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Analytical Evaluation of Uncertainty Propagation for Probabilistic Design Optimisation

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Bag om Analytical Evaluation of Uncertainty Propagation for Probabilistic Design Optimisation

This monograph introduces several recently developed and applied analytical-based theoretical approaches and practical techniques for uncertainty evaluation and probabilistic design optimisation for the robustness and reliability of technical systems of various complexity and applications. This is enhanced by the inclusion of relevant real-world case studies.The book will be useful for researchers, engineers, and designers of complex, multi-parameter technical systems that must guarantee a specified level of functional reliability. It presents several real-world case studies helping to comprehend in detail the developed techniques and their benefits. In addition, it offers free access to the online toolboxes developed by the authors to support the calculation of uncertainties and guides the readers on the practical use of such tools.Key Features: Cohesively combines three important research, development, and real-world engineering practice areas (i) uncertainty and its evaluation, propagation, modelling, etc., and, (ii) complex technical systems design optimisation involving advanced probabilistic theories, techniques, and tools, and (iii) development of such theoriesThis is arguably the first monograph systematically and comprehensively presenting the new area of moment-based uncertainty propagation and its practical applications.The book offers a significant volume of additional explanations, guidelines and tools enabling the readers to utilise the presented material in their research, design, and engineering practice.It provides free access to developed application-specific online tools and benchmark test distributions for expanded uncertainty.The practical application section of the monograph presents several real-world case study solutions.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780750349291
  • Indbinding:
  • Hardback
  • Sideantal:
  • 275
  • Udgivet:
  • 16. juni 2023
  • Størrelse:
  • 264x186x16 mm.
  • Vægt:
  • 474 g.
  • BLACK NOVEMBER
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Leveringstid: 2-3 uger
Forventet levering: 11. december 2024

Beskrivelse af Analytical Evaluation of Uncertainty Propagation for Probabilistic Design Optimisation

This monograph introduces several recently developed and applied analytical-based theoretical approaches and practical techniques for uncertainty evaluation and probabilistic design optimisation for the robustness and reliability of technical systems of various complexity and applications. This is enhanced by the inclusion of relevant real-world case studies.The book will be useful for researchers, engineers, and designers of complex, multi-parameter technical systems that must guarantee a specified level of functional reliability. It presents several real-world case studies helping to comprehend in detail the developed techniques and their benefits. In addition, it offers free access to the online toolboxes developed by the authors to support the calculation of uncertainties and guides the readers on the practical use of such tools.Key Features: Cohesively combines three important research, development, and real-world engineering practice areas (i) uncertainty and its evaluation, propagation, modelling, etc., and, (ii) complex technical systems design optimisation involving advanced probabilistic theories, techniques, and tools, and (iii) development of such theoriesThis is arguably the first monograph systematically and comprehensively presenting the new area of moment-based uncertainty propagation and its practical applications.The book offers a significant volume of additional explanations, guidelines and tools enabling the readers to utilise the presented material in their research, design, and engineering practice.It provides free access to developed application-specific online tools and benchmark test distributions for expanded uncertainty.The practical application section of the monograph presents several real-world case study solutions.

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