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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Finding Real Uncertainties From Physical Simulations

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2014

Geologic Modeling and Simulation Report for the Aquistore Project

The Plains CO 2 Reduction (PCOR) Partnership, through the Energy & Environmental Research Center (EERC), has been supporting, and continues to support, the Petroleum Technology Research Centre (PTRC) Aquistore project. This support has been in the form of geologic characterization, involvement in the Science and Engineering Research Committee (SERC), involvement in public outreach, developing geologic models and running predictive simulations on the expected injection program at the site. The Aquistore project is part of the world’s first commercial postcombustion carbon capture, utilization, and storage project from a coal-fired power-generating facility, the SaskPower Boundary Dam, located in Saskatchewan, Canada, and will be acting as a storage site for a portion of the captured CO 2 from the Boundary Dam power plant. The Aquistore site includes one injection well and a 152-meter offset observation well. Both wells were drilled and completed in the Deadwood and Black Island Formations. At the time of this report, injection at the Aquistore site is anticipated to begin in late 2014. To better understand the storage implications of injecting carbon dioxide (CO 2 ) at the Aquistore site, the EERC has constructed P10, P50, and P90 geologic model realizations and run three new predictive simulation scenarios on each realization. These models and simulations were constructed to better understand both operational and geologic uncertainties that may exist at the Aquistore site. The geologic model realizations and simulations are an update to those completed in the original report entitled “Geologic Modeling and Simulation Report for the Aquistore Project,” Deliverable D93, approved in March 2014. In this update, the same fine-scale model extent of the 34-square-kilometer PTRC 3-D seismic survey area, with higher structural resolution, was continually used for the uncertainty analysis. A low (P10), mid (P50), and high volumetric (P90) case for the amount of pore volume accessible to store the potential injected CO 2 was ranked based on certain deviation variations of model-building parameters, including effective porosity and net-to-gross reservoir, in six sand units of the study area. Three cases with various injection rate and period schemes were simulated based on uncertainty models P10, P50, and P90.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

An evaluation of the Johnson-Cook model to simulate puncture of 7075 aluminum plates.

The objective of this project was to evaluate the use of the Johnson-Cook strength and failure models in an adiabatic finite element model to simulate the puncture of 7075- T651 aluminum plates that were studied as part of an ASC L2 milestone by Corona et al (2012). The Johnson-Cook model parameters were determined from material test data. The results show a marked improvement, in particular in the calculated threshold velocity between no puncture and puncture, over those obtained in 2012. The threshold velocity calculated using a baseline model is just 4% higher than the mean value determined from experiment, in contrast to 60% in the 2012 predictions. Sensitivity studies showed that the threshold velocity predictions were improved by calibrating the relations between the equivalent plastic strain at failure and stress triaxiality, strain rate and temperature, as well as by the inclusion of adiabatic heating.

Corona, Edmundo↗
DOE OSTI2016

Multiscale Investigation of Thermal Transport in UO{sub 2} Fuel using Modeling and Simulation

The thermal conductivity of the UO{sub 2} fuel in light water reactors (LWRs) defines how efficiently heat is transported from the fuel to the coolant, and thus directly influences the reactor efficiency. Unfortunately, the same fission events that generate heat also cause radiation damage and fission product formation that decreases the thermal conductivity. This reduction in thermal conductivity reduces the safety of the reactor in addition to its efficiency, because excess heat generated during an accident cannot be transported from the fuel, resulting in high temperatures that can result in melting. Because thermal conductivity plays such an important role in reactor efficiency and safety, many experiments have been conducted to measure the thermal conductivity of UO{sub 2} and how it changes during reactor operation. Experiments on unirradiated fuel have resulted in a large set of data indicating how the bulk thermal conductivity changes as a function of temperature, which was used to develop a semiempirical model. Additional experiments have been conducted to determine how the thermal conductivity of UO{sub 2} changes as a function of burnup, leading to burnup dependent models fitted to the data. While these empirical models perform well within the conditions at which the data was collected, they cannot be extrapolated outside of those conditions. In addition, while burnup is a measure of the amount of fissioning that has occurred, it cannot account for the impact of the operating conditions on the thermal conductivity. While experimental data provides an empirical view on the value of the thermal conductivity in the fuel during reactor operation, a more mechanistic understanding of how temperature and burnup impact the transport of heat through the fuel would be extremely valuable. Such an understanding would allow for a more accurate prediction of fuel performance, but it could also be used to direct modifications to the fuel that would raise the thermal conductivity or reduce the impact of burnup. In this work we summarize recent efforts to use modeling and simulation at the atomistic and mesoscales to obtain a mechanistic understanding of thermal transport in UO{sub 2}. The thermal conductivity of LWR fuel is one of the most critical material parameters impacting the efficiency and safety of the reactor. Thus, a significant effort is being made to develop a mechanistic understanding of the impact of temperature and burnup on it. In this work, we have provided a short summary of modeling and simulation efforts being carried out at the atomistic and mesoscale to understand this critical property. These research efforts are providing a fundamental understanding of the thermal transport in unirradiated UO{sub 2}, as well as the impact of radiation damage and fission products on thermal transport. These efforts have been focused on UO{sub 2}, but they also are laying the framework for conducted similar simulations to investigate new accident tolerant fuel concepts. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1991

Modeling methods for the design and evaluation of fault-tolerant systems

The authors describe an approach for using directed graph simulation models, behavioral simulation models, and semi-Markov analytic models to implement early- to mid-design analysis activities specified by the SDIO BM/C3 Processor and Algorithm Working Group. The use of the models was demonstrated for a mission scenario requiring parallel, reliable computations with a maximum probability of system failure between 10-4 and 10-2 over a 5-year-preengagement phase and between 10-7 and 10-5 over a half-hour engagement phase.

Scheper, Charlotte O.↗
NASA NTRS2000

An Evaluation of the High Level Architecture (HLA) as a Framework for NASA Modeling and Simulation

The High Level Architecture (HLA) is a current US Department of Defense and an industry (IEEE-1516) standard architecture for modeling and simulations. It provides a framework and set of functional rules and common interfaces for integrating separate and disparate simulators into a larger simulation. The goal of the HLA is to reduce software costs by facilitating the reuse of simulation components and by providing a runtime infrastructure to manage the simulations. In order to evaluate the applicability of the HLA as a technology for NASA space mission simulations, a Simulations Group at Goddard Space Flight Center (GSFC) conducted a study of the HLA and developed a simple prototype HLA-compliant space mission simulator. This paper summarizes the prototyping effort and discusses the potential usefulness of the HLA in the design and planning of future NASA space missions with a focus on risk mitigation and cost reduction.

Reid, Michael R.↗
DOE OSTI2013

TRACE Model for Simulation of Anticipated Transients Without Scram in a BWR

A TRACE model has been developed for using theTRACE/PARCS computational package [1, 2] to simulate anticipated transients without scram (ATWS) events in a boiling water reactor (BWR). The model represents a BWR/5 housed in a Mark II containment. The reactor and the balance of plant systems are modeled in sufficient detail to enable the evaluation of plant responses and theeffectiveness of automatic and operator actions tomitigate this beyond design basis accident.The TRACE model implements features thatfacilitate the simulation of ATWS events initiated by turbine trip and closure of the main steam isolation valves (MSIV). It also incorporates control logic to initiate actions to mitigate the ATWS events, such as water levelcontrol, emergency depressurization, and injection of boron via the standby liquid control system (SLCS). Two different approaches have been used to model boron mixing in the lower plenum of the reactor vessel: modulate coolant flow in the lower plenum by a flow valve, and use control logic to modular.

42 ENGINEERING↗
NASA NTRS2003

The Virtual Test Bed Project

This is a report of my activities as a NASA Fellow during the summer of 2003 at the NASA Kennedy Space Center (KSC). The core of these activities is the assigned project: the Virtual Test Bed (VTB) from the Spaceport Engineering and Technology Directorate. The VTB Project has its foundations in the NASA Ames Research Center (ARC) Intelligent Launch & Range Operations program (ILRO). The objective of the VTB project is to develop a unique collaborative computing environment where simulation models can be hosted and integrated in a seamless fashion. This collaborative computing environment will have as emphasis operational models. This report will focus on the decisions about the different simulation modeling environments considered, simulation platform development, technology and operational models assessment, and computing infrastructure implementation.

Rabelo, Luis↗
NASA NTRS1987

Comparative aspects of hematological responses in animal and human models in simulations of weightlessness and space flight

This paper reviews some human and animal responses to space flight as well as in control models in simulations of weightlessness. Astronauts after space flight have been found to have a decreased red blood cell mass and plasma volume. The reason for these changes is unknown but appears to be caused primarily by a decrease in the need of red blood cells in the weightless condition. Similar though more moderate changes have been found in human subjects subjected to prolonged bed rest or water immersion. What happens to the red cell mass of laboratory rats flown in microgravity is not known but rats have shown an increase in the rate of random red cell loss in flight suggesting a probable decrease. Rat models subjected to either head-down suspension or restraint alone have shown a decrease in red blood cell masses and a decrease in their plasma volume.

Lange, R. D.↗
DOE OSTI2022

Clump survival and migration in VDI galaxies: an analytical model versus simulations and observations

ABSTRACT We address the nature of the giant clumps in high-z galaxies that undergo violent disc instability, distinguishing between long-lived and short-lived clumps. We study the evolution of long-lived clumps during migration through the disc via an analytical model tested by simulations and confront theory with CANDELS-HST observations. The clump ‘bathtub’ model, which considers gas and stellar gain and loss, involves four parameters: the accretion efficiency α, the star formation rate (SFR) efficiency ϵd, and the outflow mass-loading factors for gas and stars, η and ηs. The corresponding time-scales are comparable to the migration time, two-three orbital times. The accretion-rate dependence on clump mass, gas, and stars, allows an analytical solution involving exponential growing and decaying modes. For the fiducial parameter values there is a main evolution phase where the SFR and gas mass are constant and the stellar mass is rising linearly with time. This makes the inverse specific SFR an observable proxy for clump age. When η or ϵd are high, or α is low, the decaying mode induces a decline of SFR and gas mass till the migration ends. Later, the masses and SFR approach an hypothetical exponential growth with a constant specific SFR. The model matches simulations with different, moderate feedbacks, both in isolated and cosmological settings. The observed clumps agree with our predictions, indicating that the massive clumps are long-lived and migrating. A challenge is to model feedback that is non-disruptive in massive clumps but suppresses SFR to match the galactic stellar-to-halo mass ratio.

Dekel, Avishai↗
DOE OSTI2018

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contribution of surface energy budget errors to the bias. All participating models simulate higher net shortwave and longwave radiative fluxes at the surface but there is no consistency on signs of biases in latent and sensible heat fluxes over the Central U.S. and ARM SGP. Nevertheless, biases in net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are the main contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF is affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis suggests that radiation errors are always an important source of T2m error for long-term climate runs with EF errors either of equal or lesser importance. However, for the short-term hindcasts, EF errors are more important provided a model has a substantial EF bias.

Ma, H. -Y.↗
NASA NTRS1986

Satellite rainfall retrieval by logistic regression

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Chiu, Long S.↗
NASA NTRS2016

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

Raschke, Ehrhard↗
NASA NTRS2012

A Model for Simulating the Response of Aluminum Honeycomb Structure to Transverse Loading

A 1-dimensional material model was developed for simulating the transverse (thickness-direction) loading and unloading response of aluminum honeycomb structure. The model was implemented as a user-defined material subroutine (UMAT) in the commercial finite element analysis code, ABAQUS(Registered TradeMark)/Standard. The UMAT has been applied to analyses for simulating quasi-static indentation tests on aluminum honeycomb-based sandwich plates. Comparison of analysis results with data from these experiments shows overall good agreement. Specifically, analyses of quasi-static indentation tests yielded accurate global specimen responses. Predicted residual indentation was also in reasonable agreement with measured values. Overall, this simple model does not involve a significant computational burden, which makes it more tractable to simulate other damage mechanisms in the same analysis.

Ratcliffe, James G.↗
NASA NTRS1999

Reference H Cycle 3 Stability, Control, and Flying Qualities Batch Assessments

This work is an update of the assessment completed in February of 1996, when a preliminary assessment report was issued for the Cycle 2B simulation model. The primary purpose of the final assessment was to re-evaluate each assessment against the flight control system (FCS) requirements document using the updated model. Only a limited number of final assessments were completed due to the close proximity of the release of the Langley model and the assessment deliverable date. The assessment used the nonlinear Cycle 3 simulation model because it combines nonlinear aeroelastic (quasi-static) aerodynamic with hinge moment and rate limited control surface deflections. Both Configuration Aerodynamics (Task 32) and Flight Controls (Task 36) were funded in 1996 to conduct the final stability and control assessments of the unaugmented Reference H configuration in FY96. Because the two tasks had similar output requirements, the work was divided such that Flight Controls would be responsible for the implementation and checkout of the simulation model and Configuration Aerodynamics for writing Madab "script' files, conducting the batch assessments and writing the assessment report. Additionally, Flight Controls was to investigate control surface allocations schemes different from the baseline Reference H in an effort to fulfill flying qualities criteria.

Henderson, Dennis K.↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗