Surrogate modeling of advanced computer simulations using deep Gaussian processes
Not provided.
Engineering↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Enhancement of risk informed validation framework for external hazard scenario
In recent years, the U.S. Nuclear Regulatory Commission (USNRC) and the International Atomic Energy Agency (IAEA) have developed methodologies to assess the vulnerabilities of nuclear plants against site specific extreme hazards. In many cases, advanced simulation tools are being considered to simulate multi-physics, multi-scale phenomena and to evaluate vulnerability of nuclear facilities. The credibility of advanced simulation tools is assessed based on a formal verification, validation, and uncertainty quantification procedure. One of the key limitations in validation is the lack of relevant experimental data at system-level. This limitation leads to a decrease in the confidence of system-level risk predictions. Therefore, a robust validation framework is needed to formalize the confidence in predictive capability of advanced simulation results. Additionally, this study enhances the existing risk informed validation methodology, originally proposed by Kwag et al. [1] and Bodda et al. [2], by developing additional attributes and a new set of validation indicies for a complete and wider applicability of the framework. In this manuscript, the methodology to identify the critical path that leads to the system-level failure is illustrated. The overall validation is checked for completeness and consistency by comparing the critical path for both the system-level simulation and experimental models. The applicability of the code for an intended application is represented in terms of various maturity levels and helps in the process of decision making.
42 ENGINEERING↗
Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials
This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.
36 MATERIALS SCIENCE↗
Identifying human failure events (HFEs) for external hazard probabilistic risk assessment
In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.
42 ENGINEERING↗
Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis
Machine learning (ML) models are gaining popularity in structural health monitoring (SHM) because of their ability to learn the complex relationship between damage and sensor data. However, the lack of sufficient experimental data for structures with different degrees of damage is a key problem in training ML models for SHM. This problem can be alleviated by using physics-based models to generate the required training data to build physics-informed ML (PIML) models for SHM. However, it takes significant computational effort to perform enough high-fidelity simulations of the diagnostic test. It is thus desirable to know whether the available computational resource budget should be expended on numerous low-fidelity physics simulations, or a small number of high-fidelity simulations, or their combination. In this paper, we investigate this aspect of generating adequate training data for PIML, by constructing multi-fidelity PIML models. We evaluate the performance of several PIML models, trained with different amounts of low-fidelity and high-fidelity data, in locating hidden cracks in concrete structures using a nonlinear dynamics-based diagnosis technique. Here, we find that high-fidelity physics simulations that do not cover the (test and damage) parameter space do not improve the performance of diagnostic PIML models built using data from many low-fidelity physics simulations.
42 ENGINEERING↗
Data-based and secure switched cyber–physical systems
In this work, we develop a completely model-free moving target defense framework for the detection and mitigation of sensor and/or actuator attacks in cyber–physical systems with dynamics that evolve in discrete-time. We incorporate an intrusion detection mechanism based on an approximate dynamic programming technique that learns the policies for optimal regulation and optimal tracking while simultaneously defending against actuator and sensor attacks in a model-free fashion. Switching rules are leveraged to force proactive and reactive defense mechanisms as well as, guarantee the stability of the equilibrium point. Finally, as a case study, we apply the proposed moving target defense framework to a DC–DC converter that is used in electric vehicles.
42 ENGINEERING↗
The infinite level normal forms for non-resonant double Hopf singularities
Here, in this paper, we explore hypernormal forms of vector fields that have non-resonant double Hopf singularities with a non-zero radial cubic part. Our primary focus is on investigating the infinite-level normal form classification of this type of singularities. We provide a normal form decomposition in terms of planar-rotating and planar-radial vector fields, which greatly facilitate the pattern recognition and analysis of the corresponding generalized homological maps. Notably, our paper represents the first instance of the normal form classification for general non-resonant double Hopf singularities without structural symmetry.
97 MATHEMATICS AND COMPUTING↗
The impact of autonomous vehicles on commute ridesharing with uncertain work end time
The uncertainty in work end time can prevent ridesharing between two commuters with identical home and work locations. This effect can be alleviated by autonomous vehicles (AV): when two commuters share a ride from home to work, if the realized work end times result in a long wait between the two, an additional AV can be easily repositioned from home to work, whereas with regular human-driven vehicles one has to either bear the long wait or request expensive taxi service. This observation implies that AVs have great potential for increasing commute ridesharing. To study this effect, we develop evening commute bottleneck models with uncertain work end time and establish mode choice equilibrium for the round-trip commute. We find that, with regular vehicles, because of the expected waiting cost caused by work end time uncertainty, commuters typically do not share rides even though they have the same home and work locations. With AVs, commuters always share rides for the morning commute, and they also share rides for the evening commute if their work end times turn out to be close; otherwise, they reposition an additional AV to pick one of them up. Finally, our numerical examples show that AVs can encourage commute ridesharing and significantly reduce the expected round-trip travel costs for commuters.
97 MATHEMATICS AND COMPUTING↗
Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?
Not provided.
Business & Economics↗
Unprecedented Shifts in Hydrology Are Emerging Across California's Critical Basins: An Evaluation From 0.5 to 3.5°C
With advances in climate models and downscaling techniques, stakeholders anticipate high-resolution analysis to inform regional to local changes in water management. Here, we produce hydrologic projections from an ensemble of Earth System Models (ESMs) that were selected and downscaled to support California's 5th Climate Assessment. An ensemble of 19 ESMs was downscaled to a 3-km resolution across California using a statistical-dynamical downscaling approach and subsequently run through two calibrated hydrology models. Although California has been extensively studied in the context of climate change, we provide the first evaluation of the warming thresholds at which hydroclimate metrics demonstrate statistically significant shifts. We show that present-day to near-term warming levels in Klamath and Northern Sierra Nevada basins, which serve as a critical source of water for California, show statistically significant decreases in snowfall and peak snowpack and associated decreases in summer snowmelt and runoff. More generally, shifts in these hydroclimate metrics occur for intermediate elevation basins ranging from 1,315 to 1,455 m (4,314.3–4,773.6 ft), while the warming level of emergence is delayed for lower and higher elevation basins. We also find that several basins already demonstrate significant increases in 5- to 100-year runoff intensities, primarily due to the increasing influence of precipitation falling as rain. Hydroclimate metrics with trends that demonstrate near-term warming levels of emergence are reflected in reanalysis-based observations, suggesting California is entering a fundamentally different hydroclimate regime. While this will likely stress California's water management, the research provided can support when to implement adaptation efforts.
Bass, B. [Univ. of California, Los Angeles, CA (Un↗
The need for reproducible research in soft robotics
In recent years we have witnessed the rise of commercialization efforts for soft robotic technology, including soft grippers (Soft Robotics, Inc.), stretchable sensors (StretchSense, Inc. and Lightlace, Inc.), and platforms for human-robot interaction (Festo Inc., Meta Reality Labs, Toyota Research Institute, and Disney Research). However, commercialization as a whole lags the trends enjoyed by other robotic technology at equivalent points in their respective lifecycles.
43 PARTICLE ACCELERATORS↗