ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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This paper presents a review of the state of the art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such as energy generation and transport systems. The history of the digital twin is discussed first, along with a review of the associated literature; the process of synthesizing a digital twin is then considered, including definition of the aims and objectives of the digital twin. An example of the asset management phase for a wind turbine is included in order to demonstrate how the synthesis process might be applied in practice. In order to illustrate modeling issues arising in the construction of a digital twin, a detailed case study is presented, based on a physical twin, which is a small-scale three-story structure. This case study shows the progression toward a digital twin highlighting key processes including system identification, data-augmented modeling, and verification and validation. Finally, a discussion of some open research problems and technological challenges is given, including workflow, joints, uncertainty management, and the quantification of trust. In a companion paper, as part of this special issue, a mathematical framework for digital twin applications is developed, and together the authors believe this represents a firm framework for developing digital twin applications in the area of engineering dynamics. This article is available in the ASME Digital Collection at .

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last decade, and has been identified by many industries as a highly desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models; most importantly, governing how they are verified and validated, and how validation information can be transferred between models. Unlike its companion, which does not attempt detailed specification of any twin components, this paper will attempt to outline a rigorous representation theory of models, based on the introduction of two new concepts: mirrors and virtualizations. The paper is not intended as a passive wish list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science, and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework. This article is available in the ASME Digital Collection at .

This study assesses the seismic responses of viaduct structures in the Japanese high-speed rail system under moderate earthquake forces considering the slip between tracks and wheels. Equations of motion for the train–bridge interaction system were derived, where the track–wheel interaction was described by the Coulomb friction law. A full train–bridge finite element model incorporating nonslip and stick–slip interaction models was built using commercial finite element analysis software: abaqus. Simulation results indicate that the slip phenomenon might occur under a moderate earthquake and that a conventional nonslip model with an infinitely large friction coefficient is inappropriate. A parametric study revealed that the braking-train-induced slip friction little influenced the bridge response to moderate earthquake forces. The bridge's dynamic motions were dominated by ground motion irrespective of the values assigned as the train's initial speed and track–wheel friction coefficient. A computationally efficient method was proposed for calculating the longitudinal seismic responses of a bridge interacting with a braking train, following the linear superposition principle. As illustrated, this method could be helpful in reliability or uncertainty analysis when a great number of computationally expensive seismic analyses are required for train–bridge interaction systems. This article is available in the ASME Digital Collection at .

Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical response. This problem is aggravated by limited availability of clean data, uncertainty in available physics-based models and additional logistic and computational expense associated with experiments. In such a scenario, Bayesian methods have played an impactful role in alleviating the aforementioned obstacles by quantifying uncertainty of different types under limited resources. These methods, usually deployed as a framework, allows decision makers to make informed choices under uncertainty while being able to incorporate information on the fly, usually in the form of data, from multiple sources while being consistent with the physical intuition about the problem. This is a major advantage that Bayesian methods bring to fruition especially in the industrial context. This paper is a compendium of the Bayesian modeling methodology that is being consistently developed at GE Research. The methodology, called GE's Bayesian hybrid modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years. In this work, we explain the various advancements in GEBHM's methods and demonstrate their impact on several challenging industrial problems. This article is available in the ASME Digital Collection at .

The aim of this work is to propose an indicator, based on the info-gap approach, which assesses the robustness of the dynamic response of the model of a structure to lack of knowledge in the associated eigensolutions. The info-gap uncertainty model for the eigensolutions is constructed mode by mode based on a set of experimentally identified eigensolutions obtained from tests on a set of nominally identical structures. A robustness analysis is then performed, which provides a useful bound to the maximum response levels that are consistent with the defined uncertainty, thus allowing to extrapolate to a population of untested structures. The proposed methodology is validated experimentally on a simple structure composed of two plates clamped together on one side. Uncertainty is introduced by adding lumped masses at random locations. A subset of these test results is used to construct the info-gap model of the eigenproperties and the remaining data are used to confirm that the robustness curve usefully bounds the observed maximum responses. This article is available in the ASME Digital Collection at .

The ASME pressure vessels for human occupancy (PVHO) codes and standards are engineering standards developed to provide a reliable design method for pressure vessel windows. This empirical method is based primarily on years of government-sponsored testing and development and does not directly use engineering theory. This empirical algorithm makes it challenging to revise without additional large-scale physical testing. The industries using the PVHO code need a way to incorporate advances in material science, manufacturing technology, and overall engineering advances without spending years in code case review. Verification and validation techniques, coupled with stochastic finite element analysis (FEA) to address operational variables, can be the basis for a “design by analysis” method to complement the existing testing requirements to produce a full engineering package consistent with other pressure vessel and pressure vessel component design. A design method sufficiently reliable for PVHO could be used in other applications. This article is available in the ASME Digital Collection at .

Failure mode and symptoms analysis (FMSA) is a relatively new and still not very much employed variation of failure modes, effects and criticality analysis (FMECA), a technique broadly used in reliability, safety, and quality engineering. While FMECA is an extension of the well-known failure mode and effects analysis (FMEA) method, primarily used when a criticality analysis is required, FMSA focuses on the symptoms produced by each considered failure mode and the selection of the most appropriate detection and monitoring techniques and strategies, maximizing the confidence level in the diagnosis and prognosis. However, in the same way as FMECA and FMEA, FMSA inherits some deficiencies, presenting somewhat biased results and uncertainties intrinsic to its development, due to its own algorithm and the dependence on knowledge-based inputs from experts. Accordingly, this article presents a fuzzy logic application as a complement to FMSA in order to mitigate such uncertainties' effects. As a practical example, the method is applied to a Kaplan turbine shaft system. The monitoring priority number (MPN) obtained through FMSA is compared to the fuzzy monitoring priority number (FMPN) resulting from fuzzy logic application, demonstrating how the proposed method improves the evaluation of detection and monitoring techniques and strategies. This article is available in the ASME Digital Collection at .

A common issue in energy allocation problems is managing the tradeoff between selling surplus energy to maximize short-term revenue, versus holding surplus energy to hedge against future shortfalls. For energy allocation problems, this surplus represents resource flexibility. The decision maker has an option to sell or hold the flexibility for future use. As a decision in the current period can affect future decisions significantly, future risk evaluation of uncertainties is recommended for the current decision in which a traditional robust optimization is not efficient. Therefore, an approach to flexible-robust optimization has been formulated by integrating a real options (RO) model with the robust optimization framework. In the energy problem, the real option model evaluates the future risk, and provides the value of holding flexibility, whereas the robust optimization quantifies uncertainty and provides a robust solution of net revenue by selling flexibility. This problem is solved using bilevel programming and a complete general mathematical formulation of bilevel flexible-robust optimization model is presented for multireservoir systems and results shown to provide an efficient decision making process in energy sectors. To reduce the computational expense, mathematical techniques have been used in the proposed model to reduce the dimension in the quantification and propagation of uncertainties. This article is available in the ASME Digital Collection at .

The growing complexity of equipment and systems has motivated the search for automated methods of fault diagnosis. Fault diagnosis represents the process of identifying the origin of a fault through the observation of a series of effects that it causes in the system. The method proposed in this paper for system fault diagnosis takes advantage of two very different techniques: Bayesian networks (BN) and systems modeling language (SysML). SysML allows the modeling of requirements, structure, behavior and parameters to provide a robust description of a system, its components, and its environment. This system model is used, in the proposed method, to obtain the BN graph in a novel structured procedure. The BN graph obtained must, in turn, present the components that are most likely responsible for a certain fault of the system under study. The BN model uses components reliabilities to solve the diagnosis problem. A case study of a water storage system is presented and it shows how the method can contribute to an assessment of the monitoring process of a system even in the early stages of its design. With this kind of information, the designer can assess the need for changes in the system to make it more reliable or better monitored. This article is available in the ASME Digital Collection at .

A new finite element method is presented for the analysis of uncertain heat transfer problems using universal gray number theory. The universal gray number representation involves normalization of the uncertain parameters based on their lower and upper bound values with its own distinctive rules of arithmetic operations which makes this method distinctive from conventional interval analysis approaches. This work introduces the concept of fuzzy finite element-based heat transfer analysis using universal gray number theory, that compared to the interval-based fuzzy analysis, would yield significantly improved and more accurate results. Heat transfer problems, including a one-dimensional tapered fin, a two-dimensional hollow rectangle representing a thin slice of a chimney of a thermal power plant, and a three-dimensional (axisymmetric) solid body with different boundary conditions, were considered for the uncertainty analysis. It is shown that, in each case, the interval values of the response parameters given by the universal gray number theory are consistent with the ranges of the input parameters, compared to those given by the interval analysis. It is also revealed that universal gray number theory is more inclusive and less computationally intensive compared to the interval analysis. This article is available in the ASME Digital Collection at .