Resilience decision-making for complex systems

Complex systems-such as gas turbines, industrial plants, and infrastructure networks- are of paramount importance to modern societies. However, these systems are subject to various threats. Novel research does not only focus on monitoring and improving the robustness and reliability of systems but also focus on their recovery from adverse events. The concept of resilience encompasses these developments. Appropriate quantitative measures of resilience can support decision-makers seeking to improve or to design complex systems. In this paper, we develop comprehensive and widely adaptable instruments for resilience-based decision-making. Integrating an appropriate resilience metric together with a suitable systemic risk measure, we design numerically efficient tools aiding decision-makers in balancing different resilience-enhancing investments. The approach allows for a direct comparison between failure prevention arrangements and recovery improvement procedures, leading to optimal tradeoffs with respect to the resilience of a system. In addition, the method is capable of dealing with the monetary aspects involved in the decision-making process. Finally, a grid search algorithm for systemic risk measures significantly reduces the computational effort. In order to demonstrate its wide applicability, the suggested decision-making procedure is applied to a functional model of a multistage axial compressor, and to the U-Bahn and S-Bahn system of Germany's capital Berlin. Copyright © 2020 by ASME

The concept of diagonal approximated signature: new surrogate modeling approach for continuous-state systems in the context of resilience optimization

The increasing size and complexity of modern systems presents engineers with the inevitable challenge of developing more efficient yet comprehensive computational tools that enable sound analyses and ensure stable system operation. The previously introduced resilience framework for complex and sub-structured systems provides a solid foundation for comprehensive stakeholder decision-making, taking into account limited resources. In their work, a survival function approach based on the concept of survival signature models the reliability of system components and subsystems. However, it is limited to a binary component and system state consideration. This limitation needs to be overcome to ensure comprehensive resilience analyses of real world systems. An extension is needed that guarantees both maintaining the existing advantages of the original resilience framework, yet enables continuous performance consideration. This work introduces the continuous-state survival function and concept of the Diagonal Approximated Signature (DAS) as a corresponding surrogate model. The proposed concept is based on combinatorial decomposition adapted from the concept of survival signature. This allows for the advantageous property of separating topological and probabilistic information. Potentially high-dimensional coherent structure functions are the foundation. A stochastic process models the time-dependent degradation of the continuous-state components. The proposed approach enables direct computation of the continuous-state survival function by means of an explicit formula and a stored DAS, avoiding costly online Monte Carlos Simulation (MCS) and overcoming the limitation of a binary component and system state consideration during resilience optimization for sub-structured systems. A proof of concept is provided for multi-dimensional systems and an arbitrary infrastructure system.</jats:p

Multidimensional resilience decision-making for complex and substructured systems

Complex systems, such as infrastructure networks, industrial plants and jet engines, are of paramount importance to modern societies. However, these systems are subject to a variety of different threats. Novel research focuses not only on monitoring and improving the robustness and reliability of systems, but also on their recoverability from adverse events. The concept of resilience encompasses precisely these aspects. However, efficient resilience analysis for the modern systems of our societies is becoming more and more challenging. Due to their increasing complexity, system components frequently exhibit significant complexity of their own, requiring them to be modeled as systems, i.e., subsystems. Therefore, efficient resilience analysis approaches are needed to address this emerging challenge. This work presents an efficient resilience decision-making procedure for complex and substructured systems. A novel methodology is derived by bringing together two methods from the fields of reliability analysis and modern resilience assessment. A resilience decision-making framework and the concept of survival signature are extended and merged, providing an efficient approach for quantifying the resilience of complex, large and substructured systems subject to monetary restrictions. The new approach combines both of the advantageous characteristics of its two original components: A direct comparison between various resilience-enhancing options from a multidimensional search space, leading to an optimal trade-off with respect to the system resilience and a significant reduction of the computational effort due to the separation property of the survival signature, once a subsystem structure has been computed, any possible characterization of the probabilistic part can be validated with no need to recompute the structure. The developed methods are applied to the functional model of a multistage high-speed axial compressor and two substructured systems of increasing complexity, providing accurate results and demonstrating efficiency and general applicability

Metastability in better-than-worst-case designs

Abstract Better-Than-Worst-Case-Designs use timing speculation to run with a cycle period faster than the one required for worst-case conditions. This speculation may produce timing violations and metastability that result in failures and non-deterministic timing behavior. The effects of these phenomena are not always well understood by designers and researchers in this area

Efficient reliability analysis of complex systems in consideration of imprecision

In this work, the reliability of complex systems under consideration of imprecision is addressed. By joining two methods coming from different fields, namely, structural reliability and system reliability, a novel methodology is derived. The concepts of survival signature, fuzzy probability theory and the two versions of non-intrusive stochastic simulation (NISS) methods are adapted and merged, providing an efficient approach to quantify the reliability of complex systems taking into account the whole uncertainty spectrum. The new approach combines both of the advantageous characteristics of its two original components: 1. a significant reduction of the computational effort due to the separation property of the survival signature, i.e., once the system structure has been computed, any possible characterization of the probabilistic part can be tested with no need to recompute the structure and 2. a dramatically reduced sample size due to the adapted NISS methods, for which only a single stochastic simulation is required, avoiding the double loop simulations traditionally employed. Beyond the merging of the theoretical aspects, the approach is employed to analyze a functional model of an axial compressor and an arbitrary complex system, providing accurate results and demonstrating efficiency and broad applicability. © 2021 The Author

Virtual process for evaluating the influence of real combined module variations on the overall performance of an aircraft engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aero-thermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan high-pressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas-Path-Analysis for further modules, such as the com-pressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency ηHPT and capacity _mHPT)are particularly driven by the variation of the stagger angle. On the other hand, ηHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow _mHPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches

Virtual process for evaluating the influence of real combined module variations on the overall performance of an aircraft engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aero-thermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan high-pressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas-Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;η&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; and capacity &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mrow&gt;&lt;mml:mover&gt;&lt;mml:mi&gt;m&lt;/mml:mi&gt;&lt;mml:mo&gt;˙&lt;/mml:mo&gt;&lt;/mml:mover&gt;&lt;/mml:mrow&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt;) are particularly driven by the variation of the stagger angle. On the other hand, &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mi&gt;η&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow &lt;inline-formula&gt;&lt;mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"&gt;&lt;mml:msub&gt;&lt;mml:mrow&gt;&lt;mml:mover&gt;&lt;mml:mi&gt;m&lt;/mml:mi&gt;&lt;mml:mo&gt;˙&lt;/mml:mo&gt;&lt;/mml:mover&gt;&lt;/mml:mrow&gt;&lt;mml:mrow&gt;&lt;mml:mi&gt;H&lt;/mml:mi&gt;&lt;mml:mi&gt;P&lt;/mml:mi&gt;&lt;mml:mi&gt;T&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:math&gt;&lt;/inline-formula&gt; are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.</jats:p

Virtual Process for Evaluating the Influence of Real Combined Module Variations on the Overall Performance of an Aircraft Engine

The effects of real combined variances in components and modules of aero engines, due to production tolerances or deterioration, on the performance of an aircraft engine are analysed in a knowledge-based process. For this purpose, an aerothermodynamic virtual evaluation process that combines physical and probabilistic models to determine the sensitivities in the local module aerodynamics and the global overall performance is developed. Therefore, an automatic process that digitises, parameterises, reconstructs and analyses the geometry automatically using the example of a real turbofan highpressure turbine blade is developed. The influence on the local aerodynamics of the reconstructed blade is investigated via a computational fluid dynamics (CFD) simulations. The results of the high-pressure turbine (HPT) CFD as well as of a Gas- Path-Analysis for further modules, such as the compressors and the low-pressure turbine, are transferred into a simulation of the performance of the whole aircraft engine to evaluate the overall performance. All results are used to train, validate and test several deep learning architectures. These metamodels are utilised for a global sensitivity analysis that is able to evaluate the sensitivities and interactions. On the one hand, the results show that the aerodynamics (especially the efficiency hHPT and capacity m˙ HPT ) are particularly driven by the variation of the stagger angle. On the other hand, hHPT is significantly related to exhaust gas temperature (Tt5), while specific fuel consumption (SFC) and mass flow m˙ HPT are related to HPC exit temperature (Tt3). However, it can be seen that the high-pressure compressor has the most significant impact on the overall performance. This novel knowledge-based approach can accurately determine the impact of component variances on overall performance and complement experience-based approaches.</jats:p

Low-energy Pion-nucleon Scattering

This paper contains the results of an analysis of recent low-energy pion-nucleon scattering experiments. Obtained are phase shifts, the pion-nucleon coupling constant and an estimate of the Sigma term.Comment: 30 pages, 11 figures, LaTe

Developing country consumers’ acceptance of biofortified foods: a synthesis

The success of biofortified staple crops depends on whether they are accepted and consumed by target populations. In the past 8 years, several studies were undertaken to understand consumers’ acceptance of foods made with biofortified staple crops. Consumer acceptance is measured in terms of their sensory evaluation and economic valuation of biofortified varieties vis-à-vis conventional ones. These studies apply expert sensory panel and hedonic trait analyses methods adopted from food sciences literature, as well as various preference elicitation methods (including experimental auctions, revealed choice experiments, and stated choice experiments) adopted from experimental economics literature. These studies also test the impact of various levers on consumers’ evaluation and valuation for biofortified foods. These levers include (i) nutrition information and the media through which such information is conveyed; (ii) the length and content of nutrition information; (iii) different branding options; (iv) the nature (national or international) of the branding/certification agency that is endorsing the biofortified staple food; and (v) the nature (national or international) of the agency that is delivering the biofortified staple food. This paper brings together evidence on consumer acceptance of biofortified crops on 5 crops across 7 countries in Africa, Asia and Latin America. The results of these studies are expected to aid in the development of biofortified crops that consumers like, as well as in the development of appropriate marketing and consumer awareness or information campaigns to encourage the switch in consumption from traditional staples to biofortified ones