Search NASA⌕ Search

SEARCH · Search NASA

Results for “Simulator data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Quantifying Barotrauma Risk to Juvenile Fish during Hydro-turbine Passage

We introduce a method for hydro turbine biological performance assessment (BioPA) to bridge the gap between field and laboratory studies on fish injury and turbine engineering design. Using this method, a suite of biological performance indicators is computed based on simulated data from a computational fluid dynamics (CFD) model of a proposed hydro turbine design. Each performance indicator is a measure of the probability of exposure to a certain dose of an injury mechanism. If the relationship between the dose of an injury mechanism (stressor) and frequency of injury (dose-response) is known from laboratory or field studies, the likelihood of fish injury for a turbine design can be computed from the performance indicator. By comparing the values of the indicators from various turbine designs, engineers and biologists can identify the more-promising designs and operating conditions to minimize hydraulic conditions hazardous to passing fish. In this paper, the BioPA method is applied to estimate barotrauma induced mortal injury rates for Chinook salmon exposed to rapid pressure changes in Kaplan-type hydro turbines. Following the description of the general method, application of the BioPA to estimate the probability of mortal injury from exposure to rapid decompression is illustrated using a Kaplan hydro turbine at the John Day Dam on the Columbia River in the Pacific Northwest region of the USA. The estimated rates of mortal injury increased from 0.3% to 1.7% as discharge through the turbine increased from 334 to 564 m3/s for fish assumed to be acclimated to a depth of 5 m. The majority of pressure nadirs occurred immediately below the runner blades, with the lowest values in the gap at the blade tips and just below the leading edge of the blades. Such information can help engineers focus on problem areas when designing new turbine runners to be more fish-friendly than existing units.

computational fluid dynamics (CFD)↗
DOE OSTI2023

Data-Driven Modeling of a High Capacity Cryogenic System for Control Optimization

The Cryogenic Moderator System (CMS) is responsible for maintaining a steady flow of cold neutrons for numerous physics experiments at the Spallation Neutron Source (SNS) in Oak Ridge National Laboratory (ORNL). Sudden losses in beam power, known as beam trips, cause a major disturbance to the CMS due to large step changes in cooling demands. Ongoing efforts on upgrading the neutron beam power from 1.4 to 2.0MW are expected to generate larger transients that can further strain the CMS subsystems if they are not properly controlled. To manage such disturbances, four flow valves and one electric heater are adjusted by five decentralized proportional-integral-derivative (PID) controllers. However, the original PID gains were calibrated empirically based only on tracking performance and not based on disturbance rejection. To address this issue without compromising current CMS operations, a control-oriented model was developed to recalibrate the PID controllers offline. The zero-dimensional (0-D) model was based on simple physics-based principles and data-driven system identification techniques. The CMS was broken into several subsystems for analysis, each of which corresponds to a parametric model tied to the thermodynamic states of the working fluid. The model parameters were identified using the nonlinear least squares method where the residuals were calculated from available sensor data. Simulation results show that the proposed model can capture the dynamics of the CMS at steady state and during beam trips.

Maldonado Puente, Bryan↗
DOE OSTI2016

Effect of subsurface soil moisture variability and atmospheric conditions on methane gas migration in shallow subsurface

We report a major concern resulting from the increased use and production of natural gas has been how to mitigate fugitive greenhouse gas emissions (predominantly methane) from natural gas infrastructure (e.g., leaky shallow pipelines). Subsurface migration and atmospheric loading of methane from pipeline leakage is controlled by source configurations and subsurface soil conditions (e.g., soil heterogeneity and soil moisture) and are further affected by atmospheric conditions (e.g., wind and temperature). However, the transport and attenuation of methane under varying subsurface and atmospheric conditions are poorly understood, making it difficult to estimate leakage fluxes from methane concentration measurements at and above the soil surface. Based on a series of controlled bench-scale experiments using a large porous media tank interfaced with an open-return wind tunnel, this study investigated multiphase processes controlling migration of methane from a point source representing a buried pipeline leaking at fixed flow rate (kg/s) under various saturation and soil-texture conditions. In addition, potential effects of atmospheric boundary controls, wind (0.5 and 2.0 m s -1 ) and temperature (22 and 35 °C), were also examined. Results showed the distinct effects of soil heterogeneity and, to a varying degree, of soil moisture on surface methane concentrations. In addition, results also showed the pronounced effects of wind and, to a lesser degree, of temperature on surface methane concentrations in the presence of varying soil and moisture conditions. The observed subsurface methane profiles were simulated using the multiphase transport simulator TOUGH2-EOS7CA. Observed agreement between measured and simulated data demonstrates that for the conditions studied, multiphase migration of a multicomponent gas mixture (including methane) under density-dependent flow can be adequately represented with a Fickian advection-diffusion (or dispersion) model (ADM) framework. The dominant effect of saturation over the soil texture, could also be inferred from numerical characterization.

09 BIOMASS FUELS↗
DOE OSTI2017

Jet stability and wall impingement flow field in a thermal striping experiment

We present velocity and temperature field measurements for a 0.9 x 0.9 x 1.7 m glass tank in which two air jets at Re=10000 mix and impinge upon the lid at ambient temperature and pressure. Flow patterns are characterized across a 350 x 200 mm plane located 3 mm below the lid for two inlet geometries: 1) “extended”, in which inlet channels protrude above the tank base, and 2) “flush”, a flat base without protrusions. This minor geometry variation produced distinct changes in the lid flow field, appearing as three stagnant regions for the extended case and only one for flush. The dichotomy is attributed to system stability characteristics: jets are stable in the extended case and unstable for flush. In a separate set of nonisothermal tests, the impingement temperature field was measured for inlet temperature mismatches of 4 oC and jets near Re=10000. A 50 m-long fiber optic distributed temperature sensor positioned 2 mm below the lid measured at 1350 locations. Like the velocity fields, the temperature fields differ for the two inlet geometries: good thermal mixing for the flush case and subdued mixing for the extended case. Simulations with the spectral element code Nek5000 replicated the observed stability dichotomy, duplicating the number of stagnant regions observed in the experiment and matching their locations within ±10 mm. Simulation data suggests that flush case instability is due to interactions between jets and wall flows at the bottom of the tank. The clear flow dichotomy exhibited by this two-jet setup presents an unambiguous case to test the ability of CFD tools to predict subtle flow field changes driven by minor modifications in geometry in the context of thermal striping.

42 ENGINEERING↗
DOE OSTI2026

Two-phase flow numerical analysis of electrode geometry for alkaline water electrolyzers

Hydrogen is a promising component of a future energy-secure and efficient economy, but its competitiveness depends on reducing production costs. One strategy is to operate alkaline water electrolyzers at higher current densities to increase output. However, this intensifies performance losses due to gas bubble accumulation, which blocks transport pathways and deactivates electrochemically active surfaces. Enhancing bubble evacuation through electrode design is therefore essential. Previous studies have explored various approaches — such as modifying surface morphology, applying sonication or pressure modulation, and introducing surfactants — but these efforts have addressed a limited range of conditions due to the complexity of two-phase flow and electrode geometries. Experiments have also largely been focused on either cell level improvements, which lack the information necessary to isolate each contributing factor, or on modified geometries that are not relevant to practical cell operation. From a modeling perspective, conventional Eulerian multiphase models do not track the complex gas–liquid interfacial dynamics and often neglect surface tension and contact angle effects, reducing their predictive accuracy. To provide insights on the effects of different electrode geometries on the performance of alklaine water electrolyzers this work employs an immersed boundary volume-of-fluid method to simulate bubble behavior in 3D porous electrodes. Multiple base electrode geometries, typically used in practice, with varying porosity are evaluated under a constant surface gas generation rate. Simulation data is analyzed to quantify electrode gas coverage, bubble size dynamics and other relevant metrics. Results show that porosity strongly influences bubble accumulation on electrode surfaces, with higher porosity reducing gas coverage, and its not strictly dependent on the electrode geometry. However, the electrode’s base geometry significantly affects gas accumulation at the separator gap, independent of porosity. A foam electrode geometry resulted in the lowest gas coverage of all electrodes with a median volumetric gas coverage of 11%, but at the cost of a 70% reduction in active area compared with the largest surface area electrode, while gyroid electrodes showed the best trade-off between gas coverage, particularly at the separator surface, and electrochemically active area. In conclusion, the results highlight the need for holistic electrode design strategies.

Alkaline↗
DOE OSTI2022

Grey-box Fault Models and Applications for Low Carbon Emission CO2 Refrigeration System

CO2 refrigeration systems have attracted more attentions in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in certain commercial buildings such as supermarkets. However, very limited energy studies have been done for such refrigeration systems under faults. Furthermore, this study investigated five common faults for supermarket transcritical CO2 refrigeration systems with display cases. The following major tasks were implemented for this study. (1) Five fault models (gray-box format) regarding the power consumption and supply air temperature were proposed for low- and high-temperature display cases, respectively. The models cover: ice accumulation on evaporator coil, evaporator fan partial failure, expansion valve failure, display door open, and condenser blockage. (2) These fault models were calibrated with field tests, and the modeling accuracies were in good agreement with measurement datasets. (3) The proposed fault models demonstrated more than 90% of fault detection for Fault Detection and Diagnostics (FDD) purposes for both field measurement and simulated data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A machine learning pipeline for membrane segmentation of cryo-electron tomograms

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify cell membrane structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging technique that tries to piece together disconnected components belonging to the same membrane structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here works well for extracting membrane surfaces in two real tomogram datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Volumetric-modulated arc therapy vs conventional fixed-field intensity-modulated radiotherapy in a whole-ventricular irradiation: A planning comparison study

This study evaluated the dosimetric difference between volumetric-modulated arc therapy (VMAT) and conventional fixed-field intensity-modulated radiotherapy (cIMRT) in whole-ventricular irradiation. Computed tomography simulation data for 13 patients were acquired to create plans for VMAT and cIMRT. In both plans, the same median dose (100% = 24 Gy) was prescribed to the planning target volume (PTV), which comprised a tumor bed and whole ventricles. During optimization, doses to the normal brain and body were reduced, provided that the dose constraints of the target coverage were satisfied. The dose-volume indices of the PTV, normal brain, and body as well as monitor units were compared between the 2 techniques by using paired t-tests. The results showed no significant difference in the homogeneity index (0.064 vs 0.065; p = 0.824) of the PTV and conformation number (0.78 vs 0.77; p = 0.065) between the 2 techniques. In the normal brain and body, the dose-volume indices showed no significant difference between the 2 techniques, except for an increase in the volume receiving a low dose in VMAT; the absolute volume of the normal brain and body receiving 1 Gy of radiation significantly increased in VMAT by 1.6% and 8.3%, respectively, compared with that in cIMRT (1044 vs 1028 mL for the normal brain and 3079.2 vs 2823.3 mL for the body; p<0.001). The number of monitor units to deliver a 2.0-Gy fraction was significantly reduced in VMAT compared with that in cIMRT (354 vs 873, respectively; p<0.001). In conclusion, VMAT delivers IMRT to complex target volumes such as whole ventricles with fewer monitor units, while maintaining target coverage and conformal isodose distribution comparable to cIMRT; however, in addition to those characteristics, the fact that the volume of the normal brain and body receiving a low dose would increase in VMAT should be considered.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Yield strength prediction of high-entropy alloys using machine learning

Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Numerical integration of detector response functions via Monte Carlo simulations

Calculations of detector response functions are complicated because they include the intricacies of signal creation from the detector itself as well as a complex interplay between the detector, the particle-emitting target, and the entire experimental environment. As such, these functions are typically only accessible through time-consuming Monte Carlo simulations. Furthermore, the output of thousands of Monte Carlo simulations can be necessary in order to extract a physics result from a single experiment. Here we describe a method to obtain a full description of the detector response function using Monte Carlo simulations. We also show that a response function calculated in this way can be used to create Monte Carlo simulation output spectra a factor of ~1000× faster than running a new Monte Carlo simulation. A detailed discussion of the proper treatment of uncertainties when using this and other similar methods is provided as well. Here, this method is demonstrated and tested using simulated data from the Chi-Nu experiment, which measures prompt fission neutron spectra at the Los Alamos Neutron Science Center.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Investigation of Compton scattering for gamma beam intensity measurements and perspectives at ELI-NP

Compton γ-ray sources have been used for over 30 years with new facilities being under construction or proposed. The gamma beam system under implementation at the Extreme Light Infrastructure - Nuclear Physics facility in Romania will deliver brilliant γ-ray beams with energies up to 19.5 MeV. Several instruments for measuring the parameters of the γ-ray beam are under development at ELI-NP. One of these instruments based on a High Purity Germanium detector is typically used for beam energy measurements at other facilities. Here we investigate the use of a High Purity Germanium detector to continuously monitor the intensity of the ELI-NP gamma beam by measuring the inelastic scattering of photons. This technique relies on both experimental and simulated data and it has been successfully tested during a recent experiment at the High Intensity γ-ray Source facility.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Development and assessment of models for turbulent Rayleigh-Taylor mixing using the macroscopic forcing method

Reynolds-Averaged Navier Stokes (RANS) simulations are a popular method for designing ICF experiments, and accurate mixing models are crucial for these simulations to give good predictions. To this end, the present work seeks to demonstrate the Macroscopic Forcing Method (MFM) as a tool for both improving existing RANS models as well as assessing RANS model forms. First, MFM analysis from Lavacot et al. (Phys. Rev. Fluids, 2025) is used to develop the k–L–F model, an extension of the k–L model of Dimonte and Tipton (Phys. Fluids, 2006) that incorporates nonlocality through addition of a turbulent species flux transport equation. MFM is then applied to the k–L–F model along with the k–L and BHR–4 models to assess their forms and compare the model-implied eddy diffusivity moments to those measured from high-fidelity simulations. Furthermore, the analysis reveals that models incorporating nonlocality (k–L–F and BHR–4) match the high-fidelity simulation data better than purely local models (k–L), both in terms of mean fields and eddy diffusivity moments. However, all of the considered RANS models struggle to match temporal moments at high Atwood numbers, highlighting the importance of temporal nonlocality in these regimes and the need for additional improvement even among models incorporating nonlocality.

general physics↗
DOE OSTI2022

Probing criticality with deep learning in relativistic heavy-ion collisions

Systems with different interactions could develop the same critical behavior due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗