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  • 51. Kokkolaras, Michael
    et al.
    Mourelatos, Zissimos P.
    Department of Mechanical Engineering, Oakland University.
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Design optimization of hierarchically decomposed multilevel systems under uncertainty2006In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 128, no 2, p. 503-508Article in journal (Refereed)
    Abstract [en]

    This paper presents a methodology for design optimization of hierarchically decomposed systems under uncertainty. We propose an extended, probabilistic version of the deterministic analytical target cascading (ATC) formulation by treating uncertain quantities as random variables and posing probabilistic design constraints. A bottom-to-top coordination strategy is used for the ATC process. Given that first-order approximations may introduce unacceptably large errors, we use a technique based on the advanced mean value method to estimate uncertainty, propagation through the multilevel hierarchy of elements that comprise the decomposed system. A simple yet illustrative hierarchical bilevel engine design problem is used to demonstrate the proposed methodology. The results confirm the applicability of the proposed probabilistic ATC formulation and the accuracy of the uncertainty propagation technique

  • 52. Kokkolaras, Michael
    et al.
    Mourelatos, Zissimos P.
    Department of Mechanical Engineering, Oakland University.
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Impact of uncertainty quantification on design decisions for a hydraulic-hybrid powertrain engine2006In: 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference: 14th AIAA/ASME/AHS Adaptive Structures Conference - 8th AIAA Non-Deterministic Approaches Conference - 7th AIAA Gossamer Spacecraft Forum - 2nd AIAA Multidisciplinary Design Optimization Specialist Conference : [Newport, Rhode Island, 1 - 4 May 2006], Reston, Va.: American Institute of Aeronautics and Astronautics, AIAA , 2006, Vol. 7, p. 4954-4963Conference paper (Refereed)
    Abstract [en]

    The method for solving an optimal design problem under uncertainty depends on how the latter is quantified. When sufficient information is available the popular probabilistic approach can (and should) be adopted. In reality however, we often do not have sufficient data to infer appropriate probability distributions for the uncertain quantities modeled as random variables. The amount of available information about the uncertain quantities may be limited to ranges of values (intervals). In this case, the interval analysis approach can be employed to reformulate and solve the optimal design problem. In this study, we use both approaches to solve an engine design optimization problem that considers fuel economy and acceleration performance of a medium-sized truck with a hydraulic-hybrid powertrain. We then contrast the obtained results and comment on the characteristics and features of the two approaches. We also demonstrate an extension of the interval analysis approach to multilevel systems using a simple yet illustrative engine-related example

  • 53. Kokkolaras, Michael
    et al.
    Papalambros, Panos Y.
    University of Michigan.
    Analytical Target Cascading in Design Optimization of Hierarchical Multilevel Systems under Uncertainty2003In: ISMP 2003: [the International Symposium on Mathematical Programming ... The 18th symposium takes place ... in Copenhagen August 18 - 22, 2003] / [ed] Jørgen Tind, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2003Conference paper (Refereed)
  • 54. Kokkolaras, Michael
    et al.
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Nondeterministic Formulations of Analytical Target Cascading for Decomposition-based Design Optimization under Uncertainty2008In: Structural design optimization considering uncertainties, London: Taylor and Francis Group , 2008Chapter in book (Refereed)
  • 55.
    Li, Jing
    et al.
    Mechanical Engineering Department, Oakland University.
    Mourelatos, Zissimos P.
    Mechanical Engineering Department, Oakland University.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Gorsich, David J.
    U.S. Army, TARDEC.
    Validating Designs Through Sequential Simulation-Based Optimization2010In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2010: presented at ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 15 - 18, 2010, Montreal, Quebec, Canada, New York: American Society of Mechanical Engineers , 2010, Vol. Volume 1: 36th Design Automation Conference, Parts A and B, p. 1023-1031Conference paper (Other academic)
    Abstract [en]

    Computational simulation models support a rapid design process. Given model approximation and operating conditions uncertainty, designers must have confidence that the designs obtained using simulations will perform as expected. This paper presents a methodology for validating designs as they are generated during a simulation-based optimization process. Current practice focuses on validation of simulation models throughout the entire design space. In contrast, the proposed methodology requires validation only at design points generated during optimization. The goal of such validation is confidence in the resulting design rather than in the underlying simulation model. The proposed methodology is illustrated on a simple cantilever beam design subject to vibration

  • 56.
    Li, Zhijun
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Izquierdo, Luis E.
    University of Michigan.
    Hu, S. Jack
    University of Michigan.
    Papalambros, Panos Y.
    University of Michigan.
    Multiobjective optimization for integrated tolerance allocation and fixture layout design in multistation assembly2006In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 20: presented at 2006 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, September 10 - 13, 2006, Philadelphia, Pennsylvania, USA, New York: American Society of Mechanical Engineers , 2006, Vol. 1 : 32nd Design Automation Conference, Parts A and B, p. 199-206Conference paper (Refereed)
    Abstract [en]

    Cost and product quality are significant attributes in manufacturing processes, such as multistation assembly. We use multiobjective optimization for integrated tolerance allocation and fixture layout design to address their interaction and to quantify tradeoffs among cost, product quality, and assembly process robustness. Design decisions relate to product tolerances, assembly process tolerances, and fixture locating positions. A nested optimization strategy is adopted, and the proposed methodology is demonstrated using a vehicle side frame assembly example. The obtained results provide evidence for the existence of tradeoffs, based on which we can identify critical quality and budget requirements.

  • 57.
    Li, Zhijun
    et al.
    Department of Mechanical Engineering, University of Michigan.
    Yue, Jianpeng
    Department of Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Camelio, Jaime
    Department of Mechanical Engineering, University of Michigan.
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Hu, S. Jack
    Department of Mechanical Engineering, University of Michigan.
    Product tolerance allocation in compliant multistation assembly through variation propagation and analytical target cascading2004In: Proceedings of the ASME Design Engineering Division - 2004: presented at the 2004 ASME International Mechanical Engineering Congress and Exposition, November 13 - 19, 2004, Anaheim, California, USA, New York: American Society of Mechanical Engineers , 2004, p. 813-820Conference paper (Refereed)
    Abstract [en]

    Compliant sheet metal assembly is a hierarchical manufacturing process that plays a significant role in automotive product development. Parts are joined in different stations to form the final product (e.g., the vehicle body structure). Dimensional variation is a product attribute of major importance that characterizes quality, and is mainly affected by the variability of parts, fixtures, and joining methods at each of the multiple stations. The propagation of dimensional variation through the multistation assembly system is modeled as a linear process, where all three aforementioned sources of variability are taken into account at each station using finite element models. In this article we apply the analytical target cascading process to the tolerance allocation problem in multistation assembly systems. Specifically, we translate final product variation targets to tolerance specifications for subassemblies and incoming parts. We demonstrate the methodology by means of a vehicle side frame assembly example.

  • 58.
    Li, Zhijun
    et al.
    Foxconn Technology Group, Sunnyvale.
    Kokkolaras, Michael
    Izquierdo, L.E
    Warwick Digital Lab., University of Warwick.
    Papalambros, Panos Y.
    Mechanical Engineering, University of Michigan.
    Hu, S. Jack
    Mechanical Engineering, University of Michigan.
    Multiobjective Optimization for Integrated Tolerance Allocation and Fixture Layout Design in Multistation Assembly2008In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 130, no 4, p. 0445011-0445016Article in journal (Refereed)
    Abstract [en]

    Cost and dimensional variation of products are significant attributes in multistation assembly processes. These attributes depend on product/process tolerances and fixture layouts. Typically, tolerance allocation and fixture layout design are conducted separately without considering potential interrelations. In this work, we use multiobjective optimization for integrated tolerance allocation and fixture layout design to address interactions and to quantify tradeoffs among cost, product variation, and assembly process sensitivity. A nested optimization strategy is applied to a vehicle side frame assembly. Results demonstrate the presence and quantification of tradeoffs, based on which we introduce the concept of critical variation and critical budget requirements

  • 59.
    Li, Zhijun
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Jung, Dohoy
    University of Michigan.
    Papalambros, Panos Y.
    University of Michigan.
    Assanis, Dennis N.
    University of Michigan.
    An Optimization Study of Manufacturing Variation Effects on Diesel Injector Design with Emphasis on Emissions2005In: S A E Transactions, ISSN 0096-736X, Vol. 113, no Section 5, p. 724-733Article in journal (Refereed)
    Abstract [en]

    This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages

  • 60.
    Li, Zhijun
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Jung, Dohoy
    University of Michigan.
    Papalambros, Panos Y.
    University of Michigan.
    Assanis, Dennis N.
    University of Michigan.
    An Optimization Study of Manufacturing Variation Effects on Diesel Injector Design with Emphasis on Emissions2004In: Emissions: advanced catalyst and substrates, measurement and testing, and diesel gaseous emissions: [Powertrain & Fluid Systems Conference & Exhibition, October 27 - 30, 2003, Westin Convention Center Hotel, Pittsburgh, Pennsylvania, USA], Warrendale, Pa: Society of Automotive Engineers, Incorporated , 2004Conference paper (Refereed)
  • 61.
    Li, Zhijun
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Papalambros, Panos
    University of Michigan.
    Hu, S. Jack
    University of Michigan.
    Product and Process Tolerance Allocation in Compliant Multistation Assembly Using Analytical Target Cascading2008In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 130, no 9Article in journal (Refereed)
  • 62.
    Liu, Huibin
    et al.
    Department of Mechanical Engineering, Northwestern University, Evanston, IL.
    Chen, Wei
    Department of Mechanical Engineering, Northwestern University, Evanston, IL.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Kim, Harrisson M.
    Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign.
    Probabilistic analytical target cascading: A moment matching formulation for multilevel optimization under uncertainty2006In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 128, no 4, p. 991-1000Article in journal (Refereed)
    Abstract [en]

    Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation.

  • 63.
    Liu, Huibin
    et al.
    Northwestern University.
    Chen, Wei
    Department of Mechanical Engineering, Northwestern University, Evanston, IL.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    University of Michigan.
    Kim, Harrison M.
    University of Illinois.
    Probabilistic analytical target cascading: A moment matching formulation for multilevel optimization under uncertainty2005In: Proceedings of the ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2005: presented at 2005 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, September 24 - 28, 2005, Long Beach, California, USA, New York: American Society of Mechanical Engineers , 2005, Vol. 2: 31st Design Automation Conference, Parts A and B, p. 1173-1182Conference paper (Refereed)
  • 64.
    louca, L.S.
    et al.
    Department of Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Delagrammatikas, G.J.
    Department of Mechanical Engineering, University of Michigan.
    Michelena, N.F.
    Department of Mechanical Engineering, University of Michigan.
    Filipi, Z.S.
    Department of Mechanical Engineering, University of Michigan.
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Assanis, D.N.
    Department of Mechanical Engineering, University of Michigan.
    Analytical Target Cascading for the Design of an Advanced Technology Heavy Truck2002In: Proceedings of the ASME Design Engineering Division - 2002: presented at the 2002 ASME International Mechanical Engineering Congress and Exposition, November 17 - 22, 2002, New Orleans, Louisiana, New York: American Society of Mechanical Engineers , 2002, p. 3-10Conference paper (Refereed)
    Abstract [en]

    Analytical target cascading (ATC) is a methodology that can be used during the early development stages of large and complex systems for propagating desirable overall product targets to appropriate individual specifications for the various subsystems and components. The ATC process is applied to the design of an advanced technology heavy truck. A series hybrid-electric propulsion system, in-hub motors, and variable height suspensions are introduced with the intent to improve both commercial and military design attributes according to a dual-use design philosophy. Emphasis is given to fuel economy, ride, and mobility characteristics. These vehicle responses are predicted by appropriately developed analytical and simulation models. This article is an extension to previous work: the engine is now included at the bottom level, several battery types are considered to study their effect on fuel economy, and a more demanding driving schedule is used to assess regenerative braking benefits and ride quality. Results are presented for target values associated with a 100% improvement on fuel economy while maintaining performance attributes relative to existing designs.

  • 65.
    Michelena, Nestor
    et al.
    University of Michigan.
    Louca, Loucas
    University of Michigan.
    Kokkolaras, Michael
    Lin, Chan-Chiao
    University of Michigan.
    Jung, Dohoy
    University of Michigan.
    Filipi, Zoran
    University of Michigan.
    Assanis, Dennis
    University of Michigan.
    Papalambros, Panos Y.
    University of Michigan.
    Peng, Huei
    University of Michigan.
    Stein, Jeff
    University of Michigan.
    Design of an Advanced Heavy Tactical Trucks: A Target Cascading Case Study2001In: International Truck and Bus Meeting and Exhibition: Chicago, Illinois, November 12 - 14, 2001, Warrendale, Pa: Society of Automotive Engineers, Incorporated , 2001Conference paper (Refereed)
    Abstract [en]

    The target cascading methodology is applied to the conceptual design of an advanced heavy tactical truck. Two levels are defined: an integrated truck model is represented at the top (vehicle ) level and four independent suspension arms are represented at the lower (system ) level. Necessary analysis models are developed, and design problems are formulated and solved iteratively at both levels. Hence, vehicle design variables and system specifications are determined in a consistent manner. Two different target sets and two different propulsion systems are considered. Trade-offs between conflicting targets are identified. It is demonstrated that target cascading can be useful in avoiding costly design iterations late in the product development process.

  • 66.
    Mourelatos, Z.P.
    et al.
    University of Michigan.
    Gorsich, D.
    University of Michigan.
    Kokkolaras, Michael
    Papalambros, Panos Y
    University of Michigan.
    Modeling Uncertainty in Analytical Target Cascading2003In: Mathematics for industry: challenges and frontiers: a process view: practice and theory ; [proceedings of the SIAM Conference on Mathematics for Industry: Challenges and Frontiers, Toronto, Ontario, October 13 - 15, 2003] / [ed] David R. Ferguson, Philadelphia: Society for Industrial and Applied Mathematics, , 2003Conference paper (Refereed)
  • 67.
    Mourelatos, Z.P.
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    University of Michigan.
    Gorsich, D.
    University of Michigan.
    Design Optimization of Hybrid Military Trucks Considering Uncertainty2005In: SIAM Conference on Mathematics for Industry: challenges and frontiers, Detroit October 24-26, 2005, Philadelphia, Pa: SIAM , 2005Conference paper (Refereed)
  • 68.
    Nyström, Mattias
    et al.
    Luleå tekniska universitet.
    Larsson, Tobias
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Karlsson, Lennart
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Linking analytical target cascading to engineering information systems for simulation-based optimal vehicle design2003In: Research for practice - innovation in products, processes and organisations: ICED 03, 14th International Conference on Engineering Design ; 19 - 21 August 2003, The Royal Institute of Technology, Stockholm / [ed] Anders Folkeson, Glasgow: Design Research Society, 2003Conference paper (Refereed)
  • 69.
    Pai, Yogita
    et al.
    University of Michigan.
    Kokkolaras, Michael
    Hulbert, Greg
    University of Michigan.
    Papalambros, Panos Y.
    University of Michigan.
    Pozolo, Michael K.
    US Army RDECOM-TARDEC.
    Fu, Yan
    Ford Motor Company.
    Yang, Ren-Jye
    Ford Motor Company.
    Barbat, Saeed
    Ford Motor Company.
    Assessment of a Bayesian Model and Test Validation Method2009Conference paper (Refereed)
  • 70.
    Pai, Yogita
    et al.
    Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Hulbert, Gregory M.
    Mechanical Engineering, University of Michigan.
    Towards Confidence Extrapolation of Validated Simulations2009In: 10th US National Congress on Computational Mechanics (USNCCM 9): proceedings ; July 16-19, 2009, Columbus, Ohio, 2009Conference paper (Refereed)
  • 71. Pai, Yogita
    et al.
    Kokkolaras, Michael
    Hulbert, Gregory M.
    Fu, Y.
    Yang, R-J
    Barbat, S.
    Sensitivity Analysis of Bayesian Model Validation2009Conference paper (Refereed)
  • 72.
    Park, S.
    et al.
    University of Michigan.
    Malikopoulos, A.
    University of Michigan.
    Kokkolaras, Michael
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Jung, D.
    University of Michigan.
    Thermal management system modeling and component sizing for heavy duty series hybrid electric vehicles2011In: International Journal of Vehicle Design. Heavy Vehicle Design, ISSN 1744-232X, E-ISSN 1741-5152, Vol. 18, no 3, p. 272-287Article in journal (Refereed)
    Abstract [en]

    A thermodynamics-based Vehicle Thermal Management System (VTMS) model for a heavy-duty, off-road vehicle with a series hybrid electric powertrain is developed for analysing thermal behaviour and investigating power consumption under different vehicle driving conditions. The developed model is first used to define an acceptable and reasonable VTMS configuration; design space exploration techniques are then applied to determine optimal component sizes subject to performance and geometry constraints.

  • 73.
    Park, Sungjin
    et al.
    University of Michigan.
    Malikopoulos, A.
    Kokkolaras, Michael
    AbdulNour, Bashar
    General Dynamics Land Systems.
    Sedarous, J.
    Jung, D.
    Thermal Management System Modeling and Optimization for Heavy Hybrid Electric Military Vehicles2010Conference paper (Refereed)
  • 74.
    Parkinson, Matthew B.
    et al.
    Engineering Design Program and Mechanical Engineering, The Pennsylvania State University.
    Reed, Matthew P.
    University of Michigan, Transportation Research Institute, Ann Arbor.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Optimizing Truck Cab Layout for Driver Accommodation2007In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 129, no 11, p. 1110-1117Article in journal (Refereed)
    Abstract [en]

    One important source of variability in the performance and success of products designed for use by people is the people themselves. In many cases, the acceptability of the design is affected more by the variability in the human users than by the variability attributable to the hardware from which the product is constructed. Designing for human variability as an inherent part of the product optimization process can improve the overall performance of the product. This paper presents a new approach to artifact design that applies population sampling and stochastic posture prediction in an optimization environment to achieve optimal designs that are robust to variability among users, including differences in age, physical size, strength, and cognitive capability. A case study involving the layout of the interior of a heavy truck cab is presented, focusing on simultaneous placement of the seat and steering-wheel adjustment ranges. Trade-offs between adjustability (an indicator of cost), driver accommodation, and safety are explored under this paradigm.

  • 75.
    Parkinson, Matthew B.
    et al.
    University of Michigan, Transportation Research Institute, Ann Arbor.
    Kokkolaras, Michael
    Reed, Matthew P.
    Department of Mechanical Engineering, University of Michigan.
    Papalambros, Panos Y.
    Engineering Design, Pennsylvania State University.
    Robust truck cabin layout optimization using advanced driver variance models2005In: Proceedings of the ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2005: presented at 2005 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, September 24 - 28, 2005, Long Beach, California, USA, New York: American Society of Mechanical Engineers , 2005, Vol. 2 : 31st Design Automation Conference, Parts A and B, p. 1103-1109Conference paper (Refereed)
    Abstract [en]

    One important source of variance in the performance and success of products designed for use by people is the people themselves. In many cases, the acceptability of the design is affected more by the variance in the human users than by the variance attributable to the hardware from which the product is constructed. Consequently, optimization of products used by people may benefit from consideration of human variance through robust design methodologies. We propose that design under uncertainty methodologies can be utilized to generate designs that are robust to variance among users, including differences in age, physical size, strength, and cognitive capability. Including human variance as an inherent part of the product optimization process will improve the overall performance of the product (be it comfort, maintainability, cognitive performance, or other metrics of interest) and could lead to products that are more accessible to broader populations, less expensive, and safer. A case study involving the layout of the interior of a heavy truck cab is presented, focusing on simultaneous placement of the seat and steering wheel adjustment ranges. Tradeoffs between adjustability/cost, driver accommodation, and safety are explored under this paradigm.

  • 76.
    Primikiri, Eleni
    et al.
    Taubman College of Architecture and Urban Planning.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    IDEA: Interpolating Data for Energy Analysis2003In: Building simulation 2003: proceedings of the 8th international IBPSA conference; Eindhoven - Netherlands, 11-14 August 2003 / [ed] Godfried Augenbroe, Ghent: International Building Performance Simulation Association (IBPSA), 2003, p. 1061-1068Conference paper (Refereed)
  • 77.
    Primikiri, Eleni
    et al.
    Department of Architecture, University of Patras.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Interpolation of energy performance data for building design decisions2006In: International Journal of Sustainable Energy, ISSN 1478-6451, E-ISSN 1478-646X, Vol. 25, no 2, p. 79-90Article in journal (Refereed)
    Abstract [en]

    In recent years major advances have been made in the development of computational tools for architects to facilitate building performance evaluation. However, most tools require expert knowledge and remain accessible primarily to building engineers or specialized architects. This article presents a methodology of interpolating data for energy analysis to enable easier implementation of building simulation in the design process. Architects and other building project participants can visualize and analyze complex thermal performance of buildings for different design scenarios, requiring little specialized knowledge and without a burdensome computational cost

  • 78.
    Sandberg, Marcus
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Kokkolaras, Michael
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Isaksson, Ola
    Larsson, Tobias
    A master-model approach to whole jet engine analysis and design optimization2009In: WCSMO-8: 8th World Congress on Structural and Multidisciplinary Optimization, 2009Conference paper (Refereed)
  • 79.
    Sandberg, Marcus
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Kokkolaras, Michael
    Larsson, Tobias
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Oldenburg, Mats
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Boart, Patrik
    Volvo Aero Corporation, Sverige.
    Projekt: Strukturell konceptuell konstruktion och analys av helajetmotorer - en METodik för OPtimering, Integration och Automatisering (METOPIA)2009Other (Other (popular science, discussion, etc.))
    Abstract [sv]

    Detta projekt är en fortsättning på förstudieprojektet NFFP4202 - Helmotormodell för systemanalys av mekaniska egenskaper där en plattform, samt en pilot som demonstrerade plattformens förmåga i ett industriellt sammanhang, utvecklades. Detta projekt fokuserar på vidareutveckling av den framtagna plattformen med optimeringsteknologi och simuleringsdriven produktutvecklingsmetodik för att via produktdefinitionen (jetmotorkomponenterna) balansera flera olika funktionsbehov (t ex strukturdynamiska, aerodynamiska, termodynamiska) och skapa möjligheter till effektivare analyser av helmotorkoncept. Resultatet är en metodik innefattande systemangreppssätt, modelluppbyggnad, modellarkitektur samt analys, som tillämpas på ett realistiskt scenario genom en pilot.

  • 80.
    Sandberg, Marcus
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Kokkolaras, Michael
    Larsson, Tobias
    Lindgren, Lars-Erik
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Oldenburg, Mats
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Projekt: NFFP4 - Helmotormodellering2009Other (Other (popular science, discussion, etc.))
    Abstract [sv]

    Nationella Flygtekniska Forskningsprogrammet NFFP Projekt: V4202 Helmotormodellering

  • 81.
    Sandberg, Marcus
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Kokkolaras, Michael
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Tyapin, Ilya
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Isaksson, Ola
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Larsson, Tobias
    A knowledge-based master-model approach with application to rotating machinery design2011In: Concurrent Engineering - Research and Applications, ISSN 1063-293X, E-ISSN 1531-2003, Vol. 19, no 4, p. 295-305Article in journal (Refereed)
    Abstract [en]

    Novel rotating machinery design concepts and architectures are being explored to reduce mass, energy consumption, manufacturing costs, and environmental impact while increasing performance. As component manufacturers supply parts to original equipment manufacturers, it is desirable to design the components using a systems approach so that they are optimized for system-level performance. To accomplish that, suppliers must be able to model and predict the behavior of the whole machinery. Traditional computer-aided design/computer-aided engineering master-modeling approaches enable manual changes to be propagated to linked models. Novel knowledge-based master-modeling approaches enable automated coordination of multidisciplinary analyses. In this article, we present a specific implementation of such a knowledge-based master-modeling approach that facilitates multidisciplinary design optimization of rotating machinery. The master-model (MM) approach promotes the existence of a single governing version of the product definition as well as operating scenarios. Rules, scripts, and macros link the MM to domain-specific models. A simple yet illustrative industry application is presented, where rotor-dynamics and displacement analyses are performed to evaluate relocation alternatives for the rear bearing position of a rotating machinery under a ‘fan-blade-off’ load case.

  • 82.
    Sandberg, Marcus
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Tyapin, Ilya
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Kokkolaras, Michael
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Isaksson, Ola
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    A knowledge-based master modeling approach to system analysis and design2011In: Impacting society through engineering design: ICED 11 København, the 18th International Conference on Engineering Design ; 15th - 18th August 2011, Technical University of Denmark (DTU), Copenhagen, Denmark ; proceedings volumes / [ed] Steve Culley; Ben Hicks; Tim McAloone; T.J. Howard, Design Research Society, 2011, Vol. 4 : Product and systems design, p. 347-356Conference paper (Refereed)
    Abstract [en]

    The jet engine industry relies on product models for early design predictions of attributes such as structural behavior, mass and cost. When the required analysis models are not linked to the governing product model, effective coordination of design changes is a challenge, making design space exploration time-consuming. Master modeling (MM) approaches can help alleviate such analysis overhead; the MM concept has its origins in the computer-aided design (CAD) community, and mandates that manual changes in one model automatically propagate to assembly, computer-aided manufacturing (CAM) and computer-aided engineering (CAE) models within the CAD platform. Knowledge-based master models can also be used to communicate changes in the product definition to models that are external to the CAD platform. This paper presents details of the knowledge-based master modeling approach as applied to mechanical jet engine analysis and design, where different fidelity models and analysis tools are supported in the early design stages.

  • 83.
    Sarin, H.
    et al.
    Department of Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Hulbert, G.
    Department of Mechanical Engineering, University of Michigan.
    Papalambros, Panos
    Department of Mechanical Engineering, University of Michigan.
    Barbat, S.
    Passive Safety, Research and Advanced Engineering, Ford Motor Company.
    Yang, R-J
    Passive Safety, Research and Advanced Engineering, Ford Motor Company.
    A comprehensive metric for comparing time histories in validation of simulation models with emphasis on vehicle safety applications2009In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2008: presented at 2008 ASME International Design Engineering Conferences and Computers and Information in Engineering Conference, August 3 - 6, 2008, New York City, New York, USA, New York: American Society of Mechanical Engineers , 2009, Vol. 1 Part B, p. 1275-1286Conference paper (Refereed)
    Abstract [en]

    Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing a metric to compare time histories that are outputs of simulation models to time histories from experimental tests with emphasis on vehicle safety applications. We focus on quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Then we propose a structured combination of some of these measures and define a comprehensive metric that encapsulates the important aspects of time history comparison. The new metric classifies error components associated with three physically meaningful characteristics (phase, magnitude and topology), and utilizes norms, cross-correlation measures and algorithms such as dynamic time warping to quantify discrepancies. Two case studies demonstrate that the proposed metric seems to be more consistent than existing metrics. It is also shown how the metric can be used in conjunction with ratings from subject matter experts to build regression-based validation models

  • 84.
    Sarin, H.
    et al.
    Department of Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Hulbert, G.
    Department of Mechanical Engineering, University of Michigan.
    Papalambros, Panos
    Department of Mechanical Engineering, University of Michigan.
    Barbat, S.
    Passive Safety, Research and Advanced Engineering, Ford Motor Company.
    Yang, R-J
    Passive Safety, Research and Advanced Engineering, Ford Motor Company.
    Comparing time histories for validation of simulation models: Error measures and metrics2010In: Journal of Dynamic Systems Measurement, and Control, ISSN 0022-0434, E-ISSN 1528-9028, Vol. 132, no 6Article in journal (Refereed)
    Abstract [en]

    Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing measures and a metric to compare time histories obtained from simulation model outputs and experimental tests. The focus of the work is on vehicle safety applications. We restrict attention to quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First, we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Three independent error measures are proposed for vehicle safety applications, associated with three physically meaningful characteristics (phase, magnitude, and slope), which utilize norms, cross-correlation measures, and algorithms such as dynamic time warping to quantify discrepancies. A combined use of these three measures can serve as a metric that encapsulates the important aspects of time history comparison. It is also shown how these measures can be used in conjunction with ratings from subject matter experts to build regression-based validation metrics.

  • 85.
    Simpson, Timothy W.
    et al.
    Pennsylvania State University.
    Marion, Tucker
    Pennsylvania State University.
    Weck, Olivier de
    Massachusetts Institute of Technology.
    Hölttä-Otto, Katja
    University of Massachusetts at Dartmouth.
    Kokkolaras, Michael
    Shooter, Steve B.
    Bucknell University.
    Platform-based design and development: Current trends and needs in industry2006In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2006: presented at 2006 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, September 10 - 13, 2006, Philadelphia, Pennsylvania, USA, New York,: American Society of Mechanical Engineers , 2006, Vol. 1 : 32nd Design Automation Conference, Parts A and B, p. 801-810Conference paper (Refereed)
    Abstract [en]

    Many companies constantly struggle to find cost-effective solutions to satisfy the diverse demands of their customers. In this paper, we report on two recent industry-focused conferences that emphasized platform design, development, and deployment as a means to increase variety, shorten lead-times, and reduce development and production costs. The first conference, Platform Management for Continued Growth, was held November-December 2004 in Atlanta, Georgia, and the second, 2005 Innovations in Product Development Conference - Product Families and Platforms: From Strategic Innovation to Implementation, was held in November 2005 in Cambridge, Massachusetts. The two conferences featured presentations from academia and more than 20 companies who shared their successes and frustrations with platform design and deployment, platform-based product development, and product family planning. Our intent is to provide a summary of the common themes that we observed in these two conferences. Based on this discussion, we extrapolate upon industry's needs in platform design, development, and deployment to stimulate and catalyze future work in this important area of research.

  • 86.
    Tosserams, S.
    et al.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Kokkolaras, Michael
    Etman, L.F.P.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Rooda, J.E.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    A Non-hierarchical Formulation of Analytical Target Cascading2010In: Journal of mechanical design (1990), ISSN 1050-0472, E-ISSN 1528-9001, Vol. 132, no 5Article in journal (Refereed)
  • 87.
    Tosserams, S.
    et al.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Kokkolaras, Michael
    Etman, L.F.P.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Rooda, J.E.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Extension of analytical target cascading using augmented Lagrangian coordination for multidisciplinary design optimization2008In: 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference: MAO ; 10 - 12 September 2008, Victoria, British Columbia, Canada ; [papers], Reston, Va.: American Institute of Aeronautics and Astronautics, AIAA , 2008Conference paper (Refereed)
    Abstract [en]

    Analytical target cascading (ATC) is a method originally developed for translating system-level design targets to design specifications for the elements comprising the system. ATC has also been shown to be useful for coordinating distributed design optimization of hierarchical, multilevel systems. The traditional ATC formulation uses a hierarchically decomposed problem structure, in which coordination is performed by communicating target and response values between parents and children. This paper presents two extensions of the ATC formulation to allow non-hierarchical target-response coupling between subproblems and to introduce system-wide constraints that depend on local variables of two or more subproblems. The ATC formulation with these extensions belongs to a subclass of augmented Lagrangian coordination, and has thus converge properties under the usual convexity and continuity assumptions. A supersonic business jet design problem reported earlier in the literature is used to illustrate these extensions.

  • 88.
    Tyapin, Ilya
    et al.
    University of Agder.
    Sandberg, Marcus
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Kokkolaras, Michael
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Lundbladh, Anders
    Volvo Aero Corporation.
    Isaksson, Ola
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Jet engine design optimization using knowledge-based master models2012In: Proccedings of ASME Turbo Expo 2012, American Society of Mechanical Engineers , 2012, Vol. 7, A and B, p. 41-47Conference paper (Refereed)
    Abstract [en]

    This paper presents a preliminary design optimization study of a jet engine structure using a knowledge-based master modeling approach. The objective function is derived based on input-output relationships of a cost-performance model, where specific fuel consumption, pressure loss and direct cost are considered. The advantage of this problem formulation is that it entails a single composite objective function that takes into account mass, structural characteristics, dynamic response and translates them to a direct operational cost function to be minimized. A fan-blade-off scenario is considered as the loading case in this paper. The loss of one fan blade during nominal operation causes a rotor imbalance and structural deformation.

  • 89.
    Tzevelekos, Nikos
    et al.
    Department of Mechanical Engineering, University of Michigan.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Hulshof, Martijn F.
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Etman, L.F. Pascal
    Department of Mechanical Engineering, Eindhoven University of Technology.
    Rooda, J.E. (Koos)
    Department of Mechanical Engineering, Eindhoven University of Technology.
    An empirical local convergence study of alternative coordination schemes in analytical target cascading2003In: Short papers of the Fifth World Congress of Structural and Multidisciplinary Optimization: May 19 - 23, 2003, Lido di Jesolo, Italy, Milano: Schönenfeld & Ziegler , 2003Conference paper (Refereed)
  • 90.
    Youn, Byeng Dong
    et al.
    Department of Mechanical and Industrial Engineering, University of Iowa.
    Choi, Kyung K.
    Department of Mechanical and Industrial Engineering, University of Iowa.
    Kokkolaras, Michael
    Papalambros, Panos Y.
    Department of Mechanical Engineering, University of Michigan.
    Mourelatos, Z.P.
    Department of Mechanical Engineering, Oakland University.
    Gorsich, David
    Department of the Army, United States Army Tank-Automotive and Armaments Command.
    Techniques for estimating uncertainty propagation in probabilistic design of multilevel systems2004In: A collection of technical papers: 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference : Albany, New York, 30 August - 1 September 2004, Reston, Va.: American Institute of Aeronautics and Astronautics, AIAA , 2004, p. 1893-1902Conference paper (Refereed)
    Abstract [en]

    In probabilistic design of multilevel systems, the challenge is to estimate uncertainty propagation since outputs of subsystems at lower levels constitute inputs of subsystems at higher levels. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to simulation-based, multilevel system design optimization under uncertainty, it is investigated which type of applications each method is best suitable for. The probabilistic formulation of the analytical target cascading methodology is used to solve the multilevel problem. A hierarchical bi-level engine design problem is employed to investigate unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

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