A Multiobjective, Multidisciplinary Design Optimization of Solid Propellant Based Space Launch Vehicle

Multidisciplinary design and optimization of an innovative nano air launch vehicle with a twin-fuselage UAV as carrier aircraft

The design of new Space Launch Vehicle (SLV) involves a full set of disciplines – propulsion, structural sizing, aerodynamics, mission analysis, flight control, stages layout – with strong interaction between each other. Since multidisciplinary design optimization of multistage launch vehicles is a complex and computationally expensive. An efficient Least Square Support Vector Regression (LS-SVR) technique is used for trajectory simulation of multistage space launch vehicle. This newly formulation problem-about 17 parameters, linked to both the architecture and the command (trajectory optimization), 8 constraints – is solved through hybrid optimization algorithm using Particle Swarm Optimization (PSO) as global optimizer and Sequential Quadratic Programming (SQP) as local optimizer starting from the solution given by (PSO). The objective is to find minimum gross launch weight (GLW) and optimal trajectory during launch maneuvering phase for liquid fueled space launch vehicle (SLV).The computational cost incurred is compared for two cases of conceptual design involving exact trajectory simulation and with Least Square Support Vector Regression based trajectory simulation.KeywordsSupport Vector MachineParticle Swarm OptimizationConceptual DesignSupport Vector RegressionLittle Square Support Vector MachineThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper addresses a detailed preliminary design study of a twin fuselage semi-reusable TSTO launch vehicle. This double fuselage carrier, which would use hydrogen as a fuel, also collects the liquid oxygen (LOX), needed afterwards to propel the all-rocket orbiter. This collection takes place during an extended subsonic cruise phase that precedes the air launch of the orbiter. The concept of this 3 generation semi-reusable launch vehicle will be explained and the results from a detailed preliminary design with the AAA (Advanced Aircraft Analysis) software from Darcorp (USA) will also be given. The results for the aircraft weight distribution will be given as well as performance, aerodynamics and longitudinal stability indications for two reference missions. Besides that, the influence of the collection ratio obtained with the LOX collection plant upon the design of the launch vehicle is also addressed.

In order to design a vehicle body with high strength and high stiffness, a multi-disciplinary design process should include careful consideration of multi-disciplinary design constraints to properly account for vehicle static stiffness (bending/torsional), durability, Noise/Vibration/Harshness (NVH), crash worthiness, light weight vehicle structure during the early stage of vehicle design process. With this approach, fast development of new vehicle body structures can be achieved with minimal number of iterations to match the conflicting design goals from each discipline. In the current research, a multi-disciplinary design optimization (MDO) based on a meta model is developed and refined to apply for the design of body structure. In an effort to apply the MDO for vehicle body structure, 4 phase procedures were established in the current research. In Phase I, a base model is created. In Phase II, an effect analysis is carried out. In Phase III, a meta model is created. Finally in Phase IV, using the optimization algorithm, the meta model created in Phase III is eventually refined through the process of optimization. In this research, static stiffness (bending / torsional), dynamic stiffness (1st torsion mode) were used for constrained conditions and the mass minimization was the object function for optimization.

Prediction of the maximum expected electromagnetic pick-up of conductors inside a realistic shielding enclosure is an important canonical problem for system-level EMC design of space craft, launch vehicles, aircraft and automobiles. This paper introduces a simple statistical power balance model for prediction of the maximum expected current in a wire conductor inside an aperture enclosure. It calculates both the statistical mean and variance of the immission from the physical design parameters of the problem. Familiar probability density functions can then be used to predict the maximum expected immission for design purposes. The statistical power balance model requires minimal EMC design information and solves orders of magnitude faster than existing numerical models, making it ultimately viable for scaled-up, full system-level modeling. Both experimental test results and full wave simulation results are used to validate the foundational model.

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

The design of multidiscipli nary systems (such as aircraft, automobiles, and others) often requires an iterative cycle that includes a design initialization, a system analysis, a sensitivity analysis, and design optimization. This design cycle is standard in the field of Multidisciplinary Optimization (MDO) and has often been referred to in literature as the Multiple Discipline-Feasible (MDF) approach. The name stems from the fact that complete multidisciplinary feasibility is maintained each and every design cycle. The drawback of MDF is that it can be a timely and a costly procedure. Numerous researchers have developed alternate means for posing and subsequently solving the multidisciplinary design problem. One such solution procedure has been referred to both as Simultaneous Analysis and Design (SAND) and All-at-Once (AAO), and treats the entire multidisciplinary design cycle as one large optimization problem. Another alternate solution procedure lias been referred to as Individual-Discipline-Feasible (DDF); this procedure exhibits characteristics which lie in between the two extremes exemplified by MDF and AAO. IDF assures that each individual discipline is feasible on every design cycle, while driving the entire system (all disciplines) towards multidisciplinary feasibility. The present work will present a rigorous numerical comparison of these solution strategies over a wide variety of problem sizes and complexities. The multidisciplinary design test problems that are used for these comparisons are generated by a robust simulation tool called CASCADE. * Research Assistant, Student member, AIAA | Associate Professor, Member AIAA Background and Motivation Concurrent engineering is a systematic approach to the integrated, simultaneous design of products and their related processes. Many of the recently developed capabilities to address concurrent engineering have stemmed from the emerging area of Multidiscipli nary Optimization, or MDO. The origins of MDO can be traced back to the early 1980's, where Sobieski14 used a linear decomposition approach to subdivide the design of a large engineering system into a grouping of related and more manageable subsystems. However, such a decomposition often results in a grouping of subsystems which cannot be placed into a definitive top-down hierarchy. The resultant decomposition grouping is typically hybrid-hierarchic in nature as shown in the example decomposition of Figure 1.

In order to reduce the cost of oceanographic exploration, a new underwater vehicle is designed to sail the required distance with the lowest energy consumed. Since the new underwater vehicle is a complicated multidisciplinary system, it is firstly decomposed into four smaller disciplines and then a multidisciplinary design optimization (MDO) problem is built based on these disciplines. The Multidisciplinary Feasible (MDF) architecture is adopted as the solution strategy to this optimization problem considering that it is easily implemented and a multidisciplinary feasible solution is always guaranteed throughout the optimization process. To solve this optimization problem efficiently, the coupled adjoint method is firstly introduced to improve the efficiency of gradient calculation and then a discipline-merging method is proposed to further enhance the computational efficiency. After this, the discipline-merging method is verified against the finite difference method in two aspects of solution accuracy and computational costs and the results show it is an effective and high efficient gradient calculation method. Finally, the multidisciplinary design optimization of the new underwater vehicle is performed efficiently under the MDF architecture with the discipline-merging method to calculate gradients.

Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an a priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecutre in a multidisciplinary design environment are also discussed.

The optimal design of multidisciplinary systems under uncertainty involves three types of iterative analyses- multidisciplinary analyses, reliability analyses, and optimization. If these processes are carried out in a nested manner in reliability based multidisciplinary design optimization (RBMDO), the computational expense can be enormous. Computationally efficient simultaneous analysis and design (SAND) or All-AtOnce methods have been developed earlier for deterministic MDO. Single loop RBDO methods have also been developed to integrate reliability analysis and optimization in a single loop. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints. The formulation allows for solution with a single design optimization loop by using the KKT optimality conditions of the first-order reliability method (FORM) as constraints in the design optimization along with disciplinary consistency constraints. Alternative solution processes which remove the FORM MPP’s from the decision space and many constraints from the problem are developed. The alternative solution methods are more efficient and more robust than using nonlinear programming to solve the single loop formulation. A numerical example is solved with the two proposed solution algorithms. I. Introduction In the design of multidisciplinary systems it is often necessary to make decisions under uncertainty. This requires that multidisciplinary systems modeling be integrated with uncertainty analysis and optimization. A common example of such an engineering design application would be the design of an aircraft wing under uncertain aerodynamic loading conditions, with multiple structural components with uncertain capacities, such that the entire structural system achieves a prescribed level of reliability. The problem has coupled physics, in that the pressure distribution is dependent upon the deflection, and the deflection is dependent on the pressure distribution. Such designs have been performed using multidisciplinary optimization (MDO). Other examples where MDO may be applied include electronics packaging, in which the disciplines of heat transfer and circuit analysis are coupled, the design of new types of aircraft and integrating them into an existing fleet of aircraft, and even the management of interconnected homeland infrastructures. While MDO has matured with the development of different types of algorithms for various types of problems and design organizations, there has been little work to incorporate reliability analysis into MDO. However, system and component reliabilities are often a major concern in the design of multidisciplinary systems. MDO can be computationally expensive due to the need to achieve consistency across multiple disciplinary analysis codes; each disciplinary analysis code may be computationally expensive to run. Further, the optimal design of multidisciplinary systems under uncertainty involves three types of iterative procedures- multidisciplinary analyses, reliability analyses, and optimization. The integration of these three procedures is referred to in this paper as reliability based multidisciplinary design optimization (RBMDO). If these processes are carried out simultaneously, the computational expense can be enormous. Computational savings can often be achieved with simultaneous analysis and design (SAND) or All-At-Once methods, which allow for the solution of the multidisciplinary analyses and the optimization in one single loop. Single loop RBDO methods allow for the integration of reliability analysis and optimization in a single loop. Applying SAND and single-loop RBDO together may allow for large computational savings when applied to problems in RBMDO. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints based on the SAND method for MDO and by use of the KKT conditions of the first order reliability method (FORM). The resulting formulation may be solved as a standard nonlinear programming problem. While the single loop formulation presented in this paper is a solution of the RBMDO problem, robustness and efficiency are improved through two improved algorithms which can remove the MPP’s from the decision space, thereby significantly reducing the optimizer’s workload.

With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input–output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.

Research on multidisciplinary fatigue optimization design method in structural design of high speed train

The design of space systems is a complex and multidisciplinary process with multiple conflicting objectives, large number of design variables, and constraints that limits application of the existing multidisciplinary design optimization architectures to this class of design problems. This paper presents an enhanced multidisciplinary design optimization architecture to concurrent holistic design optimization of a satellite system. The proposed multidisciplinary design optimization architecture extends concepts of multidiscipline feasible and bi-level integrated system synthesis into a unified architecture using metamodels. The proposed architecture was evaluated and compared with the existing multidisciplinary design optimization architectures that include all-at-once, bi-level integrated system synthesis, and multidisciplinary design optimization using a remote sensing small satellite in low earth orbit. The satellite design optimization problem deals with the minimization of the total mass of the satellite, involving disciplines of mission analysis, payload, structures, attitude determination and control, communication, command and data handling, power and thermal. The computational performance and accuracy of the proposed architecture were compared with multidisciplinary design optimization benchmark problems. Then the proposed architecture is successfully applied to the satellite system design problem. The results obtained show that metamodel-based bi-level integrated system synthesis-multidisciplinary design optimization architecture presented in this paper provides an effective way of solving large-scale design problems.

5 - Multidisciplinary Design and Optimization Methods

To meet the ever-expanding needs of satellite customers, a new generation of launch vehicles in the Long-March (LM) family is being developed according to the principles of low-cost, high-reliability, and maximum flexibility. By reducing the pre-launch preparation time, the launch system will strengthen the capability of providing international commercial launch services in the future. The new generation launch vehicles consist of several standard modules featuring two types of newly developed engines and three new modules powered by non-toxic, non-polluting propellants. Among the three modules, 5-m module is powered by two 50-ton-thrust engines which burn liquid hydrogen (LH2) and liquid oxygen (LOX), the 3.35-m module is powered by two 120-ton-thrust engines which burn LOX and kerosene (KO), and the 2.25-m one is powered by one 120-ton-thrust engine. As a first step, a series of large-scale launch vehicles is formed with 5-m-dia common core. By modifying the modules, the next step is to combine medium launch vehicles with the 3.35-m-dia core and a small launch vehicle with the 2.25-m-dia core. Finally a new group of launch vehicles will be formed to accommodate a wide range of performance requirements, which provide a capability of delivering payloads ranging from 1.5 to14 tons to geosynchronous transfer orbit (GTO) and up to 25 tons to low earth orbit (LEO). This paper highlights the design principles, systems concept and a series of configurations for the new generation of the LM launch vehicles. Copyright  2003 by Beijing Institute of Astronautical Systems Engineering. Published by the American Institute of Aeronautics and Astronautics, Inc. with permission. Released to IAF/IAA/AIAA to publish in all forms. The Long-March family and its history The Long-March (LM) launch vehicle program was initiated in mid 1960s in China. In April 1970, the first launch vehicle LM-1 made its maiden flight and sent DFH-1 satellite into the earth orbit, which indicated that China entered the space era. In the past three decades, the LM family was developed rapidly and formed a series of launch vehicles consisting of LM-2C, LM-2E, LM-3, LM-3A, LM-3B, LM-4, etc. These launch vehicles are suitable for various tasks, such as LEO, SSO and GTO missions. The largest launch capacity of the LM family has reached 9,200 kg for LEO, and 5,200 kg for GTO, which basically meets the demands of various customers. Up to now, the LM family has accomplished 70 launches. From October 1996 to July 2003, 28 consecutive successful flights were made, which established a new record in the LM history. Requirements analysis Although LM family has made eye-catching achievements in many important fields of space transportation technology, the assessment of 1 54th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law 29 September 3 October 2003, Bremen, Germany IAC-03-V.1.04 Copyright © 2003 by the author(s). Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Released to IAF/IAA/AIAA to publish in all forms. launch services market clearly shows the need for the LM launch vehicles to be upgraded. − Lift-capability and flexibility need to be increased not only to match the continuing upward trend in terms of GEO communication satellites, but also enable new applications such as constellations, scientific missions and multiphase releases. − Present propellants have to be replaced by non-toxic, non-polluting ones in order to satisfy the demand of the environment protection. − Higher reliability and safety are required for commercial launches, especially for the manned mission. − Launch cost must be significantly reduced for the future market.