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

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRSDate not supplied

Entry, Descent, and Landing Analysis for the OSIRIS-REx Sample Return Capsule

The Origins, Spectral Interpretation, Resource Identification, and Security – Regolith Explorer (OSIRIS-REx) sample return capsule (SRC) returned to Earth on September 24, 2023, safely landing in the Utah Test and Training Range (UTTR). To ensure a safe and successful landing, a pair of high-fidelity EDL simulations, based on the Program to Optimize Simulated Trajectories (POST) architecture, were used to regularly assess the latest orbit determination (OD) solution from the navigation team, making predictions on Entry, Descent, and Landing (EDL) performance and SRC landing location. The results from these analyses fed into the decision processes for the final trajectory correction maneuvers (TCM’s) and SRC release. The models and methods of analysis will be discussed and a comparison of the final pre-entry landing prediction against the observed landing location will be presented along with an assessment of the best estimates of day-of-entry environmental conditions.

Scott R Francis↗
DOE OSTI2023

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

42 ENGINEERING↗
DOE OSTI2016

Knowledge management: Role of the the Radiation Safety Information Computational Center (RSICC)

The Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory (ORNL) is an information analysis center that collects, archives, evaluates, synthesizes and distributes information, data and codes that are used in various nuclear technology applications. RSICC retains more than 2,000 software packages that have been provided by code developers from various federal and international agencies. RSICC’s customers (scientists, engineers, and students from around the world) obtain access to such computing codes (source and/or executable versions) and processed nuclear data files to promote on-going research, to ensure nuclear and radiological safety, and to advance nuclear technology. The role of such information analysis centers is critical for supporting and sustaining nuclear education and training programs both domestically and internationally, as the majority of RSICC’s customers are students attending U.S. universities. Additionally, RSICC operates a secure CLOUD computing system to provide access to sensitive export-controlled modeling and simulation (M&S) tools that support both domestic and international activities. This presentation will provide a general review of RSICC’s activities, services, and systems that support knowledge management and education and training in the nuclear field.

Valentine, Timothy↗
DOE OSTI2026

Super-resolution model for overlapping peak detection and improved spatial resolution in high-energy diffraction microscopy

Reconstruction quality in Far-field High-Energy Diffraction Microscopy (FF-HEDM) is limited by the spatial resolution of area detectors and the frequent occurrence of overlapping diffraction spots. To address these challenges, we developed a super-resolution (SR) framework using convolutional neural networks (CNNs) to recreate 2D diffraction peaks at up to ×8 resolution from raw detector data. A specialized simulation tool was created to generate synthetic training datasets with varying degrees of peak overlap. Integrated into the Microstructural Imaging using Diffraction Analysis Software (MIDAS), the SR model improves the spatial accuracy and precision of 3D grain reconstruction by an order of magnitude. This approach provides a robust solution for investigation of complex micromechanical states and material classes where the analysis is limited by the presence of overlapping peaks. Furthermore, the methodology developed here can potentially be extended to other techniques that require sub-pixel accuracy for high-fidelity data analysis.

High-energy diffraction microscopy↗
DOE OSTI2021

High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. Furthermore, this paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStock TM and ResStock TM ), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Electrical Load Forecasting Over Multihop Smart Metering Networks With Federated Learning

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but require data sharing, which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. Here, this article presents a novel personalized FL (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches regarding better load forecasting and reduced operational latency costs.

Rahman, Ratun [Univ. of Alabama, Huntsville, AL (U↗
DOE OSTI2025

NNL.Fe.qSNAP-ZBL.2024.1: A Fe Spectral Neighbor Analysis Potential for Radiation Damage Simulations

The NNL.Fe.qSNAP-ZBL.2024.1 machine-learned potential (MLP) has been generated to support the development of an elemental body-centered cubic (BCC) Fe athermal recombination corrected neutron damage model and simulations of primary recoil atom (PRA) cascades in BCC Fe. This MLP is a quadratic spectral neighbor analysis potential (qSNAP) hybridized with the universal Ziegler-Beirsack-Littmark (ZBL) potential at short-range and is named according to Naval Nuclear Laboratory MLP naming conventions (NNL.material-system.MLP-type.year.version). Training set calculations for Fe are presented along with the subsequent MLP fitting procedure. A key criterion of the fitting procedure is that ZBL describes the short-range interaction with minimal impact on the MLP. The MLP is compared to density functional theory (DFT) predicted properties relevant to radiation damage simulation, including threshold displacement energies, for validation. The NNL.Fe.qSNAP-ZBL.2024.1 potential is considered suitable for molecular dynamics (MD) simulations of radiation defects up to 800 K and PRA cascades in BCC Fe up to around 10 keV. The potential can additionally be used on a limited basis for recoils of 10–20 keV, within which range the emergence of structures outside the training set in cascade simulations may cause system instabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1997

Measurement and Validation of Bidirectional Reflectance of Space Shuttle and Space Station Materials for Computerized Lighting Models

Task illumination has a major impact on human performance: What a person can perceive in his environment significantly affects his ability to perform tasks, especially in space's harsh environment. Training for lighting conditions in space has long depended on physical models and simulations to emulate the effect of lighting, but such tests are expensive and time-consuming. To evaluate lighting conditions not easily simulated on Earth, personnel at NASA Johnson Space Center's (JSC) Graphics Research and Analysis Facility (GRAF) have been developing computerized simulations of various illumination conditions using the ray-tracing program, Radiance, developed by Greg Ward at Lawrence Berkeley Laboratory. Because these computer simulations are only as accurate as the data used, accurate information about the reflectance properties of materials and light distributions is needed. JSC's Lighting Environment Test Facility (LETF) personnel gathered material reflectance properties for a large number of paints, metals, and cloths used in the Space Shuttle and Space Station programs, and processed these data into reflectance parameters needed for the computer simulations. They also gathered lamp distribution data for most of the light sources used, and validated the ability to accurately simulate lighting levels by comparing predictions with measurements for several ground-based tests. The result of this study is a database of material reflectance properties for a wide variety of materials, and lighting information for most of the standard light sources used in the Shuttle/Station programs. The combination of the Radiance program and GRAF's graphics capability form a validated computerized lighting simulation capability for NASA.

Fletcher, Lauren E.↗
DOE OSTI2016

CASL VMA FY16 Milestone Report (L3:VMA.VUQ.P13.07) Westinghouse Mixing with COBRA-TF

COBRA-TF (CTF) is a low-resolution code currently maintained as CASL's subchannel analysis tool. CTF operates as a two-phase, compressible code over a mesh comprised of subchannels and axial discretized nodes. In part because CTF is a low-resolution code, simulation run time is not computationally expensive, only on the order of minutes. Hi-resolution codes such as STAR-CCM+ can be used to train lower-fidelity codes such as CTF. Unlike STAR-CCM+, CTF has no turbulence model, only a two-phase turbulent mixing coefficient, β. β can be set to a constant value or calculated in terms of Reynolds number using an empirical correlation. Results from STAR-CCM+ can be used to inform the appropriate value of β. Once β is calibrated, CTF runs can be an inexpensive alternative to costly STAR-CCM+ runs for scoping analyses. Based on the results of CTF runs, STAR-CCM+ can be run for specific parameters of interest. CASL areas of application are CIPS for single phase analysis and DNB-CTF for two-phase analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Computational Modeling, Simulation and Analysis of the RPI Walthousen Reactor Critical Facility (RCF)

The Walthousen Reactor Critical Facility (RCF), located in Schenectady, New York, is currently owned and operated by Rensselaer Polytechnic Institute (RPI). The facility was originally constructed by the American Locomotive Company (ALCO) for the research and development of small, portable reactors. Originally designed for highly-enriched uranium fuel, the prototype reactor first came online in August 26, 1956, and was transferred to RPI ownership in 1963. Several changes to the reactor's design and operation have been made, the most notable of which was the conversion from high-enriched uranium to low-enriched uranium fuel. The RCF is an open-pool critical assembly that operates at a low power with a license limit of less than 100 watts. This low operating power permits a much greater flexibility in configuration and operation as compared to other research reactors. Because of this, experiments may be designed that would be unfeasible for research reactors that operate at much higher powers. The RCF's functional capabilities provide opportunities to perform several reactor experiments and measurements, such as: core criticality experiments, differential and integral rod worth measurements, moderator temperature coefficient of reactivity calculations, critical rod height and critical water height measurements, etc.. These experiments are regularly performed in conjunction with RPI course-work education and Senior Reactor Operator (SRO) training. The unique capabilities and experiments that may be performed in the RCF are particularly useful for validating certain aspects of computational analysis codes. A computational model of the RCF reactor has been previously generated and validated using the Monte Carlo N-Particle (MCNP) code. For the purpose of future experiments, this model is also being adapted for use with the SHARP Toolset, which includes the PROTEUS neutronics code, the Nek5000 fluid dynamics code, and the Diablo structural mechanics code. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A forward modeling approach to analyzing galaxy clustering with S IM BIG

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the SimBIG forward modeling framework. SimBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply SimBIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ (k), to k max = 0.5 h/Mpc. We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Quijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of ΛCDM cosmological parameters: Ω m , Ω b , h, n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h/Mpc. This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ~ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, SimBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SimBIG analyses of summary statistics beyond P ℓ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

47 OTHER INSTRUMENTATION↗
NASA NTRS2001

SRS Computer Animation and Drive Train System

The spinning rocket simulator (SRS) is an ongoing project at Oral Roberts University. The goal of the SRS is to gather crucial data concerning a spinning rocket under thrust for the purpose of analysis and correction of the coning motion experienced by this type of spacecraft maneuver. The computer animation simulates a virtual, scale model of the component of the SRS that represents the spacecraft itself. This component is known as the (VSM), or virtual spacecraft model. During actual physical simulation, this component of the SRS will experience a coning. The goal of the animation is to cone the VSM within that range to accurately represent the motion of the actual simulator. The drive system of the SRS is the apparatus that turns the actual simulator. It consists of a drive motor, motor mount and chain to power the simulator into motion. The motor mount is adjustable and rigid for high torque application. A digital stepper motor controller actuates the main drive motor for linear acceleration. The chain transfers power from the motor to the simulator via sprockets on both ends.

Arthun, Daniel↗
DOE OSTI2017

Utilizing the SACADA system to collect simulator training data to inform human reliability analyses - 169

Lack of appropriate human performance data for the development of human reliability analysis (HRA) methods has been identified as a key issue contributing to the method-to-method variability among the HRA methods. The United States Nuclear Regulatory Commission (NRC) worked with domain experts to develop the Scenario Authoring, Characterization, and Debriefing Application (SACADA) system to collect nuclear power plants' licensed operator simulator training and examination data to inform HRA. In the past two decades, significant improvements in understanding and modelling human performance in HRA have affected the perspectives of the types of data to be collected for HRA. A significant change is that human performance is strongly dependent on the context of the tasks to be performed. To incorporate this understanding, the SACADA data taxonomy collects the context and actual performance information of the training objective elements (TOEs) in nuclear plant operator simulator training. This provides a framework consistent with HRA to use context to estimate human error probabilities. It is recognized that measuring crew performance is equally important as improving operator training. SACADA is designed to support the nuclear trainers in capturing this important information that is used to identify training weaknesses and drive continuous improvement. This paper discusses the SACADA taxonomy, the SACADA system's functions, and the supporting software design to enable the SACADA system to be practically implemented in the nuclear power plants' operator simulator training program to collect data for tracking operator performance and HRA analysis. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗
DOE OSTI2022

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

ABSTRACT Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$_2$$ 2 fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗