Multidisciplinary Design Optimization of Aerospace Vehicle – Single Engine Rotorcraft

Aerospace vehicle design includes several disciplines with often-conflicting requirements. A formal system design framework is developed in this research where the designer coordinates with disciplinary experts to find an overall optimized design while simultaneously optimizing disciplinary objectives. The overall system objective function chosen in the preliminary design is minimum production cost for a light turbine-training helicopter. Several disciplinary objectives including specific fuel consumption for propulsion, empty weight for weights group, and figure of merit for aerodynamics group are optimized. In addition to disciplinary optimization, several analyses are performed including vehicle engineering, dynamic analysis, stability and control, transmission design, and noise analysis. The design loop starts from the conceptual stage where the initial sizing of the helicopter is done based on mission requirements. The initial sizing information is then passed to disciplinary experts for preliminary design. The design loop is iterated several times using multidisciplinary design techniques like All At Once (AAO) and Collaborative Optimization (CO) approaches. A light training helicopter is proposed that satisfies all the mission requirements, is optimized for several disciplines and has minimum production cost. Merits, demerits, requirements and limitation of the proposed methodology are discussed. INTRODUCTION The multidisciplinary nature of helicopter makes it hard for the preliminary designer to estimate the actual cost of the aircraft in the early design stage. There has been a lot of emphasis on bringing more and more design information early in the design stage [1, 2, 3, and 4]. In this research, a variety of disciplinary design analyses are performed at the preliminary helicopter design stage where the design cycle is repeated in an iterative fashion using Multidisciplinary Design Optimization (MDO) to ensure an overall optimized design. All At Once (AAO) and Collaborative Optimization (CO) techniques developed by Kroo, Sobieski, Braun et al [5, 6, and 7] are used. These methods help integrate various design disciplines while removing their interdependency. AAO approach solves the problem by removing disciplinary optimizers and introducing a multi-objective criterion for the system level optimizer. CO allows simultaneous use of disciplinary optimizers thus ensuring that not only an overall optimized design is obtained, but also the design is best from disciplinary point of view. A light turbine-training helicopter is used as baseline for analysis. The requirements come from the Request for Proposal (RFP) from AHS 2006 student design competition. The helicopter is expected to lift two 90 kg people, 20 kg of miscellaneous equipment, and enough fuel to Hover Out of Ground Effect (HOGE) for 2 hours, into HOGE at 6,000 ft. on an ISA + 20C. The winning design team at Georgia Tech made use of a variety of software packages and codes for different disciplines to perform the analyses. The idea is to perform the required mission with minimum cost. A variety of software packages and codes for different disciplines are integrated in this research to perform analyses. In a traditional design approaches, due to strong dependency of disciplines on each other, it is not possible to run several analyses in parallel and therefore the design process is slowed down significantly. Infact, in several cases, by the end of the design, it is not possible to complete even one design loop involving all analyses simultaneously. This process does not guarantee overall system cost minimization. In this research, a platform is developed where all the disciplinary codes, software and analyses are integrated and several design loops are performed. The overall system design criterion is chosen to be the minimum production cost. All the disciplinary and the system level design constraints are satisfied. Optimized results obtained from AAO and CO approaches are compared. These methods, when fully converged, ensure an overall optimized design with several disciplines involved. A data repository is created where design information is stored iteratively. As the design matures, new information is obtained from disciplines and subsequent analyses are performed. A parallel design helps reduce the design time from several months to a few hours. The entire design process is automated in this research. There are no feedbacks or feed forward loops between different disciplines and all the disciplinary optimizers are retained. The weight optimizer minimizes the empty weight of the helicopter, the propulsion optimizer minimizes the fuel consumption and emission, and the aerodynamics optimizer maximizes the hover Figure of Merit. All the other disciplines are of analysis type. The initial problem setup is time consuming and tedious but after the problem is setup and software packages are integrated, information flow becomes very efficient. ModelCenter is used as a platform for information transfer and system level optimization. Individual wrappers are written for data transfer between ModelCenter and disciplinary codes. These codes include commercial software, legacy codes and customized in house programs. Data is transferred in batch mode for rapid analysis. The design team estimated the average vehicle cost, based on the production of 3000 units, to be $203,541.85 per unit. This estimate was based on sequential disciplinary analyses. The results obtained from MDO environment with parallel execution show a reduction in the average unit cost to $178,175.69. This is a 12.46% reduction in cost while all the constraints and RFP mission requirements are satisfied. This study demonstrates an automated framework for preliminary helicopter design. The framework ensures that detailed disciplinary analyses are performed in an automated manner in parallel at the initial design stage while at the same time overall disciplinary and system level optimized results are obtained. The platform also allows the designer to add further detailed disciplinary analyses so that the overall fidelity of the system can be improved. INTRODUCTION The generic Integrated Product and Process Development (IPPD) methodology allows the engineers and program managers to decompose the product and process design iterations [5]. The product development cycle of IPPD methodology is further divided into the conceptual and preliminary design loops. In this research, disciplinary analyses are identified, linked with each other through a common platform and preliminary design loop iteration is performed in an automated fashion. This approach provides an opportunity to perform system optimization using MDO techniques. Several requirements exit for a framework to provide an easy to use and robust MDO environment. Key attributes for the MDO environment, as listed by Sobieszczanski Sobieski [3], are computer speed, computer agility, task decomposition, sensitivity analysis, human interface and data transmission. The framework requirements for MDO application development have been outlined in the work of Salas and Townsend [6] as architectural design, problem formulation, problem execution, and access to information. Sobieski [3] indicates that among several tools that specialize in process integration and exploration, ModelCenter from Phoenix Integration Inc. focuses on product modeling and ease of tools and process integration across distribution and heterogeneous computing environments. ModelCenter is used in this research because of its flexibility to incorporate several existing commercial packages e.g. Excel, Matlab, MathCad, CATIA etc. ModelCenter also facilitates the use of wrappers to integrate in-house legacy codes. Two MDO approaches, i.e. All At Once (AAO) and Collaborative Optimization (CO) are used in this research to resolve the conflicting objective functions of different disciplines and the system level optimizer. AAO approach dictates that all the local design variables and constraints are moved to the system level. The local disciplinary optimizers are eliminated. The problem is reduced to a single level scheme with just one system optimizer. AAO approach with the capability of parallel execution of disciplines is depicted in Figure 1. With this approach, the disciplines become independent of each other. This approach facilitates the information flow from the repository to the respective disciplines and back to the repository in an automated fashion. Figure 1. AAO Approach with a Central Data Repository When the feedback loops are removed, compatibility constraints are added to the system level optimizer. The generic system level problem is defined as follows. Objective Function: Minimize F Subject To: B A g g g , , Variables: B A X X X , , Where F = Overall Evaluation Criterion (OEC) g = System level constraints B A g g , = Compatibility constraints 0 2 ' ≤ − − = ε B B g A 0 2 ' ≤ − − = ε A A g B X = System level variables A