Multidisciplinary Design Optimization of Aero-craft Shapes by Using Grid Based High Performance Computational Framework1 Hong Liu 1, Xi-li Sun 2, Qian-ni Deng 2, Xin-da Lu 2 1
Department of Engineering Mechanics, Shanghai Jiao Tong University 2 Department of Computer Science, Shanghai Jiao Tong University, 200030, Shanghai , P. R. China
[email protected]
Abstract. This paper presents a novel high performance computational (HPC) framework for multidisciplinary design optimization (MDO) of aero-craft shapes by using “Grid computing” technology. In this HPC framework, MDO computation is completed by using a genetic algorithm based optimization code and some performance evaluation codes. All those codes in the HPC system are named as “Grid Services” independently. The HPC framework can run on various computers with various platforms, through which computer resources are provided to the designers in the form of services, no matter where those computers are located. Based on this HPC framework the MDO process can be performed interactively. To demonstrate the ability of this HPC framework, a conceptual aero-craft optimization case was described.
1 Introduction Multidisciplinary Design Optimization (MDO) is highly required in the development of novel high-performance aero-crafts, including flight performance, aerodynamic performance etc. Therefore, for many years MDO of aero-crafts has been a significant challenge for scientists and designers. MDO is a methodology for design and analysis of complex engineering systems and subsystems that coherently exploit the synergism of mutually interacting phenomena [1]. Many research works also have shown that MDO is a systematic methodology and focuses on the interactive relationship between many disciplines for a system [2],[3],[4]. Generally, Multidisciplinary Design Optimization of a complex system, especially an aero-craft system, usually involves many scientists from various disciplines. The design results from each discipline are finally grouped into a single designer-in-chief. Therefore aerospace design methods based on a designer-in-chief have been widely adopted in the period of conceptual design of a flight vehicle. As time goes by, the requirement for a complex system is increased. MDO problems become more com1
This research is supported by the National Natural Science Foundation of China (Grant number: 90205006) and Shanghai Rising Star Program (Grant number: O2QG14031).
plex. Advances in disciplinary analysis in resent years have made those problems worse and those analysis model restricted to simple problems with very approximate approaches can not been used again. As those analysis codes for MDO of flight vehicles have grown larger and larger, it is indeed too incomprehensible and difficult for a designer-in-chief to maintain, since few know clearly what is included in the code and few can explain clearly the results from those codes. Therefore, the role of disciplinary scientist increases and it becomes more difficult for a designer-in-chief to manage the design process. To complete the design process smoothly, the designerin-chief must joint all specialists in a collaborative optimization process. Thus, a need exists, not simply to increase the speed of those complex analyses, but rather to simultaneously improve optimization performance and reduce complexity [2]. However, high performance computing based on parallel computing technology can only increase the speed of those analysis codes, one will wonder whether there is a high performance system that can lessen the complexity or improve optimization performance for collaborative works. As the Grid technology developed, there appears a new way to solve the problem. Recently, significant progress has been made on the computational grid technology. The goal of the Grid computing is to share computing resources globally. The problem underlies of Grid concept is coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. In Grid computing environment, the computing resources are provided as Grid service. Then, we can construct a Grid based HPC framework, to improve the performance of the MDO algorithms and gain many new characteristics that is impossible in traditional means for MDO of a large aero-craft system. This grid based HPC framework for MDO can make the design process be easily controlled and be conducted interactively, and through this computing Grid system many specialists in various disciplines can be easily managed in one MDO group. In the following section, the HPC framework based on Grid technology will be described in details.
2 Grid Based HPC Framework for MDO
2.1 Procedure for MDO of Aero-crafts Procedure for Multidisciplinary Design Optimization of aero-crafts by using genetic algorithms (GA) can be shown from figure 1. The basic idea of the GA is to simulate the evolution of the nature to find an optimized solution for a given problem or a set of Pareto optimal solution for a MDO problem. When conduct Optimization of the aero-craft shapes using GA, we will follow the steps listed below. Firstly, set the range of a set of given parameters that we called designing parameters. Secondly, use a random algorithm to select a specific value for each parameter from the given range. Thirdly, use the analysis code, such as CFD module and CFM module to compute each child task and get the result set. Fourthly, use a comparison algorithm to select the best result. Fifthly, use the best result as the “seeds” to generate the next generation.
The Scope for Design Variables of Conceptual Aero-craft Shapes Geometry Model & Grid Generation of Aero-craft Individual Design Requirements of Various Disci-
Aerodynamics (Fast CFD)
Flight Stability (CFM)
Other Design Requirements
Multidisciplinary Design Optimization for Individual Configurations New Configuration & New Grid Stop Judgment
Continue
Optimized Configuration Fig. 1. Flowchart of the Optimization Procedure.
2.2 Grid based HPC Framework It is easy to see that the GA has parallel computation nature. Each child task can be allocated to a single CPU and executed independently. Traditionally, we can use a Parallel Virtual Machine (PVM) or Message Passing Interface (MPI) to finish this work on a supercomputer with many CPUs. However, high performance computing based on PVM or MPI can only increase the speed of those analysis codes. By using the grid computing technology, a novel high performance framework that can lessen the complexity or improve optimization performance for collaborative works is proposed. How the system has been constructed will be briefly described. The Grid based HPC framework for MDO is composed of many Grid services. Each Grid service is built on the web and grid technology. Each service has some standard interfaces to support the registering service, the querying service, the communication and the interacting between the client and the service provider. The Grid Service features of the HPC framework will be discussed firstly. Secondly all the components of the HPC framework will be discussed.
Grid Service A Grid service is a web service that conforms to a set of conventions that define how a client interacts with a Grid service [5]. General features of the Grid service are: 1. Every Grid service is a web service, whose public interfaces and bindings are defined and described using XML. Then, these systems may interact with the web service in a manner prescribed by its definition, using XML based messages conveyed by internet protocols [6]. 2. The Grid service has a set of predefined interfaces, such as Grid Service, Registry, Factory, Handle Map, Primary Key etc [5]. 3. Each Grid service has the responsibility to maintain the local policy, which means the Grid service can decide what kind services it will provide and what kind of resources can be shared. Components of the HPC System Designer
UDDI Center
Analysis Services (CFD,CFM code)
UserService Allocate Service
Analysis Services (CFD,CFM code)
Select Service
(CFD,CFM code)
Analysis Services Cooperatives
Fig. 2. System Framework.
The system is composed of three modules as shown in figure 2. The first part is User service module. From figure 2, the executor and the co-designer are the specific users of this system. The user can submit the tasks, monitor the computing progress and get the middle and final results via the web pages. When received the requests from the users, the User service can query the UDDI center to find what kinds of services available now. The User service can supports the user accounts management, supports the task submitting and parameter adjusting. It can interact with other services to finish the computing work. The second part is the UDDI center module. The responsibility of this module is acting as an information exchange center. The third part is the Application services, including Allocate service, Select service and Analysis service including CFD service, CFM service and other Analysis services. The Allocate service is a service to control process. It accepts the requests from the User service. Then the control process will find the available Select and Analysis computing services. Then the connections are built among the Allocate service, Select
service and Analysis services. The parameters are conveyed to the Select service to generate the specific child tasks distributed to the analysis computing services. The Analysis computing service is the computing part of this HPC framework. The executable codes are named as solvers. In this paper, CFD, CFM and other solvers are adopted in the analysis module for Grid service. CFD (computational fluid dynamics) code is a 3-D explicit flow solver in which three-dimensional Navier-Stokes equations are solved by employing a central difference and artificial dissipation finite volume scheme [7]. CFM (computational flight mechanics) code is a code developed to analyze the stability of aircraft or aerospace craft In order to run all those analysis codes, an automatic grid generation code is required. In the analysis service, we adopt our 3-D overlapping (Chimera) grid generation code [8] to generate computational grids for CFD and CFM solver. After the results are returned, the Select service is called and selects the best one from them.
3 Application on Aero-craft Shape Optimization Design
3.1 Hardware Architecture To construct the basic hardware environment for the Grid system, we connect most of computer resources in our university, including a SGI Onyx3800 (64CPUs, 64 Gflops) supercomputer, four set of IBM-1350 Clusters (86 Gflops), Sun E-450 & Series Ultra workstations and many PC connected to our Campus network. The maximum speed of data-flow of our campus network between those computers can reach 1 Gb/s. The connection work for our final computing environment is in construction. 3.2 Demonstration of the Web Service in the Grid System The Grid based HPC system finally works well by connecting users and the Grid services. If the users want to start a new design, they only need submit their tasks through the web. To complete a MDO task, the designer-in-chief also can communicate with other designers from various disciplines through the web. There is a billboard on the web page. The task executor and the co-designers exchange information by it. When the co-designer finds some parameters are not suitable for the task, he leaves messages on the billboard. The task executer can adjust the parameters. Web services will help any designers complete their whole designing tasks mutually and interactively. When an authenticated user submits a task, the tasks control process will allocate a sequence number for that task and generate a record in the database. All the information of the task including the parameters, the results and the interaction messages between the executor and co-designers can be record in the database as needed. After the user’s identity has been identified, users may query how many analysis services are available in the UDDI center. Then the user may set the task’s initial parameters,
send the parameters and the compute task to the User service. The User service then looks up the Allocate service and Select service in the UDDI center and then uses the connection model to build connections with them.
(A)
(B)
Fig. 1. The demonstration of the Web services of the optimization Grid system, (A) is the First Web Page for Register and (B) is the Second Web Page for Task Running.
Figure 3 illustrates the first and second web page entering the Grid system and running an optimization design task. After the results are returned to the User service, the User service puts that information on the user’s browser, so the user can monitor the design process dynamically. It is clearly that what a designer to do is obtain all his requirements from a special Web. 3.3 Application of the Grid System for Aero-craft Shape Optimization As a demonstration case for the Grid system, a conceptual aero-craft deign is conducted. In the aero-craft conceptual design process, we only consider two basic performances, including aerodynamic high ratio of lift to drag and flight stability. The performances can be computed by CFD solver and CFM solver respectively. To complete this optimization design, we define an objective function is a function of L/D (ratio of lift to drag) maximization, and set the flight stability as constrain to the optimization problems. For this aero-craft conceptual design, there are 11 design variables to be optimized. The size of population, the mutation rate and crossover rate are the basic control parameters to the GA, which can be changed interactively from the web page of the Grid system. Then we can obtain results, as shown from figure 4 and figure 5. Figure 4 shows all of the optimization process and illustrates that with the generation increasing, Lift/Drag Ratio and the fitness of the evaluation is become larger. Figure 5 is the aero-craft shape of Generation 500 and Generation 3000. As the generation increased, more optimized aero-craft shape can be obtained before the optimal is reached. The designer-in-chief also can joint two scientists from aerodynamics and
flight mechanics. They can exchange their advices through the web of the Grid System.
L/D Ratio
Fittness & L/D Ratio With Generation 120
Fittness
100
L/D Ratio
80 60 40 20 0 0
500
1000
1500
2000
2500
3000
3500
4000
Generation
Fig. 2. The Optimization Process: Lift/Drag Ratio with the generation increasing.
(3D) 22 Jul 2001
(3D) 07 Dec 2001
Y X
X
Y Z Z
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(B)
Fig. 5. The aero-craft shape of Generation 500 and Generation 3000, (A) is the shape for 500 generation and (B) is the shape for 3000 generation.
4 Conclusion A Grid based HPC framework for MDO of aero-crafts is described and by using this computing grid an application case for a conceptual aero-craft design is completed. The highlights of this HPC framework were clearly shown. Firstly, it presents a new framework for the applications of MDO of aero-crafts, which can utilize the computing power over a wide range, and in which the number of analysis service is dynami-
cally changed. Secondly, it is a great improvement in the optimization design method for the aero-craft shapes. By using this HPC system, designers can control the parameters during the progress, and the designer and the co-designers can exchange their points of view about the designing. This enables scientists from various disciplines to complete collaborative design work interactively all over the world.
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