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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Distributionally Safe Path Planning: Wasserstein Safe RRT

In this paper, we propose a Wasserstein metric-based random path planning algorithm. Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the safety of a returned path in an uncertain obstacle environment. Vehicle and obstacle states are modeled as distributions based upon state and model observations. Additionally, we define limits on distributional sampling error so the Wasserstein distance between a vehicle state distribution and obstacle distributions can be bounded. This enables the algorithm to return safe paths with a confidence bound through combining finite sampling error bounds with calculations of the Wasserstein distance between discrete distributions. W-Safe RRT is compared against a baseline minimum encompassing ball algorithm, which ensures balls that minimally encompass discrete state and obstacle distributions do not overlap. The improved performance is verified in a 3D environment using single, multi, and rotating non-convex obstacle cases, with and without forced obstacle error in adversarial directions, showing that W-Safe RRT can handle poorly modeled complex environments.

42 ENGINEERING↗
DOE OSTI2024

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Abstract Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-Informed Machine Learning-Aided System Space Discretization

Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.

Kim, Junyung↗
NASA NTRS2016

Application of Deterministic and Probabilistic System Design Methods and Enhancements of Conceptual Design Tools for ERA Project

This report documents work done by the Aerospace Systems Design Lab (ASDL) at the Georgia Institute of Technology, Daniel Guggenheim School of Aerospace Engineering for the National Aeronautics and Space Administration, Aeronautics Research Mission Directorate, Integrated System Research Program, Environmentally Responsible Aviation (ERA) Project. This report was prepared under contract NNL12AA12C, "Application of Deterministic and Probabilistic System Design Methods and Enhancement of Conceptual Design Tools for ERA Project". The research within this report addressed the Environmentally Responsible Aviation (ERA) project goal stated in the NRA solicitation "to advance vehicle concepts and technologies that can simultaneously reduce fuel burn, noise, and emissions." To identify technology and vehicle solutions that simultaneously meet these three metrics requires the use of system-level analysis with the appropriate level of fidelity to quantify feasibility, benefits and degradations, and associated risk. In order to perform the system level analysis, the Environmental Design Space (EDS) [Kirby 2008, Schutte 2012a] environment developed by ASDL was used to model both conventional and unconventional configurations as well as to assess technologies from the ERA and N+2 timeframe portfolios. A well-established system design approach was used to perform aircraft conceptual design studies, including technology trade studies to identify technology portfolios capable of accomplishing the ERA project goal and to obtain accurate tradeoffs between performance, noise, and emissions. The ERA goal, shown in Figure 1, is to simultaneously achieve the N+2 benefits of a cumulative noise margin of 42 EPNdB relative to stage 4, a 75 percent reduction in LTO NOx emissions relative to CAEP 6 and a 50 percent reduction in fuel burn relative to the 2005 best in class aircraft. There were 5 research task associated with this research: 1) identify technology collectors, 2) model technology collectors in EDS, 3) model and assess ERA technologies, 4) LTO and cruise emission prediction, and 5) probabilistic analysis of technology collectors and portfolios.

Mavris, Dimitri N.↗
DOE OSTI2024

"Source Term Modeling for Advanced Gas Micro-Reactors"

Maintaining the safety of the public, environment, and operating personnel is the most important factor in designing, operating, maintaining, and decommissioning nuclear reactors. In recent years, there has been a growing interest in the development of micro-reactors employing TRi-structural ISOtropic (TRISO)-coated particle fuel. In gas reactors, TRISO fuel plays an important role in the safety case for high temperature reactors because of the fission product retention properties of the fuel. This ability enables the use of a functional containment strategy for the reactor where multiple barriers are used to prevent fission product release to the environment. Part of the safety analysis of these advanced reactors is the assessment of radionuclide releases under normal and accident conditions through the multiple credited safety barriers. Using conservative assumptions, a mechanistic analysis can be performed to quantify these releases that combines the probabilistic assessment of failure with analytic solutions to radionuclide transport equations. Source term modeling for TRISO fuel has been performed for previous reactor designs; however, these models are outdated, in many cases proprietary, and need updates to be applied to the current state of TRISO fuel technology and alternative gas reactor core configurations [1]. Currently, the only publicly available source term assessment for gas reactors is an expert-based Monte Carlo simulation based on the effectiveness of the fuel kernel, coating layers, and graphite block in a modular high temperature gas reactor [2]. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that could be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. The release is calculated by the diffusion of the key safety important fission products through the kernel, silicon carbide (SiC), graphite for both intact and defective TRISO particles based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system to remove fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. The model then can calculate the fission product release for any transient temperature profile and fission product releases can then be used to assess radiological dose to the workers and the public using conventional dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy (DOE) Advanced Gas Reactor (AGR) TRISO fuel development program. The model is coded in python with inputs and outputs in excel spreadsheets, as well as python plotting utilities to aid in the interpretation of the results. References: [1] INL, NGNP Mechanistic Source Term White Paper, INL-10-17997, July 2010. [2] David A. Petti, Richard R. Hobbins, Peter Lowry, Hans Gougar, “Representative Source Terms and The Influence of Reactor Attributes on Functional Containment in Modular High Temperature Gas-cooled Reactors,” Nuclear Technology, Vol. 184, p. 181-197, Nov. 2013.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRSDate not supplied

Performance Risk Model Validation with Operationally Relevant Tasks

Human Research Program aims to develop methods to support astronauts’ health and productivity during spaceflight. The Crew Health and Performance Probabilistic Risk Assessment (CHP-PRA) team uses powerful computational methods to predict mission risk in both domains: medical and performance. Here, we show how CHP-PRA uses the Performance Risk Model (PRisM) to quantify the performance risk and show an application of the model on operationally relevant tasks. There are various metrics adopted across performance researchers that PRisM can accommodate. For data analysis, interpretation, and integration, we use a method of unifying data from multiple sources by converting each to a single metric. We consult subject matter experts prior to integrating the converted data into PRisM. The method we use is inspired by the Cooper-Harper rating scale [1]. Using this unified metric, we can easily combine data from various tests and lab groups. We explain our conversion method in detail and show how it pertains to the process of testing and validation of PRisM on operational tasks. We conducted an initial validation in collaboration with the Behavioral Health and Performance (BHP) lab. We test PRisM using data on their operationally relevant task ROBoT-r, a track-and-capture task for grappling incoming resupply vehicles [2]. Several other labs at NASA Johnson Space Center worked together to design 7 Functional Task Tests (FTTs) in pursuit of simulating the tasks required after landing on a planetary surface and after return to Earth [3]. Here we use the results from both ROBoT-r and the 7 FTTs and compare their experiment data to PRisM’s computational output to demonstrate how PRisM can support operations by predicting crew performance on future missions.

performance modeling↗
DOE OSTI2021

Event-modeled Risk Assessment Using Linked Diagrams

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is a software tool developed at INL for researching the capabilities of dynamic PRA (Probabilistic Risk Assessment). In order to promote the effective use of dynamic PRA by the general community, EMRALD focuses on the following key aspects: Simplifying the modeling process by providing a structure that corresponds to traditional PRA modeling methods Providing a user interface (UI) that makes it easy for the user to model and visualize complex interactions Allowing the user to couple with other analysis applications such as physics based simulations. This includes one-way communication for most applications and two-way loose coupling for customizable applications Providing the sequence and timing of events that lead to the specified outcomes when calculating results Traditional aspects of components with basic events, fault trees, and event trees are all captured in a dynamic framework of state diagrams, which are displayed.

Prescott, SteveR↗
NASA NTRS2000

Intermediate Temperature Stress Rupture of Woven SiC Fiber, BN Interphase, SiC Matrix Composites in Air

Tensile stress-rupture experiments were performed on woven Hi-Nicalon reinforced SiC matrix composites with BN interphases in air. Modal acoustic emission (AE) was used to monitor the damage accumulation in the composites during the tests and microstructural analysis was performed to determine the amount of matrix cracking that occurred for each sample. Fiber fractograph), was also performed for individual fiber failures at the specimen fracture surface to determine the strengths at which fibers failed. The rupture strengths were significantly worse than what would have been expected front the inherent degradation of the fibers themselves when subjected to similar rupture conditions. At higher applied stresses the rate of rupture "?as larger than at lower applied stresses. It was observed that the change in rupture rate corresponded to the onset of through-thickness cracking in the composites themselves. The primary cause of the sen,ere degradation was the ease with which fibers would bond to one another at their closest separation distances, less than 100 nanometers, when exposed to the environment. The near fiber-to-fiber contact in the woven tows enabled premature fiber failure over large areas of matrix cracks due to the stress-concentrations created b), fibers bonded to one another after one or a few fibers fail. i.e. the loss of global load sharing. An@, improvement in fiber-to-fiber separation of this composite system should result in improved stress- rupture properties. A model was den,eloped in order to predict the rupture life-time for these composites based on the probabilistic nature of indin,idual fiber failure at temperature. the matrix cracking state during the rupture test, and the rate of oxidation into a matrix crack. Also incorporated into the model were estimates of the stress-concentration that would occur between the outer rim of fibers in a load-bearing bundle and the unbridged region of a matrix crack after Xia et al. For the lower stresses, this source of stress-concentration was the likely cause for initial fiber failure that would trigger catastrophic failure of the composite.

Morscher, Gregory N.↗
NASA NTRS2010

Probabilistic Assessment of Cancer Risk from Solar Particle Events

For long duration missions outside of the protection of the Earth s magnetic field, space radiation presents significant health risks including cancer mortality. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which include high energy protons and heavy ions. While the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle, the individual SPE occurrences themselves are random in nature. We estimated the probability of SPE occurrence using a non-homogeneous Poisson model to fit the historical database of proton measurements. Distributions of particle fluences of SPEs for a specified mission period were simulated ranging from its 5 th to 95th percentile to assess the cancer risk distribution. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. We estimated the overall cumulative probability of GCR environment for a specified mission period using a solar modulation model for the temporal characterization of the GCR environment represented by the deceleration potential (^). Probabilistic assessment of cancer fatal risk was calculated for various periods of lunar and Mars missions. This probabilistic approach to risk assessment from space radiation is in support of mission design and operational planning for future manned space exploration missions. In future work, this probabilistic approach to the space radiation will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Kim, Myung-Hee Y.↗
DOE OSTI2023

FINITE ELEMENT MODEL MESH REFINEMENT EFFECTS ON QUALIFICATION OF NUCLEAR GRADE GRAPHITE COMPONENTS

The American Society of Mechanical Engineers (ASME) provides the full and simplified design-by-analysis probabilistic assessments for determining acceptance of nuclear grade graphite core components. The assessments can be characterized by three parts: (1) a component stress distribution, often determined by a finite element (FE) model; (2) a Weibull probability density function (pdf) that characterizes the experimental tensile strength distribution; and (3) the post-processor, which combines the FE model and the Weibull strength distribution in accordance with the full and simplified assessments to determine component acceptance. It is known that the level of mesh refinement in FE models can affect the modeled component’s calculated stress distribution. Depending on the component geometry, the stress distribution may converge with sufficient refinement. It was previously unknown whether the acceptance decision resulting from the full and simplified assessments might change even with sufficient mesh refinement. This study explores that question using experimental strength results for a dog-bone geometry for two graphite grades, IG-110 and PCEA. The simplified assessment has two criteria that must be met, the first limits the combined membrane stress by the allowable stress and the second limits the peak equivalent stress by the allowable stress scaled by the ratio of flexural to tensile strength. In the application of the simplified assessment, convergence of the peak equivalent stress required extreme mesh refinement, however, the acceptance decision was not affected. It is hypothesized that more complex geometries with stress concentrations may present mesh refinement effects on the simplified assessment acceptance decision. Mesh refinement did affect the acceptance decision in the full assessment for the applied pressure loadings in this study. This work suggests component stress distribution convergence is not a sufficient criteria for POF convergence in the full assessment and that mesh refinement should continue until the POF has converged, especially where the resulting POF is bordering the SRC acceptable POF limit.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

14 SOLAR ENERGY↗
DOE OSTI2020

Curvature perturbations from stochastic particle production during inflation

We calculate the curvature power spectrum sourced by spectator fields that are excited repeatedly and non-adiabatically during inflation. In the absence of detailed information of the nature of spectator field interactions, we consider an ensemble of models with intervals between the repeated interactions and interaction strengths drawn from simple probabilistic distributions. We show that the curvature power spectra of each member of the ensemble shows rich structure with many features, and there is a large variability between different realizations of the same ensemble. Such features can be probed by the cosmic microwave background (CMB) and large scale structure observations. They can also have implications for primordial black hole formation and CMB spectral distortions. The geometric random walk behavior of the spectator field allows us to calculate the ensembleaveraged power spectrum of curvature perturbations semi-analytically. For sufficiently large stochastic sourcing, the ensemble averaged power spectrum shows a scale dependence arising from the time spent by modes outside the horizon during the period of particle production, in spite of there being no preferred scale in the underlying model. We find that the magnitude of the ensemble-averaged power spectrum overestimates the typical power spectra in the ensemble because the ensemble distribution of the power spectra is highly non-Gaussian with fat tails.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Geometric Modeling of Inclusions as Ellipsoids

Nonmetallic inclusions in gas turbine disk alloys can have a significant detrimental impact on fatigue life. Because large inclusions that lead to anomalously low lives occur infrequently, probabilistic approaches can be utilized to avoid the excessively conservative assumption of lifing to a large inclusion in a high stress location. A prerequisite to modeling the impact of inclusions on the fatigue life distribution is a characterization of the inclusion occurrence rate and size distribution. To help facilitate this process, a geometric simulation of the inclusions was devised. To make the simulation problem tractable, the irregularly sized and shaped inclusions were modeled as arbitrarily oriented, three independent dimensioned, ellipsoids. Random orientation of the ellipsoid is accomplished through a series of three orthogonal rotations of axes. In this report, a set of mathematical models for the following parameters are described: the intercepted area of a randomly sectioned ellipsoid, the dimensions and orientation of the intercepted ellipse, the area of a randomly oriented sectioned ellipse, the depth and width of a randomly oriented sectioned ellipse, and the projected area of a randomly oriented ellipsoid. These parameters are necessary to determine an inclusion s potential to develop a propagating fatigue crack. Without these mathematical models, computationally expensive search algorithms would be required to compute these parameters.

Bonacuse, Peter J.↗
DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗
NASA NTRS1977

Impact cratering and regolith dynamics

The most recent models concerning mechanical aspects of lunar regolith dynamics related to impact cratering use probabilistic approaches to account for the randomness of the meteorite environment in both space and time. Accordingly the absolute regolith thickness is strictly a function of total bombardment intensity and absolute regolith growth rate in nonlinear through geologic time. Regoliths of increasing median thickness will have larger and larger proportions of more and more deep seated materials. An especially active zone of reworking on the lunar surface of about 1 mm depth has been established. With increasing depth, the probability of excavation and regolith turnover decreases very rapidly. Thus small scale stratigraphy - observable in lunar core materials - is perfectly compatible with regolith gardening, though it is also demonstrated that any such stratigraphy does not necessarily present a complete record of the regolith's depositional history. At present, the lifetimes of exposed lunar rocks against comminution by impact processes can be modeled; it appears that catastrophic rupture dominates over single particle abrasion.

Hoerz, F.↗
DOE OSTI2025

Probabilistic Impact Assessment and Software Tool Requirements

This research project will address the most pressing uncertainties in modeling and measuring the electric power grid effects of geomagnetic disturbances (GMDs) and the E3 portion of nuclear electromagnetic pulse (EMP). The primary goal is to help decision-makers in the electric power sector have the knowledge and tools they need to most effectively mitigate GMD effects on the North American electric grid, with a secondary focus on EMP response. Primary tasks will involve comprehensive modeling,

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1986

Induction of models under uncertainty

This paper outlines a procedure for performing induction under uncertainty. This procedure uses a probabilistic representation and uses Bayes' theorem to decide between alternative hypotheses (theories). This procedure is illustrated by a robot with no prior world experience performing induction on data it has gathered about the world. The particular inductive problem is the formation of class descriptions both for the tutored and untutored cases. The resulting class definitions are inherently probabilistic and so do not have any sharply defined membership criterion. This robot example raises some fundamental problems about induction; particularly, it is shown that inductively formed theories are not the best way to make predictions. Another difficulty is the need to provide prior probabilities for the set of possible theories. The main criterion for such priors is a pragmatic one aimed at keeping the theory structure as simple as possible, while still reflecting any structure discovered in the data.

Cheeseman, Peter↗
DOE OSTI2025

Integration of multiple coinflip devices for high-quality random sampling

Artificial intelligence, scientific computing, and probabilistic computing use random sampling to approximate solutions to various problems, with larger models requiring a substantial quantity of random numbers. To generate the required vast quantity of random numbers at high rates, we explore so-called “coinflip” devices, which are stochastic microelectronic devices ideally capable of independently generating random bits with a tunable weight at a high rate. However, coinflip devices are inherently analog and demonstrate nonidealities, like temperature dependence and drift, that can introduce determinism into the outputs. We present important considerations for building systems of multiple coinflip devices to produce high-quality bitstreams with low error and little dependency on previous bits. Using tunnel diodes as coinflip devices, we implement a control loop to adapt to temperature dependence and generate fair bitstreams with each device. While this can lead to dependencies between bits in a single bitstream, we demonstrate that combining results generated in parallel with individual tunnel diodes can produce fair and unpredictable bitstreams. The suitability of these bitstreams for use in probabilistic computing is then demonstrated through a Monte Carlo approximation of π.

Taylor, Brady Garland [Sandia National Laboratorie↗