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

Multi-parameter-path length method for resilience (MP-PLMR) has been proposed to determine the resilience of system multiparameter considerations. It was applied to two engineering situations: (i) Passive catalytic device for hydrogen management in nuclear power plant (NPP) and (ii) Engineered systems for hydrogen mitigation in NPP. The method involves normalizations of the system parameters, the time domain and correlation coefficient across the parameters. The path length for the transient was defined using all the parameters and their correlations. The resilience value in the two case studies depended on the number of parameters considered and correlations. System resilience without the consideration of correlation was also estimated. The difference between the correlated and uncorrelated resilience was significant. While there is no established metric against which the calculated values could be compared, these values can be used to define system effectiveness in conjunction with reliability of systems. This article is available in the ASME Digital Collection at .

Probabilistic failure risk analysis is frequently used in the airworthiness area, while efficient stress intensity factor (SIF) solutions are vital in its process. Universal weight function (UWF) is a method that has remarkable computational efficiency and high accuracy in SIF calculation. However, the concrete coefficients in the UWF for different geometries remain unknown, which hinders the subsequent application of the method. This article focuses on general off-center embedded cracks. The response surface method is used to construct the UWF database. The accuracy of the database is confirmed by comparing it with existing literature and the finite element method, although large errors are identified to be inevitable for certain stress. Gaussian process regression is further adopted for better fitting, and the R-square is over 0.96. In addition, the effect of the offset distance on SIFs is discussed for embedded cracks in a given plate. Results show that SIF changes are dependent on the plate boundary in the uniform stress field, while stress predominates the SIF changes in nonuniform stress fields. This article is available in the ASME Digital Collection at .

In order to impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date. This article is available in the ASME Digital Collection at .

Addressing safety concerns in commercial nuclear power plants (NPPs) often requires the use of advanced modeling and simulation (M&S) in association with the probabilistic risk assessment (PRA). Advanced M&S are also needed to accelerate the analysis, design, licensing, and operationalization of advanced nuclear reactors. However, before a simulation model can be used for PRA, its validity must be adequately established. The objective of this research is to develop a systematic and scientifically justifiable validation methodology, namely, probabilistic validation (PV), to facilitate the validity evaluation (especially when validation data are not sufficiently available) of advanced simulation models that are used for PRA in support of risk-informed decision-making and regulation. This paper is the first in a series of two papers related to PV that provides the theoretical foundation and methodological platform. The second paper applies the PV methodological platform for a case study of fire PRA of NPPs. Although the PV methodology is explained in the context of PRA of the nuclear industry, it is grounded on a cross-disciplinary review of literature and so applicable to validation of simulation models, in general, not necessarily associated with PRA or nuclear applications. This article is available in the ASME Digital Collection at .

Robust design optimization of stochastic black-box functions is a challenging task in engineering practice. Crashworthiness optimization qualifies as such problem especially with regards to the high computational costs. Moreover, in early design phases, there may be significant uncertainty about the numerical model parameters. Therefore, this paper proposes an adaptive surrogate-based strategy for robust design optimization of noise-contaminated models under lack-of-knowledge uncertainty. This approach is a significant extension to the robustness under lack-of-knowledge method (RULOK) previously introduced by the authors, which was limited to noise-free models. In this work, it is proposed to use a Gaussian Process as a regression model based on a noisy kernel. The learning process is adapted to account for noise variance either imposed and known or empirically learned as part of the learning process. The method is demonstrated on three analytical benchmarks and one engineering crashworthiness optimization problem. In the case studies, multiple ways of determining the noise kernel are investigated: (1) based on a coefficient of variation, (2) calibration in the Gaussian Process model, (3) based on engineering judgment, including a study of the sensitivity of the result with respect to these parameters. The results highlight that the proposed method is able to efficiently identify a robust design point even with extremely limited or biased prior knowledge about the noise. This article is available in the ASME Digital Collection at .

Federal regulations require refuge alternatives (RAs) in underground coal mines to provide a life-sustaining environment for miners trapped underground when escape is impossible. A breathable air supply is among those requirements. For built-in-place (BIP) RAs, a borehole air supply (BAS) is commonly used to supply fresh air from the surface. Federal regulations require that such a BAS must supply fresh air at 12.5 cfm or more per person to maintain the oxygen concentration between 18.5% and 23% and carbon dioxide level below the 1% limit specified. However, it is unclear whether 12.5 cfm is indeed needed to maintain this carbon dioxide level. The minimal fresh air flow (FAF) rate needed to maintain the 1% CO₂ level will depend on multiple factors, including the number of people and the volume of the BIP RA. In the past, to predict the interior CO₂ concentration in an occupied RA, 96-h tests were performed using a physical human breathing simulator. However, given the infinite possibility of the combinations (number of people, size of the BIP RA), it would be impractical to fully investigate the range of parameters that can affect the CO₂ concentration using physical tests. In this paper, researchers at the National Institute for Occupational Safety and Health (NIOSH) developed a model that can predict how the %CO₂ in an occupied confined space changes with time given the number of occupants and the FAF rate. The model was then compared to and validated with test data. The benchmarked model can be used to predict the %CO₂ for any number of people and FAF rate without conducting a 96-h test. The methodology used in this model can also be used to estimate other gas levels within a confined space. This article is available in the ASME Digital Collection at .