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Michel van Tooren

Bio: Michel van Tooren is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Knowledge-based engineering & Multidisciplinary design optimization. The author has an hindex of 16, co-authored 64 publications receiving 1250 citations.


Papers
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Journal ArticleDOI
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.

426 citations

Journal ArticleDOI
TL;DR: The main contribution of the paper is to highlight the development of the KNOMAD methodology and to substantiate its individual steps with sufficient detail to support the application of KNOMad in practice.
Abstract: Existing knowledge-based engineering methodologies offer opportunities for improvement, as the multidisciplinary character of engineering design is not well implemented and as the current methodologies are not optimally substantiated. To better address the integration of multidisciplinary engineering knowledge within a knowledge based engineering (KBE) framework, the KNOMAD methodology has been devised. KNOMAD stands for Knowledge Nurture for Optimal Multidisciplinary Analysis and Design and is a methodology for the analytical utilization, development and evolution of multi-disciplinary engineering knowledge within the design and production realms. The KNOMAD acronym can also be used to highlight KNOMAD's formalized process of: (K)nowledge capture; (N)ormalisation; (O)rganisation; (M)odeling; (A)nalysis; and (D)elivery. These implementation steps are taken and repeated as part of the knowledge life cycle and in this context KNOMAD nurtures the whole Knowledge Management across that life cycle. The main contribution of the paper is to highlight the development of the KNOMAD methodology and to substantiate its individual steps with sufficient detail to support the application of KNOMAD in practice. A discipline-specific case study shows the potential of the KNOMAD methodology.

88 citations

Proceedings ArticleDOI
04 Sep 2002
TL;DR: TU Delft contributed to the project with the development of a Blended Wing-Body Multi-Model Generator, which is able to supply geometries and data to the analysis software, either COTS or tailor made, used by the various disciplinary groups in the project team.
Abstract: Aim of the EC sponsored project ‘Multidisciplinary Design and Optimization of Blended Wing-Bodies’ is the development and application of a fully integrated Computer Design Engine (CDE). TU Delft contributed to the project with the development of a Blended Wing-Body Multi-Model Generator, which is able to supply geometries and data to the analysis software, either COTS or tailor made, used by the various disciplinary groups in the project team (aerodynamics, structures, stability and control etc.). A full parametric definition of the aircraft has been implemented in the KTI ICAD environment. The ICAD Multi-Model Generator (or Generative Model) holds the ‘knowledge’ of the Blended Wing Body aircraft, such that consistent models can be generated, at different leve ls of fidelity, suitable for the various disciplines involved in the CDE. A large range of aircraft variants can be generated, just editing the values of the aircraft parameters, which are all collected in one single input file. The optimiser can change the parameters value within the optimisation loop, without the need for user interactive sessions. The generative model can be run in batch mode, even from remote sites.

87 citations

Journal ArticleDOI
TL;DR: The sequential optimization and mixed uncertainty analysis (SOMUA) method is used to decompose the traditional double-level reliability-based optimization problem into separate deterministic optimization and Mixed uncertainty analysis sub-problems, which are solved sequentially and iteratively until convergence is achieved.
Abstract: To address the reliability-based multidisciplinary design optimization (RBMDO) problem under mixed aleatory and epistemic uncertainties, an RBMDO procedure is proposed in this paper based on combined probability and evidence theory. The existing deterministic multistage-multilevel multidisciplinary design optimization (MDO) procedure MDF-CSSO, which combines the multiple discipline feasible (MDF) procedure and the concurrent subspace optimization (CSSO) procedure to mimic the general conceptual design process, is used as the basic framework. In the first stage, the surrogate based MDF is used to quickly identify the promising reliable regions. In the second stage, the surrogate based CSSO is used to organize the disciplinary optimization and system coordination, which allows the disciplinary specialists to investigate and optimize the design with the corresponding high-fidelity models independently and concurrently. In these two stages, the reliability-based optimization both in the system level and the disciplinary level are computationally expensive as it entails nested optimization and uncertainty analysis. To alleviate the computational burden, the sequential optimization and mixed uncertainty analysis (SOMUA) method is used to decompose the traditional double-level reliability-based optimization problem into separate deterministic optimization and mixed uncertainty analysis sub-problems, which are solved sequentially and iteratively until convergence is achieved. By integrating SOMUA into MDF-CSSO, the Mixed Uncertainty based RBMDO procedure MUMDF-CSSO is developed. The effectiveness of the proposed procedure is testified with one simple numerical example and one MDO benchmark test problem, followed by some conclusion remarks.

65 citations

Journal ArticleDOI
TL;DR: The architecture and the functionalities of the multimodel generator module are discussed, which is a knowledge-based engineering application developed to model the geometry of both conventional and novel aircraft configurations and to automate the generation of dedicated models for low- and high-fidelity analysis tools.
Abstract: This paper introduces the concept of the design and engineering engine, which is a modular computational design system to support distributed multidisciplinary design and optimization of aircraft. In particular, this paper discusses the architecture and the functionalities of the multimodel generator module, which is a knowledge-based engineering application developed to model the geometry of both conventional and novel aircraft configurations and to automate the generation of dedicated models for low- and high-fidelity analysis tools. This paper demonstrates the capability of the knowledge-based engineering approach to record and automate complex engineering design processes, such as the generation of models for finite element analysis. The time reduction gained by process automation, together with the enabled use of high-fidelity analysis tools earlier in the design process, constitute significant achievements toward a broader exploitation of the multidisciplinary design and optimization methodology, as well as the development of novel aircraft configurations.

65 citations


Cited by
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Journal ArticleDOI
TL;DR: The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods for large-scale building optimization problems; and reducing time and effort for such activities.

1,009 citations

Journal ArticleDOI
TL;DR: The prime aim of this review article is to demonstrate the recent development and emerging applications of natural cellulose fibers and their polymer materials.

775 citations

Book
01 Jan 2014
TL;DR: In this article, statistical models in engineering are used to evaluate the performance of statistical models for software engineering problems in the field of software engineering, including software engineering and software engineering..
Abstract: Statistical models in engineering , Statistical models in engineering , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

386 citations

Journal ArticleDOI
TL;DR: Key findings include the necessity for improved methodological support and adherence, the need for a quantitative framework to assess the viability and success of KBE development and implementation projects, and the opportunity to move towards mass customization approaches through distributed deployment of K BE in the extended enterprise.

362 citations