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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Verification

This paper is focused on applying Monte Carlo simulation to probabilistic launch vehicle design and requirements verification. The approaches developed in this paper can be applied to other complex design efforts as well. Typically the verification must show that requirement "x" is met for at least "y" % of cases, with, say, 10% consumer risk or 90% confidence. Two particular aspects of making these runs for requirements verification will be explored in this paper. First, there are several types of uncertainties that should be handled in different ways, depending on when they become known (or not). The paper describes how to handle different types of uncertainties and how to develop vehicle models that can be used to examine their characteristics. This includes items that are not known exactly during the design phase but that will be known for each assembled vehicle (can be used to determine the payload capability and overall behavior of that vehicle), other items that become known before or on flight day (can be used for flight day trajectory design and go/no go decision), and items that remain unknown on flight day. Second, this paper explains a method (order statistics) for determining whether certain probabilistic requirements are met or not and enables the user to determine how many Monte Carlo samples are required. Order statistics is not new, but may not be known in general to the GN&C community. The methods also apply to determining the design values of parameters of interest in driving the vehicle design. The paper briefly discusses when it is desirable to fit a distribution to the experimental Monte Carlo results rather than using order statistics.

Hanson, John M.↗
DOE OSTI2019

Review and categorization of existing studies on the estimation of probabilistic failure metrics for Reactor Coolant Pressure Boundary piping and steam generator tubes in Nuclear Power Plants

This article presents the first output of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP), titled Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors (AWCRs), launched in 2018. This IAEA CRP is aimed at developing a good-practices document for estimating the probabilistic failure metrics of piping in AWCRs. The reliability of piping that comprise the Reactor Coolant Pressure Boundary (RCPB) is important for maintaining safe and stable operations of Nuclear Power Plants (NPPs) because failure of those piping components could lead to undesirable consequences, such as plant shutdown, costly repair, the occurrence of Loss-of-Coolant Accidents (LOCAs) and, possibly, subsequent core damage or large release events. Probabilistic failure metrics (e.g., failure rate, failure frequency, or failure probability) of RCPB components are the key inputs to the Probabilistic Safety/Risk Assessment (PSA/PRA) and risk management of NPPs. The estimation of probabilistic failure metrics, however, is challenging, especially for AWCRs, due to the lack of operating experience. Therefore, as the first step of the IAEA CRP activities, this paper is developed to provide a literature review of the existing studies (from 2000 to April 2019) on the estimation of probabilistic failure metrics for RCPB piping and Steam Generator (SG) tubes of NPPs and to categorize them based on four criteria: (1) explicitness of incorporation of physical failure mechanisms; (2) types of failure characterization; (3) types of physical models for degradation; and (4) explicitness of consideration of maintenance. The existing studies are also analyzed from the viewpoint of the following key aspects: (i) uncertainty analysis, (ii) sensitivity analysis, (iii) validation strategies, and (iv) the areas of applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗
DOE OSTI2013

Training and Research on Probabilistic Hydro-Thermo-Mechanical Modeling of Carbon Dioxide Geological Sequestration in Fractured Porous Rocks

Colorado School of Mines conducted research and training in the development and validation of an advanced CO 2 GS (Geological Sequestration) probabilistic simulation and risk assessment model. CO 2 GS simulation and risk assessment is used to develop advanced numerical simulation models of the subsurface to forecast CO 2 behavior and transport; optimize site operational practices; ensure site safety; and refine site monitoring, verification, and accounting efforts. As simulation models are refined with new data, the uncertainty surrounding the identified risks decrease, thereby providing more accurate risk assessment. The models considered the full coupling of multiple physical processes (geomechanical and fluid flow) and describe the effects of stochastic hydro-mechanical (H-M) parameters on the modeling of CO 2 flow and transport in fractured porous rocks. Graduate students were involved in the development and validation of the model that can be used to predict the fate, movement, and storage of CO 2 in subsurface formations, and to evaluate the risk of potential leakage to the atmosphere and underground aquifers. The main major contributions from the project include the development of: 1) an improved procedure to rigorously couple the simulations of hydro-thermo-mechanical (H-M) processes involved in CO 2 GS; 2) models for the hydro-mechanical behavior of fractured porous rocks with random fracture patterns; and 3) probabilistic methods to account for the effects of stochastic fluid flow and geomechanical properties on flow, transport, storage and leakage associated with CO 2 GS. The research project provided the means to educate and train graduate students in the science and technology of CO 2 GS, with a focus on geologic storage. Specifically, the training included the investigation of an advanced CO 2 GS simulation and risk assessment model that can be used to predict the fate, movement, and storage of CO 2 in underground formations, and the evaluation of the risk of potential CO 2 leakage to the atmosphere and underground aquifers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Ensemble-based flood vulnerability assessment for probable maximum flood in a changing environment

The magnitude and frequency of hydro-meteorological extremes are expected to increase in a changing environment in ways that threaten the security of US energy-water assets. These include probable maximum precipitation (PMP) and probable maximum flood (PMF), which are used as hydraulic design standards for highly sensitive infrastructures such as nuclear power plants and main dams. To assess the flood vulnerability due to PMP/PMF, an integrated high-resolution process-based hydro-meteorologic modeling framework was used to develop ensemble-based probabilistic flood maps based on best-available historic observations and future climate projections. A graphics processing unit–accelerated 2-dimensional hydrodynamic model was used to simulate the surface inundation areas corresponding to a total of 120 PMF hydrographs. These ensemble-based PMF maps were compared with flood maps obtained from the conventional deterministic PMP/PMF approach, revealing added information about conditional probability of flooding. Further, a relative sensitivity test was conducted to explore the effects of various factors in the framework, such as meteorological forcings, antecedent hydrologic conditions, reservoir storage, and flood model input resolution and parameters. In conclusion, the proposed framework better illustrates the uncertainties associated with model inputs, parameterization, and hydro-meteorological factors, allowing more informed decision-making for future emergency preparation.

58 GEOSCIENCES↗
DOE OSTI2017

Geomechanics-Based Stochastic Analysis of Injection- Induced Seismicity

The production of geothermal energy from dry and low permeability reservoirs is achieved by water circulation in natural and/or man-made fractures, and is referred to as enhanced or engineered geothermal systems (EGS). Often, the permeable zones have to be created by stimulation, a process which involves fracture initiation and/or activation of discontinuities such as faults and joints due to pore pressure and the in-situ stress perturbations. The stimulation of a rock mass is often accompanied by multiple microseismic events. Micro-seismic events associated with rock failure in shear, and shear slip on new or pre-existing fracture planes and possibly their propagations. The microseismic signals contain information about the sources of energy that can be used for understanding the hydraulic fracturing process and the created reservoir properties. Detection and interpretation of microseismic events is useful for estimating the stimulated zone, created reservoir permeability and fracture growth, and geometry of the geological structures and the in-situ stress state. The process commonly is referred to as seismicity-based reservoir characterization (SBRC). Although, progress has been made by scientific & geothermal communities for quantitative and qualitative analysis of reservoir stimulation using SBRC several key questions remain unresolved in the analysis of micro-seismicity namely, variation of seismic activity with injection rate, delayed micro-seismicity, and the relation of stimulated zone to the injected volume and its rate, and the resulting reservoir permeability. In addition, the current approach to SBRC does not consider the full range of relevant poroelastic and thermoelastic phenomena and neglects the uncertainty in rock properties and in-situ stress in the data inversion process. The objective of this research and technology developments was to develop a 3D SBRC model that addresses these shortcomings by taking into account hydro-thermo-poro-mechanical mechanisms associated with injection and utilizing a state-of-the-art stochastic inversion procedure. The approach proposed herein is innovative and significantly improves the existing SBCR technology (e.g., Shapiro et al. 2003) for geothermal reservoirs in several ways. First, the current scope of the SBRC is limited with respect to the physical processes considered and the rock properties used. Usually, the geomechanics analyses within SBRC is limited to the pore pressure diffusion in the rock mass, which is modeled using a time-dependent parabolic equation and solved using a finite element algorithm with either a line or a point source. However, water injection induces both poroelastic and thermoelastic stresses in the rock mass which affect the stress state. In fact, it has been suggested that thermoelastic stresses can play a dominant role in reservoir seismicity (Ghassemi et al., 2007). We include these important effects by using a fully-coupled poro-thermoelastic constitutive equations for the rock mass which will be solved using a 3D finite element model with more realistic injection geometries such as multiple injection/extraction sources (and in fractures), uncertainty in the material parameters and the in-situ stress distribution to better reflect the pore pressure and stress distributions. In addition, we developed a 3D stochastic fracture network model to study MEQ generation in fracture rocks. The model was verified using laboratory experiments, and calibrated and applied to Newberry EGS stimulation. In previous SBRC approaches, the triggering of micro-seismicity is modeled base on the assumption that the prior stochastic criticality model of the rock mass is a valid and adequate description. However, this assumption often does not hold in the field. Thus, we improved upon the current SBRC approach by using the micro-seismic responses to estimate the hydraulic diffusivity as well as the criticality distribution itself within the field. In this way, instead of relying on our a priori knowledge of criticality distribution, we combine an initial probabilistic description of criticality with the information contained in microseismic measurements to arrive at criticality solutions that are conditioned on both field data and our prior knowledge. Previous SBRC have relied upon a deterministic inversion approach to estimate the permeability, and the extent of the stimulated zone, whereas a stochastic inversion algorithm that recognizes and quantifies the uncertainties in the prior model, the time evolution of pore pressure distributions (modeling errors), and the observed seismic events is developed and used herein to realistically assess the quality of the solution. Finally, we developed a technique for processing discrete MEQ data to estimate fracture network properties such as dip and dip directions. The approach was successfully applied to the Fenton Hill HRD experiment and the Newberry EGS with results in good agreement with field observations.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

Accounting for Point Estimate Uncertainty in Space Systems Reliability and Risk Analysis

Understanding and accounting for uncertainty in risk analysis is a critical step in the management and communication of risk in engineered systems. The component and system-level analysis to determine the probability of a negative outcome and its consequence is often quantified by a point estimate. Many Program and Enterprise decisions involving technical concerns and issues rely on reliability engineering activities to produce quantified risk analysis to inform the decision making process. At NASA, it is common to use a Probabilistic Risk Analysis (PRA) to inform the overall risk to Loss of Mission or Loss of Crew that involves integration across all spacecraft subsystem fault trees to produce an overall probability of mission failure. The point estimate is an estimate of this overall probability and is an immediate result of a fault tree model. It is the result of a model where the probability of each event is taken to be equal to its mean. The value provides an approximation of the overall mean without running any uncertainty calculations (e.g., no sampling). Using only the point estimate can lead to a false sense of precision and the point estimate may not match the resulting mean when uncertainty is taken into consideration. This paper will explore five conditions that can cause the PRA model mean to diverge from the point estimate and will provide engineers and managers insight into the importance of understanding uncertainty in the elements of PRA models.

Paul J Collier↗
DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

Here, dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Application of Nonlinear Seismic Soil-Structure Interaction Analysis for Identification of Seismic Margins at Nuclear Power Plants

Seismic probabilistic risk assessment (SPRA) methods and approaches at nuclear power plants (NPP) were first developed in the 1970s and aspects of them have matured over time as they were applied and incrementally improved. SPRA provides information on risk and risk insights and allows for some accounting for uncertainty and variability. As a result, SPRA is now used as an important basis for risk-informed decision making for both new and operating NPPs in the US and in an increasing number of countries globally. SPRAs are intended to provide best estimates of the various combinations of structural and equipment failures that can lead to a seismic induced core damage event. However, in some instances the current SPRA approach contains large uncertainties, and potentially masks other important events (for instance, it was not the seismic motions that caused the Fukushima core melt events, but the tsunami ingress into the facility). INL has an advanced SPRA research and development (R&D) activity that will identify areas in the calculation process that contain significant uncertainties. One current area of focus is the use of nonlinear soil-structure interaction (NLSSI) analysis methods to accurately capture: 1) nonlinear soil behavior and 2) gapping and sliding between the NPP and soil. The goal of this study is to compare numerical NLSSI analysis results with recorded earthquake ground motions at Fukushima Daichii (Great Tohuku Earthquake) and evaluate the sources of nonlinearity contributing to the observed reduction in peak acceleration. Comparisons are made using recorded data in the free-field (soil column with no structural influence) and recorded data on the NPP basemat (in-structure response). Results presented in this study should identify areas of focus for future R&D activities with the goal of minimizing uncertainty in SPRA calculations. This is not a validation activity since there are too many sources of uncertainty that a numerical analysis would need to consider (variability in soil material properties, structural material properties, etc.). Rather the report will determine if the NLSSI calculations are following similar trends observed in the recorded data (i.e. reductions in maximum acceleration between the free-field and basemat) Numerical NLSSI results presented show maximum accelerations between the free field and basemat were reduced the EW and NS directions. The maximum acceleration in the UD direction increased slightly. The largest reduction in maximum accelerations between the modeled free-field and the NPP basemat resulted in nearly 50% reduction. The observation in reduction of numerical maximum accelerations in the EW and NS directions follows the observed trend in the recorded data. The maximum reductions observed in these NLSSI studies were due to soil nonlinearities, not gapping and sliding (although additional R&D is needed to develop an appropriate approach to model gapping and sliding). This exploratory study highlights the need for additional R&D on developing: (i) improved modeling of soil nonlinearities (soil constitutive models that appropriately capture cyclic soil behavior), (ii) improved modeling of gapping and sliding at the soil-structure interface (to appropriately capture the dissipation of energy at this interface), and (iii) experimental laboratory test data to calibrate the items (i) and (ii).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Estimating building occupancy: a machine learning system for day, night, and episodic events

Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

GDSA Framework Development and Process Model Integration FY2022

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model’s predictions and estimate the growth of uncertainties in the region far from measurements.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2010

JSC Safety and Mission Assurance Data Analysis Overview

These slides describe the data analysis methods that are used to determine inputs for probabilistic risk models supporting the Space Shuttle Program. Other applications can follow a similar path probably using different data sources. Statistical approaches are different and not addressed here. Topics included here: 1) Prior Distribution; 2) Likelihood Data; 3) Bayesian Updating; and 4) Uncertainty and Error. Note: This is a high-level discussion and is not intended to be a tutorial.

Roelant, Henk↗
NASA NTRS2023

Efficient Calibration of Expensive Computational Models

Accounting for uncertainty when calibrating expensive computational models is a common challenge faced by scientists and engineers. Often Bayesian techniques are adopted to estimate a probability density function over the model parameters given noisy empirical data. The methods used to perform this type of probabilistic calibration are computationally prohibitive in that they require a large number of evaluations of the expensive model. In these cases, surrogate modeling -- that is, using a fast-to-evaluate, lower fidelity stand-in for the original computational model -- may be the only option to alleviate this computational burden. However, the upfront cost of generating training data to build a surrogate model can itself be expensive. As such, it is important to be judicious when selecting training points at which the full-fidelity model is evaluated. Here, an active learning approach is proposed that enables efficient selection of training points using approximate samples of the calibrated parameter probability density function. In this way, the training points can be concentrated in regions where the calibration algorithm requires high model accuracy.

active learning↗
DOE OSTI2014

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

42 ENGINEERING↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗