CSER 2010
8th Conference on Systems Engineering Research March 17-19, 2010, Hoboken, NJ
Incorporating Multidisciplinary Design Optimization into Spacecraft Systems Engineering Jian Guo Chair of Space Systems Engineering Faculty of Aerospace Engineering Delft University of Technology Kluyverweg 1, 2629 HS, Delft, The Netherlands
[email protected]
Engineering (SE) and Multidisciplinary Design Optimization (MDO) could be used. SE and MDO share some features, for example the interdisciplinarity. However, up to the present time SE and MDO are still regarded as two different fields of spacecraft design. The former one focuses more on the technical or management process to ensure that the overall spacecraft meets the mission objectives. The latter one aims to search for an optimal spacecraft design scheme according to well structured multidisciplinary mathematical models. So far, significant work has been done in the field of spacecraft SE. Especially, some theories and approaches of SE have been well-reorganized and utilized by space industries worldwide. From another side, although much research on spacecraft MDO has been performed, this technology has been rarely used in the space industry. This paper provides a preliminary idea for the incorporation of MDO into spacecraft SE by identifying the connections between them. The paper consists of two main parts. The first part is an overview of the SE and the MDO technologies, with the emphasis on SE and MDO principles, models and tools for spacecraft design. The second part investigates the relationships between spacecraft SE and MDO. The difficulties and requirements for incorporating MDO into spacecraft SE are analyzed. Two promising technologies, i.e.
Abstract Systems Engineering (SE) and Multidisciplinary Design Optimization (MDO) are usually regarded as two different aspects of spacecraft design. This paper investigates the problem of merging these two fields for the improvement of the spacecraft system design. The SE and MDO processes are described with emphases on their principles, models and tools, followed by a detailed analysis of their relationships and the requirements for merging the two. Promising technologies, such as tradespace exploration and Knowledge-Based Engineering (KBE), are identified as possible connections between SE and MDO. Based on the analysis performed in the paper, a detailed scheme is presented that utilizes these techniques for the incorporation of MDO into spacecraft SE.
Introduction The development of spacecraft is a very complicated task involving both technical and management aspects such as cost, schedule, risk and reliability. A good design can be obtained only by considering all these aspects from a systems point of view. In order to reduce development cost, shorten development cycle, and meanwhile achieve high product quality, the technologies of Systems
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Multi-Attibute Tradespace Exploration (MATE) and Knowledge-Based Engineering (KBE), are discussed and identified as possible connections between SE and MDO. A proposal of utilizing these techniques for incorporating MDO into spacecraft SE is presented in detail, followed by conclusions.
Spacecraft Systems Engineering In this section, the general technology of SE will be introduced, followed by a discussion on spacecraft SE. Systems Engineering. There is no generally accepted definition of what SE is. In this paper, SE is regarded as an interdisciplinary field of engineering that deals with processes, models and tools to handle complex engineering projects. The SE process consists of a management process and a technical process. The former organizes the technical efforts in the lifecycle, while the latter includes assessing available information, defining effectiveness measures, creating the behaviour and structure models, performing trade-off analysis, and creating sequential build&test plan (Oliver et al.1997). A SE model can be in physical, analog, schematic, or mathematical form. Physical models look similar to what they represent, analogue models behave like the original, schematic models graphically describe a situation or process, and mathematical models symbolically represent the principles of a situation being studied (Blanchard et al.1998). SE encourages the use of tools to better manage the complexity in engineering systems. These tools focus either on the generic project management process or on the product design specific items in project planning. The former includes Project Objective Statement (POS), Work Flow Diagrams (WFD), Work Breakdown Structure (WBS), Gantt chart and so on. The latter consists of Data Flow Diagram (DFD), Functional Flow Block Diagram (FFBD), N2 chart, trade-off tools, design recording, etc (van Tooren et al. 2008).
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The appropriate tool is selected according to the requirement. For example, the N2 chart should be utilized if the interfaces between subsystems are concerned. Systems Engineering for Spacecraft Design. The overall SE process for spacecraft development can be considered as being broken down into several distinct phases. For example, European Space Agency (ESA) has defined the space program development procedure as seven phases, starting from mission analysis and needs identification and ending at disposal, NASA has a similar definition with six phases (Wertz et al. 1999). This breakdown for SE has become the standard framework in space industries worldwide. Throughout the overall spacecraft SE process, several SE tasks must be addressed, including: system requirements compliance, system-level specifications and budgets devolution, interface definition and control, budget management and maintenance, etc. An important SE technique in spacecraft development is the trade-off, which is used to evaluate alternative design options in conceptual studies. There are many evaluation criteria for such trade-offs, such as cost, satisfaction of performance requirements, technical heritage, reliability, etc. Some non-traditional criteria are also used recently, for example the flexibility or responsiveness. With the advancement of information technology, space organizations are now more and more focusing on utilizing Concurrent Engineering (CE) in spacecraft SE to reduce development times and evaluate more design options (Fortescue et al. 2003). CE is the application of classical SE in an integrated computer environment. Typical CE facilities for spacecraft development include the Project Design Center (PDC) at NASA/JPL (Smith 1998), the Concept Design Center (CDC) at the Aerospace Corp. (Aguilar et al. 2000), the Concurrent Design Facility (CDF) at ESA (Bandecchi et al. 2000), and others. Figure 1
shows the CE approach used by CDF, which re-organizes existing tools and human resources in a more effective way based on five elements: an iterative process, a multi-disciplinary team, an integrated design model, a facility, and a software/hardware infrastructure (Bandecchi et al. 2000).
Figure 1. The CE Approach of ESA/ESTEC CDF (Bandecchi et al. 2000).
Multidisciplinary Design Optimization of Spacecraft This section will discuss the MDO technique, which consists of two parts: an introduction of the general MDO and a brief review of spacecraft MDO. General MDO. MDO emerged as a new field of engineering in the 1980s. It is a methodology for the design of complex engineering systems that are governed by mutually interacting physical phenomena and made up of distinct interacting subsystems (Sobieszczanski-Sobieski 1993). MDO uses optimization methods to solve design problems incorporating a number of disciplines simultaneously. Because MDO explores the interactions between disciplines and emphasizes the synergism of the disciplines and subsystems, the optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially,. Therefore, under the MDO framework, the organization of the optimization problem is very similar to that of
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industries. This similarity makes MDO very suitable for industrial usage. Unlike traditional optimization method, MDO itself is also a multidisciplinary methodology. Several conceptual components coalesced to form MDO, which consist of design-oriented analysis (e.g. sensitivity analysis, inexpensive reanalysis, computational cost-accuracy trade, data management and visualization), approximation, system mathematical modeling, decomposition, design space search, optimization procedure, and human interface. For details about MDO, please refer to (Sobieszczanski-Sobieski 1993). Spacecraft MDO. There have been many attempts to use the MDO methodology or concept for spacecraft design. For example, (Budianto et al. 2000) proposed to design a space-based infrared satellite constellation using an innovative MDO architecture, the Collaborative Optimization (CO); (Cullimore et al. 2002) performed the MDO for a space-based telescope system, which includes the multidisciplinary coupling between optics, structural mechanics and thermo mechanics; (Jilla et al. 2004) investigated a multi-objective MDO methodology for mathematically modeling the conceptual design of a distributed satellite system; (Howell et al. 2004) applied existing MDO techniques (DOE, gradient based optimization, sensitivity analysis and heuristic techniques) to a spacecraft interferometer testbed in order to minimize the pointing error while keeping a minimum optical pathlength. Another popular field for spacecraft MDO is the framework development. Examples for this area include: the MIDAS (Multidisciplinary Integrated Design Assistant for Spacecraft) developed by NASA’s Jet Propulsion Laboratory (JPL) (George et al. 1995), the SSDSE (Spacecraft System Design and Simulation Environment) of Princeton Synergetics Inc. (Ferebee et al. 1997), the MuSSat (Modeling and Simulation of Satellite
Systems) developed by TU Munchen (Wilke et al. 2000), the SDIDE (Spacecraft Distributed Design Environment) of Tsinghua University (Guo et al. 2002), the SIDE (Satellite Integrated Design Environment) of the National University of Defense Technology (Zhao et al. 2003), the SYSTEMA developed by Astrium Satellites (Theroude et al. 2008), and so on. A spacecraft MDO framework should be a synthesis of Multidisciplinary Design (MD), Multidisciplinary Analysis (MA), and Multidisciplinary Optimization (MO) (Carty 2002). Figure 2 shows a typical spacecraft MDO framework, the SDIDE, which was developed by the author and his colleagues (Guo et al. 2002, Hu et al. 2003). This framework consists of multiple functional modules implementing the tasks of system modelling (System Setting Module), optimization procedure management (Central Task Control Module), design space search (System Optimization Module), data management (Central Data Management Module), human interface (System Data Monitoring Module), and design-oriented analysis (Discipline-level Concurrent Analysis/Design Network). It also can be found from Figure 2 that the architecture of the SDIDE is very similar to the organization of a spacecraft development team.
Relationship between Spacecraft SE and MDO The SE process is already a standard in space industry, while MDO is a relatively new concept. Therefore, MDO needs to be adapted to spacecraft SE. However the relationships between SE and MDO are not very clear, which has prevented the incorporation thus far. In this section, the relationships between SE and MDO are identified. Process vs. Solution. Spacecraft SE offers a set of tools to help structuring the spacecraft development process. These tools are elements of a set of logical definitions, diagrams and methods that support structuring, starting, executing and reporting for different lifecycle phases from market need identification to the end of in-orbit operation. Spacecraft SE focuses more on the process of designing a spacecraft than on the solutions to the design problem itself. When combined with generic project management techniques, SE can be seen as a standard for phasing a space project, providing tools for each phase, and standardizing a number of deliverables (van Tooren et al. 2008). In other words, SE supports the whole engineering process. The MDO technology is an extension of operation research from the operational phase of systems into the design phase of systems. The objective of MDO is to find the optimal solution of a design problem subject to technical, economical and other constraints. The MDO also functions as a process when solving a design problem. However, this process only aims to search the design space instead of phasing the project. Furthermore, so far, the applications of MDO in spacecraft design are limited to a certain project phase, e.g. feasibility study. From this point, the MDO focuses much more on the solution and has to be treated as a tool within the SE framework. Qualitative vs. Quantitative. The most important concept in spacecraft development is the requirement. In each project phase, a set of
Figure 2. A Typical Spacecraft MDO Framework.
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requirements are generated, then various design options are created and evaluated. In the end, the compliance of the solutions with the requirements is verified. In the SE framework, top-level requirements are usually expressed in a qualitative way. Most methods and tools for evaluations, for example Requirement Discovery Tree, Design Option Tree and even some trade-off methods, are also qualitative. Therefore, SE is more regarded as a qualitative methodology for spacecraft development although several quantitative concepts are involved. Spacecraft MDO is a computational framework consisting of the optimizer, the design of experiments tool, the analysis tools and well constructed mathematical models. The important element shared by them is a set of design variables that describes a spacecraft design. Given the initial values of the set of design variables, the MDO framework allows an automated design space search based on objectives and constraints, which are functions of design variables. The obtained best design is then checked against quantitative requirements. The MDO is a typical quantitative methodology because its strength is the capability to quantitatively search a design domain.
Challenges for Incorporation In order to incorporate MDO into spacecraft SE, the difficulties in applying MDO are to be identified, followed by an analysis of the requirements for incorporation. Difficulties of Applying MDO. Although significant work has been done in the field of MDO, this technology has not been well recognized by space industries. According to the author’s understanding, this is due to following difficulties: • Difficulty in integration –MDO was used in the very early stage of a design as a conceptual parameter optimization tool. However, integrating it into more detailed development stages is still a
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challenge, and the integration of disciplinary tools by information sharing is also a big issue. • Difficulty in modeling – the design of spacecraft is very much an experience-based work, and involves many qualitative specifications of requirements. Therefore, constructing these qualitative models and linking them with a quantitative MDO framework are the primary difficulties associated with modelling. In addition, the uncertainties of the models also need to be considered. • Difficulty in utilization – space industries have already accumulated a lot of resources, such as expertises, standards, databases and technical heritages. Utilizing these resources is the fundamental requirement for application of MDO to spacecraft development in an industrial environment. Requirements for Incorporation. The requirements for incorporating MDO into spacecraft SE are derived from both their relationships (discussed in the last section) and the difficulties of MDO (introduced in the last subsection). Generally speaking, to achieve incorporation, the framework needs to: • Allow the utilization of MDO throughout the overall SE process, i.e. different design phases, as a quantitative tool for automatic searching for the optimal design; • Allow the exchange, interpretation and utilization of quantitative or qualitative information in both the SE and the MDO processes; • Allow the identifying, capturing, structuring, formalizing and implementing of knowledge accumulated by space industries; and • Prevent major modifications to existing spacecraft SE (CE) frameworks. The development of spacecraft is a creative activity, which requires a thorough
understanding of a human’s role in the SE process and how this can be partially taken over by incorporating the MDO technology. Therefore, the resulting framework should not be an attempt to completely replace the human being and automate the overall process, but should be a supporting tool for engineers throughout the development procedure.
components, their fabrication, testing and maintenance requirements. Similarly, a knowledge model of a process is a specification of the sequence of process stages. Unlike traditional KBS, KBE is a sophisticated blend of Artificial Intelligence (AI), CAD and Object-Oriented modeling. A typical KBE system provides: • A programming environment to code the experts’ knowledge about the design of a product, i.e. how the product is defined, and the process of generating a product by the systematic application of logical rules and various algorithms and procedures; • A browsing interface to visualize the geometry of the product and make queries about its geometric and non-geometric attributes, e.g. size, mass, cost, etc.
Technologies for Incorporation Considering the requirements listed in the last section, several technologies can be helpful to successfully incorporate MDO into the SE process. Two of them are introduced here. Knowledge Based Engineering. Knowledge Based Engineering (KBE) is an engineering discipline based on Computer-Aided Design (CAD) and Knowledge Based System (KBS) such as expert systems. An important element of KBE is the electronic knowledge models, which are actually computer interpretable models of a process or product. These knowledge models can be imported in and/or stored in specific engineering applications that enable engineers to specify requirements, create processes, or create designs on the basis of the knowledge in such models. For example, knowledge about a space system includes that a space system consists of a space segment and a ground segment, whereas the space segment consists of a payload and a platform. Then, a part of a knowledge model about a space system will consist of following expressions of knowledge facts: • The space system shall have as part a space segment; • The space system shall have as part a ground segment; • The space segment shall have as part a payload; • The space segment shall have as part a platform. Such a knowledge model will be further extended with knowledge and specifications about the properties of subsystems and
Figure 3. The Design and Engineering Engine (van Tooren et al. 2008).
A typical KBE system, called Design and Engineering Engine (DEE), is shown in Figure 3 (van Tooren et al. 2008). In the DEE, requirements are translated into objective functions and constraints, which are based on the parameters and variables in the High Level Primitives (HLPs). These HLPs are used to represent the newly created knowledge and solutions in the design process. The Initiator is in charge of initializing the parameters of HPLs
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contained in the input file. The core unit of the DEE, i.e. the MultiModel Generator (MMG), provides support to the definition of product models based on HLPs, to the creation of multiple views on the product model, and to the determination of the system behaviour using expert tools. The Converger checks whether results from the expert tools are valid. The Evaluator evaluates each design option analyzed with the expert tools for its fit with the requirements and, if the fit is not sufficient, will control the search in the solution domain. Thus, KBE offers engineers a technology to capture and reuse product and process multidisciplinary knowledge in an integrated way. Multi-Attribute Trade-space Exploration. Another potential technology is called Multi-Attribute Trade-space Exploration with Concurrent Design (MATE-CON), which is a conceptual design methodology that applies decision theory to SE (Ross et al. 2004). The purpose of MATE-CON is to capture decision maker references and use them to generate and evaluate a multitude of system designs. Therefore, MATE-CON provides a systematical approach for developing technical, political, market, and budgetary uncertainty analyses of a proposed system.
Figure 4. MATE-CON Process (Ross et al. 2004). As shown in Figure 4 (Ross et al. 2004), MATE-CON starts with a set of needs and references from decision makers on a system. Then the reference space is created using Multi-Attribute Utility Theory (MAUT),
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which maps references for an attribute into a normal value-under-uncertainty function, known as utility, and quantifies how a decision maker assesses different attributes with respect to each other. Once a reference space is created, the designer will develop the architecture-level tradespace through the creation of architecture concepts. Each concept is represented by a set of design variables such as orbital parameters or power subsystem types. These design variables are derived from engineering expertise and experience and organized by means of Quality Function Deployment (QFD). Hundreds or thousands of architectures are evaluated by calculating their attribute values and subsequently their utility values and costs. After that, selected architectures will be presented to design-level for higher fidelity decision making. Meanwhile, lessons learned during the concurrent procedure are fed back to improve the models used at the architecture-level. This explicit connection between broad architecture-level analysis and evaluation and more detailed design-level analysis and evaluation is the primary characteristic of the MATE-CON process. Applicability of KBE and MATE. KBE has been successfully utilized in the MDO context and shows a strong capability to integrate product knowledge (expert tools) into existing MDO frameworks. It provides an approach to link accumulated expertises and qualitative information with quantitative spacecraft MDO technology. Additionally, MATE has been successfully demonstrated in the spacecraft SE context. Results show that MATE can provide quantitative evaluation and support for design decision making in the early stages of system development. Meanwhile, it also incorporates experts’ opinions and diverse stakeholders’ interests, which are often in qualitative form. Therefore, both KBE and MATE can be regarded as the bridge between knowledge (usually qualitative) and data (quantitative), and eventually between MDO and SE.
Scheme for Incorporation Based on the statements in the previous sections, a scheme for incorporating MDO into spacecraft SE is presented in Figure 5. This proposal does not intend to describe every step in the SE process, but provides a schematic view on when and where to incorporate MDO into it.
Figure 5. SE Process with Integrated MDO. The philosophy of the proposed framework is: The MDO is regarded as an integrated tool within the SE context and performs the responsibility of quantitatively scanning the design domain. As shown in Figure 5, the framework covers Phase A (Feasibility Study) and Phase B (Detailed Definition) of the spacecraft development procedure. The part for
Phase A is a modified version of the MATE-CON process, where the Architecture Exploration and the Architecture Evaluation are replaced by a KBE process. Here a large Concepts Tradespace is constructed, and low fidelity knowledge models are provided for Concepts Evaluation and Trade-off based on KBE. In order to increase the involvement of human-in-loop, multiple mission concepts are provided after the trade-off instead of only one. Then, knowledge base and designers’ interaction are both used for Concepts Assessment to identify the Baseline Concept. Knowledge gained during this procedure is flowed back to the knowledge base to improve the fidelity of the knowledge model. The Baseline Concept is used as the input of the second part of the framework, i.e. corresponding to Phase B. The Design Vector is then identified based on this baseline concept with the support from both the knowledge base and the designers’ expertise, followed by the construction of the Design Space. In this stage, another KBE procedure will be implemented for System/Subsystem Evaluation and Trade-off, where high fidelity design models are created from the detailed knowledge. Eventually an Optimal Design is obtained after iteration, which will be further developed in Phase C/D. Compared to the traditional spacecraft SE process, the proposed framework combines MDO’s strength on quantitatively exploring the design domain and SE’s capability of qualitatively synthesizing knowledge. The utilization of KBE links quantitative MDO with qualitative SE, and the MATE provides a quantitative approach in the SE context.
Conclusions This paper presents an idea of incorporating the promising MDO technology into the relatively mature SE process for spacecraft development. The principles, models and tools of both the SE process and the MDO technology are reviewed, respectively.
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The relationships between spacecraft SE and MDO are analyzed, and the difficulties and the requirements for incorporating MDO into SE are also identified. Two possibly useful technologies to accomplish this, i.e. KBE and MATE, are suggested by the author. A conceptual framework synthesizing MDO and SE is proposed, which utilizes MDO (KBE) as a computational tool within the SE context.
Acknowledgement The author would like to express his appreciation to Prof.dr. Eberhard Gill and Prof.dr. Michel van Tooren, both in the Faculty of Aerospace Engineering, Delft University of Technology, for their invaluable inputs and suggestions during writing this paper. The author also would like to acknowledge Ir. Daan Maessen in TU Delft for proof-reading.
References Aguilar, J.A. and Dawdy, A., “Scope vs. Detail: the Teams of the Concept Design Center.” Proceedigns of IEEE Aerospace Conference 2000, Big Sky, MT, March 18-25, 2000. Bandecchi, M. and Melton, B. and et al., “The ESA/ESTEC Concurrent Design Facility.” Proceedings of the 2nd European Systems Engineering Conference, München, 2000. Blanchard, B.S. and Fabrycky, W.J., Systems Engineering and Analysis, 3rd Edition, Prentice Hall, 1998. Budianto, I. and Olds, J., “A Collaborative Optimization Approach to Design and Deployment of a Space Based Infrared System Constellation.” Proceedings of IEEE Aerospace Conference 2000, Big Sky, MT, March 18-25, 2000. Carty, A., “An Approach to Multidisciplinary Design, Analysis & Optimization for Rapid Conceptual Design.” Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GA, Sep 4-6, 2002.
322
Cullimore B, Panczak T, Baumann J, et al, “Automated Multidisciplinary Optimization of a Space-based Telescope”, Proceedings of 2002 SAE International Conference On Environmental Systems, San Antonio, TX, July, 2002. Ferebee, M.J. and Troutman, P.A. and Monell, D.W., “Satellite Systems Design/Simulation Environment: A Systems Approach to Pre-Phase A Design”, Proceedings of the 34th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, January 6-9, 1997. Fortescue, P. and Stark, J. and Swinerd, G., Spacecraft Systems Engineering, 3rd Edition, John Wiley & Sons Ltd., West Sussex, 2003. George, J. and Peterson, J. and Southard, S., “Multidisciplinary Integrated Design Assistant for Spacecraft (MIDAS)”, AIAA-95-1372-CP, 1995. Guo, J. and You, Z. and Ren, D.H., “A Distributed Multidisciplinary Design Optimization Architecture for Spacecraft Design.” Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GA, Sep 4-6, 2002. Howell, D. and de Weck, O., “Multidisciplinary System Optimization of a Spacecraft Interferometer Testbed.” Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY, August 30 - September 1, 2004. Hu, Z.Y. and Guo, J., et al., “A New System Engineering Method for Spacecraft Design.” Proceedings of the Space 2003 Conference, Long Beach, CA, Sep 23-25, 2003. Jilla, C.D. and Miller, D.W., “Multi-Objective, Multidisciplinary Design Optimization Methodology for Distributed Satellite Systems”, Journal of Spacecraft and Rocket, Vol. 41, No. 1, 2004, pp. 39-50.
Oliver, D.W. and Kelliher, T.P. and Keegan, J.G., Engineering Complex Systems with Models and Objects, McGraw-Hill, New York, 1997. Ross, A.M. and Hastings, D.E. and Warmkessel, J.M., “Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design”, Journal of Spacecraft and Rocket, Vol. 41, No. 1, 2004, pp. 20-28. Smith, J., “Concurrent Engineering in the Jet Propulsion Laboratory Project Design Center.” Proceedings of SAE Aerospace Manufacturing Technology Conference & Exposition, Long Beach, CA, June, 1998. Sobieszczanski-Sobieski, J., "Multidisciplinary Design Optimization: An Emerging New Engineering Discipline." Technical Report, NASA, May, 1993. Theroude, C. and Hanna, M., “SYSTEMA: An Interdisciplinary Tool Suite for Space Engineering.” Proceedings of the 3rd International Workshop on System & Concurrent Engineering for Space Applications, Roma, Italy, October 15-17, 2008. van Tooren, M. and La Rocca, R., “Systems Engineering and Multi-disciplinary Design Optimization.” Proceedings of the 15th ISPE International Conference on Concurrent Engineering, Belfast, Northern Ireland, August 18-22, 2008. Wilke, M. and Quirmbach, O. and et al., “MuSSat – A Tool for Satellite Design in Concept Design Centers”, Proceedings of the 2nd European Systems Engineering Conference, München, 2000. Wertz, J.R. and Larson, W.J., Space Mission Analysis and Design, 3rd Edition, Microcosm Press, Torrance, California, 1999. Zhao, Y. and Chen, X.Q. and Wang, Z.G., "SIDE: A Tool for Integrated Multidisciplinary Design Optimization of Spacecraft." Proceedings of the 11th
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AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, VI, Sep 6-8, 2006.
Biography Jian Guo is Researcher/Spacecraft System Engineer in the Chair of Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology (TU Delft), The Netherlands. Dr Guo has four years’ experience of working as System Engineer for three small satellite missions. He is currently in charge of the micro-satellite research & development line in TU Delft, and also acts as Project Manager of an international small satellite formation flying mission. His research interests are: spacecraft systems engineering, Multidisciplinary Design Optimization (MDO) and its applications on space systems, spacecraft formation flying, and so on.
