Multidisciplinary Design Optimization Architectures Research Articles

Reliability-based multidisciplinary design optimization of an aeroelastic unpowered guided aerial vehicle

Most Aeronautical and Astronautical Systems (AAS) are inherently complex, multidisciplinary, nonlinear, and computationally intensive for design and analysis. Utilizing the Reliability-Based Multidisciplinary Design Optimization framework can address the multidisciplinary nature of these systems while accounting for inherent uncertainties. In this paper, an efficient methodology for Reliability-Based Multidisciplinary Design optimization of an aerial vehicle is developed. The computational burden of reliability assessment could make its integration within a Multidisciplinary Design Optimization cycle a formidable task. In this respect, a multilevel Multidisciplinary Design Optimization architecture is proposed in which the computational cost is reduced by considering the reliability analysis, as needed only for critical subsystems. To this end, a single-level Reliability-Based Multidisciplinary Design Optimization is derived using the Performance Measure Analysis and the Karush-Kuhn-Tucker condition. The work demonstrates the integration of this formulation into the proposed multilevel Reliability-Based Multidisciplinary Design Optimization architecture. The proposed design architecture is implemented for an aeroelastic Unpowered Guided Aerial Vehicle whose outcomes are compared with previous results obtained via a mono-level Uncertainty-Based Multidisciplinary Design Optimization architecture.

Multidisciplinary design optimization of a wide speed range vehicle with waveride airframe and RBCC engine

The wide speed range vehicle is believed to be ideal for space round trip and its design process incorporates a number of disciplines. To achieve an overall optimal design, we adopt the multidisciplinary design optimization (MDO) approach to design the wide speed range vehicle with waverider being the airframe and the rocket-based combine cycle (RBCC) engine being the propulsion system. The design process includes the modeling of each discipline, the MDO system integration and the operation of the MDO system. Disciplines integrated in the MDO architecture includes the geometry, the aerodynamics, the propulsion, the structural mass and the trajectory. The modeling processes of these disciplines are introduced in brief in this paper and some of them are novel in the relevant area. The Bi-level system integrated optimization (BLSIO) approach is proposed as the MDO strategy, which optimize the trajectory discipline using the state variables of the other disciplines in each iteration process. With an aircraft climb mission being the optimize background, the MDO process is applied successfully and the optimal design obtained by the MDO process reduced the minimum climb time by 10.02% in comparison with the reference model. The analysis shows that the reduce of the minimum climb time is mainly realized by optimizing the configuration of the vehicle and adapting the attack angle in the climb process. The optimal configuration has larger lift-to-drag ratio but fewer fuel remaining after the mission than the reference model. This concludes that although the optimized model is not superior to the reference model in all aspects, it has an overall performance within the design space. Also, the BLSIO approach is ideal in solving the MDO problem of the wide speed range mission.

Theoretical Analysis on Sequential Multidisciplinary Design Optimization Architecture

Abstract In the past few decades, multidisciplinary design optimization (MDO) has become a very important research topic along with the increase of the system complexity. In an MDO problem, it is very typical that multiple disciplines are involved, making the problem coupled and complex. Monolithic and distributed architectures have been proposed for solving MDO problems. However, efficient architectures are still needed. In the prior work, a sequential multidisciplinary design optimization (S-MDO) architecture was proposed that has a distributed structure that decomposes the original MDO problem into several subproblems. However, in the original S-MDO work, the theoretical behaviors were not analyzed because its mathematical representations were not clear. In this article, we present a clear mathematical representation of the S-MDO architecture and conduct theoretical analysis on the S-MDO architecture to explain its performance in solving MDO problems. The optimality condition of the S-MDO architecture is derived and summarized as a theorem and a proposition. To demonstrate the general formulation of solving an MDO problem using the S-MDO architecture and validate the correctness of the optimality condition, we use it to obtain the Pareto frontier of a benchmark MDO problem. From the spread of the obtained Pareto frontier, we can conclude that the S-MDO architecture performs well, as long as the global optimum of each disciplinary subproblem can be found.

A relative adequacy framework for multimodel management in multidisciplinary design optimization

In previous work, we presented a novel relative adequacy framework to manage the employment of a set of available computational models in (single-disciplinary) design optimization problems. In this paper, we extend our method to solve multidisciplinary design optimization problems with particular emphasis on strongly coupled fluid-structure interactions. We illustrate that these interactions can have a significant impact on multimodel management: models that may be selected in a single-disciplinary analysis context can be inadequate in a multidisciplinary analysis one. We implement our method for two multidisciplinary design optimization architectures: the monolithic multidisciplinary feasible formulation and a penalty-based distributed interdisciplinary feasible formulation. We illustrate the proposed multimodel management methodology by means of two example problems: a flexible beam fluid-structure interaction problem and a transonic fan flow problem. The obtained results demonstrate that our framework is accurate and efficient while exhibiting significant computational cost benefits, especially when disciplinary coupling is tight.

System Analyzer for a Bioinspired Mars Flight Vehicle System for Varying Mission Contexts.

The Marsbee is a novel bioinspired flapping flight vehicle concept for aerial Mars exploration. The Marsbee design addresses the challenges of flying on Mars by mimicking the unsteady lift generation mechanisms seen in terrestrial insects To enable the comparison of the Marsbee system to other flying Mars exploration concepts, a study was performed that employs a Multidisciplinary Design Optimization architecture to analyze and optimize the Marsbee system to suit a wide variety of missions. This study developed an analyzer for a Multidisciplinary Design Feasible (MDF) architecture, as well as explored the design space and attributes necessary in an objective function for Mars flying system missions. The analyzer is based on physical models developed in previous studies. Its functionality was demonstrated by analyzing 100,000 randomly generated designs, with design variables close to a prototype Marsbee tested in Martian density conditions. These results show that by using flexible wings rather than rigid wings the maximum flight times increased from 53 minutes to 114 minutes, and the maximum payload masses increased from 28 grams to 61 grams. These are competing effects and cannot be maximized simultaneously. The results of this study will be used to determine the optimal Marsbee system.

Efficient Aircraft Multidisciplinary Design Optimization and Sensitivity Analysis via Signomial Programming

This paper proposes a new methodology for physics-based aircraft multidisciplinary design optimization (MDO) and sensitivity analysis. The proposed architecture uses signomial programming (SP), a type of difference-of-convex optimization that is solved iteratively as a series of log-convex problems. A requirement of SP is that all constraints and objective functions must have explicit signomial formulas. The SP MDO architecture facilitates the low-cost computation of optimal sensitivities through Lagrange duality. The specific example of commercial aircraft MDO is considered. Using SP, a small-, medium-, and large-scale benchmark problem is solved 16, 39, and 26 times faster, respectively, than Transport Aircraft System Optimization (TASOPT), a comparable and widely used aircraft MDO tool. The SP solution times include computation of all optimal parameter and constraint sensitivities, a feature unique to the presented architecture. The reliability of SP is demonstrated by converging a commercial aircraft MDO problem for a number of different objective functions and evaluating both traditional and nontraditional aircraft configurations. While the presented example is commercial aircraft MDO, the SP MDO architecture is applicable to a range of engineering optimization problems.

Performance Evaluation of MDO Architectures within a Variable Complexity Problem

Though quite a number of multidisciplinary design optimization (MDO) architectures have been proposed for the optimal design of large-scale multidisciplinary systems, how their performance changes with the complexity of MDO problem varied is not well studied. In order to solve this problem, this paper presents a variable complexity problem which allows people to obtain a MDO problem with arbitrary complexity by specifying its changeable parameters, such as the number of disciplines and the numbers of design variables. Then four investigations are performed to evaluate how the performance of different MDO architectures changes with the number of disciplines, global variables, local variables, and coupling variables varied, respectively. Finally, the results supply guidance for the selection of MDO architectures in solving practical engineering problems with different complexity.

Multidisciplinary design optimization architecture to concurrent design of satellite systems

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.

A parallel bi-level multidisciplinary design optimization architecture with convergence proof for general problem

ABSTRACTQuite a number of distributed Multidisciplinary Design Optimization (MDO) architectures have been proposed for the optimal design of large-scale multidisciplinary systems. However, just a few of them have available numerical convergence proof. In this article, a parallel bi-level MDO architecture is presented to solve the general MDO problem with shared constraints and a shared objective. The presented architecture decomposes the original MDO problem into one implicit nonlinear equation and multiple concurrent sub-optimization problems, then solves them through a bi-level process. In particular, this architecture allows each sub-optimization problem to be solved in parallel and its solution is proven to converge to the Karush–Kuhn–Tucker (KKT) point of the original MDO problem. Finally, two MDO problems are introduced to perform a comprehensive evaluation and verification of the presented architecture and the results demonstrate that it has a good performance both in convergence and efficiency.

Spacecraft mission design optimization under uncertainty

The design of space systems is a complex and multidisciplinary process. In this study, two deterministic and nondeterministic approaches are applied to the system design optimization of a spacecraft which is actually a small satellite in low Earth orbit with remote sensing mission. These approaches were then evaluated and compared. Different disciplines such as mission analysis, payload, electrical power supply, mass model, and launch manifest were properly combined for further use. Furthermore, genetic algorithm and sequential quadratic programming were employed as the system-level and local-level optimizers. The main optimization objective of this study is to minimize the resolution of the satellite imaging payload while there are several constraints. A probabilistic analysis was performed to compare the results of the deterministic and nondeterministic approaches. Analysis of the results showed that the deterministic approaches may lead to an unreliable design (because of leaving little or no room for uncertainties), while using the reliability-based multidisciplinary design optimization architecture, all probabilistic constraints were satisfied.

Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes

While numerous architectures exist for solving multidisciplinary design optimization (MDO) problems, there is currently no standard way of describing these architectures. In particular, a standard visual representation of the solution process would be particularly useful as a communication medium among practitioners and those new to the field. This paper presents the extended design structure matrix (XDSM), a new diagram for visualizing MDO processes. The diagram is based on extending the standard design structure matrix (DSM) to simultaneously show data dependency and process flow on a single diagram. Modifications include adding special components to define iterative processes, defining different line styles to show data and process connections independently, and adding a numbering scheme to define the order in which the components are executed. This paper describes the rules for constructing XDSMs along with many examples, including diagrams of several MDO architectures. Finally, this paper discusses potential applications of the XDSM in other areas of MDO and the future development of the diagrams.

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.