. Numerical modeling of fluid–structure interaction was conducted through closely coupled aerodynamic and structural computational domains, with very good overall computational reliability and accuracy. Various available experimental results, which were obtained mostly for calibration purposes, have been used for computational fluid dynamics (CFD) and computational structural mechanics (CSM) validation and verification, in order to assure that numerical optimization could be carried out with acceptable accuracy.The numerical optimization procedure was applied on the short range ballistic missile fin configuration, which was developed for scientific and internal experimental, testing and calibration purposes in Military Technical Institute (VTI) in Belgrade. The proposed monolithic, multimodular and on commercial code-based numerical environment, with adopted multipoint regimes and multicriteria settings was used for aerodynamic–structural optimization. The multidiscipline aerodynamic shape optimization, with respect to predefined objectives and constrains, was carried out in order to achieve global improvement of initial aerodynamic–structural responses of the mentioned configuration. The multidisciplinary feasible method proposed in this paper, is a single level method driven by an embedded surrogate-based evolutionary optimizer. The developed algorithm enabled an increased number of feasible optimal geometries of fin, while its special feature was the overall improvement of the missile initial geometry, with decreased costs of experimental and numerical resources.A special challenge of this research was to overcome scaling between available the wind tunnel missile model geometry, used for aerodynamic experiments, and the real fin geometry model, used for static structural experiments.

Multidisciplinary design and optimization of the twin-web turbine disk

The newly designed structures are employed to improve the high pressure turbine (HPT) disk to the expected performance. The twin-web disk (TWD) has been proven to be the future trend of the HPT disk due to its breakthrough in weight loss, strength and heat transfer efficiency compared to the conventional single web disk (SWD). Because of the multi-physics working conditions and intense coupling of multiple disciplines, the conventional design of the HPT disk is a labor intensive work. A series of design procedures, including asymmetrical computational fluid dynamics (CFD) analysis, inverse distance weighted (IDW) interpolation method, multidisciplinary feasible method (MDF), and design of experiments (DOE), are proposed to obtain the proper design for both TWD and SWD in an efficient way. In the present work, the multidisciplinary design of optimization (MDO) has been performed to find the proper shape of the TWD disk with the minimum mass. The results showed that the TWD exhibits a better performance in heat transfer and weight loss than SWD. The modeling and optimization procedure of this work can be referred for engineering design.

Multidisciplinary Collaborative Optimization of General Parameters of Low Orbit Spacecraft

This article takes the optimization of general parameters of low-orbit spacecraft as object. Three subsystems of attitude and orbit control subsystem, power subsystem and structure subsystem were researched. Coupling relations between subsystems and system were described. Analysis models of the subsystems were provided. Meanwhile, optimization models of the researched problem were established based on collaborative optimization (CO). Sequential quadratic programming (NLPQL) was selected as search strategy of CO subsystem optimizer. While NLPQL, adaptive simulated annealing algorithm (ASA) and multi-island genetic algorithm (MIGA) were respectively selected as the search strategy of CO system-level optimizer to search. Furthermore, multidisciplinary feasible method (MDF) was used to optimize the same problem. NLPQL, ASA, MIGA were respectively selected as the search strategy of MDF system-level optimizer. The optimization result of CO was compared to the optimization result of MDF shows that using CO can get the result which close to the optimal result. That proves CO can be effectively applied to multidisciplinary design optimization of the general parameters of the low-orbit spacecraft.

다정밀도 해석기법을 이용한 무인항공기 다분야통합 최적설계

In this study, Multi-fidelity analysis is performed to improve the accuracy of analysis result during conceptual design stage. Multidisciplinary Design Optimization(MDO) method is also considered to satisfy the total system requirements. Low-fidelity analysis codes which are based on empirical equations are developed and validated for analyzing the Unmanned Aerial Vehicle(UAV) which have unconventional configurations. Analysis codes consist of initial sizing, aerodynamics, propulsion, mission, weight, performance, and stability modules. Design synthesis program which is composed of those modules is developed. To improve the accuracy of the design method for UAV, Vortex Lattice Method is used for the strategy of MFA. Multi-Disciplinary Feasible(MDF) method is used for MDO technique. To demonstrate the validity of presented method, the optimization results of both methods are compared. According to those results, the presented method is demonstrated to be applicable to improve the accuracy of the analyses during conceptual design stage.

Reliability-Based Multidisciplinary Design Optimization for Centrifugal Compressor Using the Fourth Moment Method

A reliability-based multidisciplinary design optimization (RBMDO) frame work for Centrifugal compressor was presented. Multidisciplinary feasible method was used to decouple the multidisciplinary analysis and the fourth moment method for reliability analysis was recommended systematically. Based on the approximation, the RBMDO framework was finished. The case study shows that optimization efforts could improve obviously the performance of centrifugal compressor under the requirements of reliability. This framework could make the design reach the best performance with a good reliability. It indicates that the proposed optimization method is available and feasible for the engineering application.

Conceptual design of wrap-around-fin rockets using the MDO methodology

The conceptual design of rockets is a system engineering that involves configuration, aerodynamics, engine, dynamic stability, and coupling effects. To obtain benefits from the synergistic effects, the latest research achievements, and the most powerful analysis tools efficiently, this paper focuses on how to apply the multidisciplinary design optimization (MDO) technology to the conceptual design of the long-range, large length to diameter ratio wrap-around-fin (WAF) rockets. The software iSIGHT was employed as the MDO framework, the multidisciplinary feasible (MDF) method as the MDO architecture, and the multi-island genetic algorithm combined with sequential quadratic programming as the search algorithm. By maximizing the payload ratio, the structural analysis, aerodynamics, engine, trajectory, and dynamic stability were considered comprehensively. A case study demonstrated that MDO could improve the rocket performance effectively.

An asymmetric suboptimization approach to aerostructural optimization

An asymmetric suboptimization method for performing multidisciplinary design optimization is introduced. The objective of the proposed method is to improve the overall efficiency of aerostructural optimization, by simplifying the system-level problem, and thereby reducing the number of calls to a potentially costly aerodynamics solver. To guide a gradient-based optimization algorithm, an extension of the coupled sensitivity equations is developed to include post-optimality information from the structural suboptimization. The optimization of an aircraft wing is performed using linear aerodynamic and structural analyses, and a thorough performance comparison is made between the new approach and the conventional multidisciplinary feasible method. The asymmetric suboptimization method is found to be the more efficient approach when it adequately simplifies the system-level problem, or when there is a large enough discrepancy between disciplinary solution times.

Aero-structural optimization using adjoint coupled post-optimality sensitivities

A new subspace optimization method for performing aero-structural design is introduced. The method relies on a semi-analytic adjoint approach to the sensitivity analysis that includes post-optimality sensitivity information from the structural optimization subproblem. The resulting coupled post-optimality sensitivity approach is used to guide a gradient-based optimization algorithm. The new approach simplifies the system-level problem, thereby reducing the number of calls to a potentially costly aerodynamics solver. The aero-structural optimization of an aircraft wing is performed using linear aerodynamic and structural analyses, and a performance comparison is made between the new approach and the conventional multidisciplinary feasible method. The new asymmetric suboptimization method is found to be the more efficient approach when it adequately reduces the number of system evaluations or when there is a large enough discrepancy between disciplinary solution times.

Application of Collaborative Optimization Using Genetic Algorithm and Response Surface Method to an Aircraft Wing Design

Collaborative optimization(CO) is a multi-level decomposed methodology for a large-scale multidisciplinary design optimization(MDO).CO is known to have computational and organizational advantages. Its decomposed architecture removes a necessity of direct communication among disciplines, guaranteeing their autonomy. However,CO has several problems at convergence characteristics and computation time. In this study, such features are discussed and some suggestions are made to improve the performance ofCO. Only for the system level optimization, genetic algorithm is used and gradient-based method is used for subspace optimizers. Moreover, response surface models are replaced as analyses in subspaces. In this manner,CO is applied to aero-structural design problems of the aircraft wing and its results are compared with the multidisciplinary feasible (MDF) method and the original CO. Through these results, it is verified that the suggested approach improves convergence characteristics and offers a proper solution.

다분야 최적화 기법을 이용한 공중발사 로켓 최적설계

공중발사 방식은 일반적인 지상발사와 비교해 볼 때 많은 장점을 지니고 있다. 그러나 공중발사 로켓의 형상은 모선에 장착 시 많은 제한이 따르기 때문에 여러 해석분야를 통합한 시스템 설계가 필요하다. 시스템 설계는 순차적 최적화와 MDF 기법을 이용하여 수행되었다. 해석 모듈은 임무분석, 단배분, 추진해석, 형상, 중량해석, 공력해석, 궤적해석을 포함한다. 두 가지 기법 중 MDF 기법을 이용하였을 때 더 좋은 결과를 도출하였다. 시스템 최적화 결과 총 중량 1244.91kg, 위성중량 7.5kg, 총 길이 6.36 m, 지름 0.60m을 지닌 초음속 공중발사 로켓이 설계되었다. Compared with the conventional ground rocket launching, air-launching has many advantages. However, a comprehensive and integrated system design approach is required because the physical geometry of air launch vehicle is quite dependent on the installation limitation of the mother plane. The system design has been performed using two different approaches: the sequential optimization and the multidisciplinary feasible(MDF) optimization method. Analysis modules include mission analysis, staging, propulsion analysis, configuration, weight analysis, aerodynamics analysis and trajectory analysis. MDF optimization shows better results than the sequential optimization. As a result of system optimization, a supersonic air launching rocket with total mass of 1244.91kg, total length of 6.36m, outer diameter of 0.60m and the payload mass of 7.5kg has been successfully designed.