Relevant bibliographies by topics / Multi-Disciplinary Design Optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi-Disciplinary Design Optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Multi-Disciplinary Design Optimization"

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KUMAR NETHALA, Y. V. KISHORE. "Multi Disciplinary Design Optimization." International Journal of Innovative Research in Science, Engineering and Technology 4, no. 11 (2015): 10512–20. http://dx.doi.org/10.15680/ijirset.2015.0411025.

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Venkayya, V. B. "Mathematical optimization in multi-disciplinary design." Mathematical and Computer Modelling 14 (1990): 29–36. http://dx.doi.org/10.1016/0895-7177(90)90144-c.

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Motsamai, Oboetswe S., Jan A. Visser, and Reuben M. Morris. "Multi-disciplinary design optimization of a combustor." Engineering Optimization 40, no. 2 (2008): 137–56. http://dx.doi.org/10.1080/03052150701641866.

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Goetzendorf-Grabowski, Tomasz. "Multi-disciplinary optimization in aeronautical engineering." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 12 (2017): 2305–13. http://dx.doi.org/10.1177/0954410017706994.

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Nowadays, optimization is a very popular tool used to improve existing projects. The optimization covers different disciplines by linking them into multidisciplinary process of design. Existing software tools allow to very effectively solve particular problems giving high quality solutions which were previously very hard to achieve. Aeronautical engineering is a domain/field which links many disciplines: aerodynamics, stability, control, structural analysis, materials, propulsion systems, avionics, etc. Therefore, the multidisciplinary optimization results in very significant progress not only in aircraft design but also in air transport, which links technical aspects with economical questions. The paper presents selected aspects of using the multidisciplinary optimization in aeronautical engineering with special focus on multidisciplinary aircraft design.
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Hu, J., Y. H. Peng, and G. L. Xiong. "Multi-disciplinary robust coordination for algebraic and differential constraints and its application to parameter design of bogies." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 11 (2008): 2147–61. http://dx.doi.org/10.1243/09544062jmes914.

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This article presents a new multi-disciplinary parameter design method. In the proposed method, the multi-disciplinary design process includes constraints modelling, parameter coordination, and parameter optimization. First, the multi-disciplinary constraints model for robust coordination is studied. Second, an approach of multi-disciplinary robust coordination for algebraic and differential constraints is presented to support multi-disciplinary collaborative design. A general algorithm is designed to predict conflict and refine the consistent intervals for parameter coordination. Finally, a multi-disciplinary optimization method is presented. The proposed method is applied to parameter design of bogies, considering design constraints from different disciplines, such as mechanics, cybernetics, dynamics, and so on.
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Fujita, Kikuo, Shinsuke Akagi, and Satoshi Miki. "Multi-Disciplinary Optimization System for Link Mechanism Design." Transactions of the Japan Society of Mechanical Engineers Series C 60, no. 579 (1994): 3670–77. http://dx.doi.org/10.1299/kikaic.60.3670.

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Priyadarshi, Pankaj, Mofeez Alam, and Kamal Saroha. "Multi-disciplinary multi-objective design optimization of sounding rocket fins." International Journal of Advances in Engineering Sciences and Applied Mathematics 6, no. 3-4 (2014): 166–82. http://dx.doi.org/10.1007/s12572-015-0121-6.

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WU, PENG, ZHILI TANG, and JIANDA SHENG. "MULTI-DISCIPLINARY DESIGN OPTIMIZATION OF HYPERSONIC AIR-BREATHING VEHICLE." International Journal of Modern Physics: Conference Series 42 (January 2016): 1660169. http://dx.doi.org/10.1142/s2010194516601691.

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A 2D hypersonic vehicle shape with an idealized scramjet is designed at a cruise regime: Mach number (Ma) = 8.0, Angle of attack (AOA) = 0 deg and altitude ([Formula: see text]) = 30kms. Then a multi-objective design optimization of the 2D vehicle is carried out by using a Pareto Non-dominated Sorting Genetic Algorithm II (NSGA-II). In the optimization process, the flow around the air-breathing vehicle is simulated by inviscid Euler equations using FLUENT software and the combustion in the combustor is modeled by a methodology based on the well known combination effects of area-varying pipe flow and heat transfer pipe flow. Optimization results reveal tradeoffs among total pressure recovery coefficient of forebody, lift to drag ratio of vehicle, specific impulse of scramjet engine and the maximum temperature on the surface of vehicle.
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Ghadge, Rohit, Ratnakar Ghorpade, and Sumedh Joshi. "Multi-disciplinary design optimization of composite structures: A review." Composite Structures 280 (January 2022): 114875. http://dx.doi.org/10.1016/j.compstruct.2021.114875.

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Jeong, S., and K. Shimoyama. "Review of data mining for multi-disciplinary design optimization." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 225, no. 5 (2011): 469–79. http://dx.doi.org/10.1177/09544100jaero906.

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Dissertations / Theses on the topic "Multi-Disciplinary Design Optimization"

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Nambiar, Arun N. "Data Exchange in Multi-Disciplinary Design Optimization frameworks." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1088189791.

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Chen, Ying. "Formulation of a Multi-Disciplinary Design Optimization of Containerships." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/36069.

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To develop a computer tool that will give the best ship design using an optimization technique is one of the objects of the FIRST project. Choosing a containership design as a test case, the Design Optimization Tools (DOT) package is used as the optimization tool. The problem is tackled from the ship owner's point of view. The required freight rate is chosen as the objective function because the most important thing that concerns the ship owner is whether the ship will make a profit or not, and if so, how much profit it can make. DOT, as well as any other numerical optimization tool, only gives an approximation of the optimum design and uses numerical approximation during the optimization. It is very important for the users to formulate carefully the optimization problem so that it will give a stable and reasonable solution. Development of a geometric module and choosing suitable empirical formulas for performance evaluation are also major issues of the project.<br>Master of Science
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Demko, Daniel Todd. "Tools for Multi-Objective and Multi-Disciplinary Optimization in Naval Ship Design." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/31743.

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This thesis focuses on practical and quantitative methods for measuring effectiveness in naval ship design. An Overall Measure of Effectiveness (OMOE) model or function is an essential prerequisite for optimization and design trade-off. This effectiveness can be limited to individual ship missions or extend to missions within a task group or larger context. A method is presented that uses the Analytic Hierarchy Process combined with Multi-Attribute Value Theory to build an Overall Measure of Effectiveness and Overall Measure of Risk function to properly rank and approximately measure the relative mission effectiveness and risk of design alternatives, using trained expert opinion to replace complex analysis tools. A validation of this method is achieved through experimentation comparing ships ranked by the method with direct ranking of the ships through war gaming scenarios. The second part of this thesis presents a mathematical ship synthesis model to be used in early concept development stages of the ship design process. Tools to simplify and introduce greater accuracy are described and developed. Response Surface Models and Design of Experiments simplify and speed up the process. Finite element codes such as MAESTRO improve the accuracy of the ship synthesis models which in turn lower costs later in the design process. A case study of an Advanced Logistics Delivery Ship (ALDV) is performed to asses the use of RSM and DOE methods to minimize computation time when using high-fidelity codes early in the naval ship design process.<br>Master of Science
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Good, Nathan Andrew. "Multi-Objective Design Optimization Considering Uncertainty in a Multi-Disciplinary Ship Synthesis Model." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/34532.

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Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated "best" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a "confidence of success" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed.<br>Master of Science
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Smaling, Rudolf M. "System architecture selection in a multi-disciplinary system design optimization framework." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/91788.

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Magalhães, Paulo Eduardo Cypriano da Silva. "Multi-objective multi-disciplinary optimization applied to the conceptual design of airliners for minimal environmental impact." Instituto Tecnológico de Aeronáutica, 2014. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2958.

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Airplane design consists of putting together several thousand parts to ultimately fulfill a set of requirements defined principally by airlines, manufacturers, and certification authorities. From the traditional perspective of an airline, an interesting airplane is one that is capable of generating the highest revenue with minimum operating cost - a maximum profit airplane. However, the airline industry is constantly broadening its consideration of what constitutes a nice-to-buy airplane. In recent times, not only economics, but also environmental considerations, are taking part in fleet-planning considerations. Following this trend induced by environmentally-aware passengers, and noise and pollution-related charges, airplane conceptual design methodologies are being expanded to incorporate methodologies for preliminary assessment of airplane noise and emissions. During the development of this dissertation, a group of airplane design methodologies was compiled and integrated into a design framework. This design framework was then expanded to incorporate noise and emissions estimation routines. This expanded group is then made into design functions and put through an automated design optimization process. In order to test both the design methodologies and the optimization techniques, two test cases are run: a long range, transcontinental jet and a mid-size regional jet. These test designs are initially single-objectively optimized for direct operating costs, noise and emissions. Then, the airplanes are optimized for pairs of these design objectives. Finally, they are optimized for the three objectives simultaneously. Results and suggestions for future works are presented.
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Arsenyev, Ilya [Verfasser]. "Efficient Surrogate-based Robust Design Optimization Method : Multi-disciplinary Design for Aero-turbine Components / Ilya Arsenyev." Aachen : Shaker, 2018. http://d-nb.info/1166507599/34.

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Kim, Sangwook. "A multi-disciplinary approach for ontological modeling of enterprise business processes : case-based approach /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052188.

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Arslantas, Yunus Emre. "Conceptual Design Optimization Of A Nano-satellite Launcher." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614364/index.pdf.

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Recent developments in technology are changing the trend both in satellite design and application of that technology. As the number of small satellites built by experts from academia and private companies increases, more effective ways of inserting those satellites into orbit is needed. Among the various studies that focus on the launch of such small satellites, research on design of Launch Vehicle tailored for nano-satellites attracts special attention. In this thesis, Multiple Cooling Multi Objective Simulated Annealing algorithm is applied for the conceptual design of Launch vehicle for nano-satellites. A set of fitness functions are cooled individually, and acceptance is based on the maximum value of the acceptance probabilities calculated. Angle of attack and propulsion characteristics are employed as optimization parameters. Algorithm finds the optimum trajectory as well as the design parameters that satisfies user defined constraints. In this study burnout velocity, and payload mass are defined as objectives. The methodolgy is applied for different design scenarios including multistage, air and ground launch vehicles.
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Ghoman, Satyajit Sudhir. "A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23113.

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The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness.<br /><br />The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE.<br /><br />The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem.<br>Ph. D.
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Books on the topic "Multi-Disciplinary Design Optimization"

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Patnaik, Surya N. A general-purpose optimization engine for multi-disciplinary design applications. National Aeronautics and Space Administration, 1996.

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Patnaik, Surya N. A general-purpose optimization engine for multi-disciplinary design applications. National Aeronautics and Space Administration, 1996.

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Patnaik, Surya N. A general-purpose optimization engine for multi-disciplinary design applications. National Aeronautics and Space Administration, 1996.

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Patnaik, Surya N. A general-purpose optimization engine for multi-disciplinary design applications. National Aeronautics and Space Administration, 1996.

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Periaux, Jacques, Felipe Gonzalez, and Dong Seop Chris Lee. Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9520-3.

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Lane, Leigh Blackmon. Multi-disciplinary teams in context-sensitive solutions. Transportation Research Board, 2007.

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The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Storming Media, 1999.

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Integrating Automated Multi-Disciplinary Optimization in Preliminary Design of Non-Traditional Aircraft. Storming Media, 2000.

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Periaux, Jacques, Felipe Gonzalez, and Dong Seop Chris Lee. Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design: Applications to Aeronautics and UAV Design. Springer, 2015.

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Periaux, Jacques, Felipe Gonzalez, and Dong Seop Chris Lee. Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design: Applications to Aeronautics and UAV Design. Springer, 2015.

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Book chapters on the topic "Multi-Disciplinary Design Optimization"

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van Tooren, Michel, and Gianfranco La Rocca. "Systems Engineering and Multi-disciplinary Design Optimization." In Collaborative Product and Service Life Cycle Management for a Sustainable World. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-972-1_38.

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Pan, Binbin, and Weicheng Cui. "Application of Multi-disciplinary Design Optimization in Manned Submersible Design." In Ocean Engineering & Oceanography. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6455-0_7.

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Pan, Binbin, and Weicheng Cui. "Reliability Based Multi-disciplinary Design Optimization Based on Reliability." In Ocean Engineering & Oceanography. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6455-0_5.

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Hou, Liqiang, Yuanli Cai, and Jisheng Li. "Evidence-Based Multi-disciplinary Robust Optimization for Mars Microentry Probe Design." In EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49325-1_7.

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Heo, S. J., I. H. Kim, D. O. Kang, et al. "Multi-Disciplinary Constraint Design Optimization Based on Progressive Meta-Model Method for Vehicle Body Structure." In Optimization of Structures and Components. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00717-5_7.

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Mattos, Bento S. de, Felipe A. Bortolete, José A. T. G. Fregnani, Ariosto B. Jorge, William M. Alves, and Ronaldo V. Cruz. "Application of Machine Learning and Multi-Disciplinary/Multi-Objective Optimization Techniques for Conceptual Aircraft Design." In Model-based and Signal-Based Inverse Methods. Biblioteca Central da Universidade de Brasilia, 2022. http://dx.doi.org/10.4322/978-65-86503-71-5.c05.

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Yonglei, Su, Zhang Weineng, and Li XueLiang. "Multi-disciplinary Design Optimization of Vehicle Performance Based on Design of Experimental and Approximate Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3842-9_84.

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Anthonis, Jan, Marco Gubitosa, Stijn Donders, Marco Gallo, Peter Mas, and Herman Van der Auweraer. "Multi-Disciplinary Optimization of an Active Suspension System in the Vehicle Concept Design Stage." In Recent Advances in Optimization and its Applications in Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12598-0_38.

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Hou, Liqiang, Hengnian Li, Peijun Yu, and Guangdong Liang. "A New Multi-disciplinary Robust Optimization Method for Micro Re-entering Lifting-Body Design." In Recent Advances in Computer Science and Information Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25766-7_69.

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Emiroğlu, Altuğ, Roland Wüchner, and Kai-Uwe Bletzinger. "Treating Non-conforming Sensitivity Fields by Mortar Mapping and Vertex Morphing for Multi-disciplinary Shape Optimization." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72020-3_9.

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Conference papers on the topic "Multi-Disciplinary Design Optimization"

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Van der Velden, A. "Multi-disciplinary SCT design optimization." In Aircraft Design, Systems, and Operations Meeting. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-3931.

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Platt, Michael, Maria Yu, and Matt Marsh. "Multi-Disciplinary Design Optimization of a LH2 Turbopump Design." In 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-4765.

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Miller, Gerald. "An active flexible wing multi-disciplinary design optimization method." In 5th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-4412.

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Hopkins, D., S. Patnaik, and L. Berke. "General-purpose optimization engine for multi-disciplinary design applications." In 6th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4163.

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Pouzadoux, Frederic, Guillaume Reydellet, Edith Taillefer, and Mohamed Masmoudi. "Introduction of Multi-Disciplinary Optimization in Compressor Blade Design." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6018.

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Kim, Cheolwan, and Yung-Gyo Lee. "Multi-Disciplinary Design Optimization of Unmanned Aerial Vehicle." In ASME 2011 Pressure Vessels and Piping Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/pvp2011-57567.

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A general procedure of preliminary design of aircraft and one-way fluid-structure interaction (FSI) applied to aircraft design is introduced briefly. Then, FSI and optimization technique are implemented to optimize a wing shape of an unmanned aerial vehicle (UAV) for minimum cruise drag. FSI analysis and optimization processes for minimizing drag of UAV are explained. Design variables are wing taper ratio and dihedral angle, and objective function is the cruise drag of UAV. Fluid solution is generated with Euler solver and structural analysis is performed with FEM solver, Diamond. Sample points are selected by Design of Experiment (DOE) method and Kriging method is used for generation of an approximation model.
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Priyadarshi, Pankaj, and Sanjay Mittal. "Multi-objective Multi-disciplinary Design Optimization of a Semi-Ballistic Reentry Module." In 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference. American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-9127.

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Ghoman, Satyajit, Ping Chen, Darius Sarhaddi, D. Lee, and Rakesh Kapania. "A Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment." In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1833.

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Yu, Bo Yang, Tomonori Honda, Syed Zubair, Mostafa H. Sharqawy, and Maria C. Yang. "Multi-Disciplinary Design Optimization for Large-Scale Reverse Osmosis Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35032.

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Large-scale desalination plants are complex systems with many inter-disciplinary interactions and different levels of sub-system hierarchy. Advanced complex systems design tools have been shown to have a positive impact on design in aerospace and automotive, but have generally not been used in the design of water systems. This work presents a multi-disciplinary design optimization approach to desalination system design to minimize the total water production cost of a 30,000m3/day capacity reverse osmosis plant situated in the Middle East, with a focus on comparing monolithic with distributed optimization architectures. A hierarchical multi-disciplinary model is constructed to capture the entire system’s functional components and subsystem interactions. Three different multi-disciplinary design optimization (MDO) architectures are then compared to find the optimal plant design that minimizes total water cost. The architectures include the monolithic architecture multidisciplinary feasible (MDF), individual disciplinary feasible (IDF) and the distributed architecture analytical target cascading (ATC). The results demonstrate that an MDF architecture was the most efficient for finding the optimal design, while a distributed MDO approach such as analytical target cascading is also a suitable approach for optimal design of desalination plants, but optimization performance may depend on initial conditions.
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Lin, JiGuan. "Examining and Improving Collaborative Optimization Approach to Multi-Disciplinary Design." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5503.

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Reports on the topic "Multi-Disciplinary Design Optimization"

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Kuprowicz, Nicholas J. The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada372032.

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