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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling, Performance Assessment, and Nodal Data Analysis of TRISO-Fueled Systems with Shift

This technical report documents several enhancements to the Shift Monte Carlo (MC) code under the US Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in fiscal year (FY) 2022. Performance enhancements were added to Shift specifically for tristructural isotropic (TRISO)–fueled reactor systems and guided based on performance analysis in FY 2021. For the pebble performance model developed in previous studies, the runtime improved by ~ 91× compared to the original model and ~ 2× compared to the user-optimized model. Compared to Serpent, Shift is ~ 3× slower if Serpent delta-tracking is enabled but ~ 2× faster when delta-tracking is disabled. The multigroup cross section generation was improved through simplifying tally input definitions, porting several post-processing tally operations from Python scripts into the Shift code base, and accounting for production reactions in the scattering multiplicity. Progress was also made on two emerging capabilities: (1) the development of Titan (a Shift reactor physics user interface) and (2) initial investigation into path-length tallies for computing multigroup scattering matrices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1992

Dynamic test/analysis correlation using reduced analytical models

Test/analysis correlation is an important aspect of the verification of analysis models which are used to predict on-orbit response characteristics of large space structures. This paper presents results of a study using reduced analysis models for performing dynamic test/analysis correlation. The reduced test-analysis model (TAM) has the same number and orientation of DOF as the test measurements. Two reduction methods, static (Guyan) reduction and the Improved Reduced System reduction, are applied to the test/analysis correlation of a laboratory truss structure. Simulated test results and modal test data are used to examine the performance of each method. It is shown that selection of DOF to be retained in the TAM is critical when large structural masses are involved. In addition, the use of modal test results may provide difficulties in TAM accuracy even if a large number of DOF are retained in the TAM.

Mcgowan, Paul E.↗
NASA NTRS1992

Dynamic test/analysis correlation using reduced analytical models

Test/analysis correlation is an important aspect of the verification of analysis models which are used to predict on-orbit response characteristics of large space structures. This paper presents results of a study using reduced analysis models for performing dynamic test/analysis correlation. The reduced test-analysis model (TAM) has the same number and orientation of DOF as the test measurements. Two reduction methods, static (Guyan) reduction and the Improved Reduced System (IRS) reduction, are applied to the test/analysis correlation of a laboratory truss structure. Simulated test results and modal test data are used to examine the performance of each method. It is shown that selection of DOF to be retained in the TAM is critical when large structural masses are involved. In addition, the use of modal test results may provide difficulties in TAM accuracy even if a large number of DOF are retained in the TAM.

Mcgowan, Paul E.↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
NASA NTRS2016

Evaluation of Contamination Inspection and Analysis Methods through Modeling System Performance

Contamination is usually identified as a risk on the risk register for sensitive space systems hardware. Despite detailed, time-consuming, and costly contamination control efforts during assembly, integration, and test of space systems, contaminants are still found during visual inspections of hardware. Improved methods are needed to gather information during systems integration to catch potential contamination issues earlier and manage contamination risks better. This research explores evaluation of contamination inspection and analysis methods to determine optical system sensitivity to minimum detectable molecular contamination levels based on IEST-STD-CC1246E non-volatile residue (NVR) cleanliness levels. Potential future degradation of the system is modeled given chosen modules representative of optical elements in an optical system, minimum detectable molecular contamination levels for a chosen inspection and analysis method, and determining the effect of contamination on the system. By modeling system performance based on when molecular contamination is detected during systems integration and at what cleanliness level, the decision maker can perform trades amongst different inspection and analysis methods and determine if a planned method is adequate to meet system requirements and manage contamination risk.

Seasly, Elaine↗
DOE OSTI2018

A Decision-Based Iterative Modeling Framework for Probabilistic Performance Assessments - 18441

Radiological probabilistic performance assessments (PPAs) employ highly complex computer models to simulate the release of radionuclides from wastes in an engineered disposal facility, migration of these radionuclides over time, and resulting radiological dose to potentially exposed individuals. An iterative approach to PPA modeling is described to help define the appropriate complexity of the computer model. An initial model is developed that addresses all relevant processes and media, but that employs preliminary statistical parameter distributions to minimize the initial effort expended. Where appropriate, transport and exposure submodels with simplifying assumptions are also used in the initial model. Different assumptions and boundaries may be explored with the initial model to assess their importance in constraining model results. Exploration of the results of the initial model are used to determine the benefits of model refinements, and to guide such refinements to focus on specific areas of the model that most influence the model outputs of interest. This approach promotes efficiency and model parsimony, but does not compromise defensibility when performed in a transparent manner. Key model boundaries for a PPA include the time frame of the modeling (temporal boundaries), where future human receptors may be located (spatial boundaries), and what types of land uses and activities may exist in the future (exposure scenarios). Simplified transport and exposure models can be useful in helping decision makers and stakeholders establish such boundaries by analyzing the consequences of selecting different bounds and understanding which assumptions and boundaries have the greatest influence on the magnitude and distribution of model results. Pathway analysis and parameter sensitivity analysis performed on PPA model results are key components of the iterative modeling approach. The objective of pathway analysis is to identify the critical radionuclides and pathways contributing to radiation dose during the modeling performance period. An environmental 'pathway' is described by the connections from a waste package or cell in the disposal facility to a future human exposure event. These connections are commonly visualized using graphical means. A simple example of a potentially complete pathway for radon-222 is: radium-containing waste → radon emanation → upwards diffusion in cap material → diffusion/advection into a future building → inhalation exposure to a building occupant Parameter sensitivity analysis is conducted on the results of a PPA to identify those specific model input parameters whose distribution of values contributes the most to the distribution of target model output, such as annual radiation dose. Pathway and sensitivity analyses on initial modeling results provide information for guiding subsequent model development, and possibly interim decisions, by informing questions tied to the modeling objectives and performance criteria. For example, questions related to radiological doses from existing disposals may include: 1. Is radiation dose from radiological exposures either obviously negligible or obviously unacceptable based on initial modeling results? 2. If dose is not obviously negligible or unacceptable relative to applicable performance criteria, what model refinements would reduce uncertainty in the results to better inform decisions? (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Educating HPC Users in the use of advanced computing technology

We examine a multi-modal approach to educating and training users of an advanced computing technology testbed at the Institute for Advanced Computational Science at Stony Brook University. Ookami provides researchers worldwide with access to 176 Fujitsu A64FX compute nodes, this being the same processor technology powering the Japanese Fugaku supercomputer, the fastest computer in the world since June 2020. However, achieving high-performance on this Arm-based, leadership computing technology requires that users be familiar with details of computer architecture, performance analysis and modeling, and high-performance programming models that are commonly omitted in introductory programming courses. Indeed, regardless of their seniority, many of the testbed users are surprisingly unfamiliar with basic concepts such as vectorization, pipelining, latency/bandwidth, roofline models, computing energy/power, threads, and non-uniform memory access. These same concepts also pervade mainstream x86 technologies, so this is of widespread concern. Due to the national/global nature of our user community that is also very diverse in both discipline and experience, the inability to offer formal classes, and our experience that most people do not tend to read online documentation or training materials in sufficient depth, we have consciously employed multiple approaches that heavily emphasize (online) personal interactions and transfer of skills. Online documentation has been organized around best-practices and FAQs; twice-weekly hackathons and office hours via Zoom enable deep dives by both the team and the user community with multiple broad benefits; a Slack channel provides both real time and archived answers and discussions; and workshops, training and webinars target community needs as they arise. Furthermore, the perspective that these tools are being used in an educational setting rather than just for project communication makes them more effective and contributes to community success.

A64FX↗
NASA NTRS1984

Investigation of the HPFTP first stage impeller crack

The occurrence of a crack in the HPFTP first stage impeller of pump 2608 R2 during test 750-245 prompted this analysis to examine possible causes of the failure. Preliminary analysis, using an existing NASTRAN model of the impeller, showed a deficiency in the model's ability to reliably calculate stress in the area of concern (outboard edge of the impeller shroud). A new NASTRAN model was constructed to better define the stress state in the area of crack initiations. Static stress analysis and normal models analysis were performed on the new model. Results are presented.

Source record↗
NASA NTRS2023

A Performance Analysis on Soil Dielectric Models Over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM < 15%) and organic soils (SOM ≥ 15%) would be the preferred option.

Soil Moisture↗
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1999

Fidelity Analysis of Sampled Imaging Systems

Many modeling, simulation and performance analysis studies of sampled imaging systems are inherently incomplete because they are conditioned on a discrete-input, discrete-output model that only accounts for blurring during image acquisition and additive noise. For those sampled imaging systems where the effects of digital image acquisition, digital filtering and reconstruction are significant, the modeling, simulation and performance analysis should be based on a more comprehensive continuous-input, discrete-processing, continuous-output end-to-end model. This more comprehensive model should properly account for the low-pass filtering effects of image acquisition prior to sampling, the potentially important noiselike effects of the aliasing caused by sampling, additive noise due to device electronics and quantization, the generally high-boost filtering effects of digital processing, and the low-pass filtering effects of image reconstruction. This model should not, however, be so complex as to preclude significant mathematical analysis, particularly the mean-square (fidelity) type of analysis so common in linear system theory. We demonstrate that, although the mathematics of such a model is more complex, the increase in complexity is not so great as to prevent a complete fidelity-metric analysis at both the component level and at the end-to-end system level: that is, computable mean-square-based fidelity metrics are developed by which both component-level and system-level performance can be quantified. In addition, we demonstrate that system performance can be assessed qualitatively by visualizing the output image as the sum of three component images, each of which relates to a corresponding fidelity metric. The cascaded, or filtered, component accounts for the end-to-end system filtering of image acquisition, digital processing, and image reconstruction; the random noise component accounts for additive random noise, modulated by digital processing and image reconstruction filtering; and the aliased noise component accounts for the frequency folding effect of sampling, modulated by digital processing and image reconstruction filtering.

Park, Stephen K.↗
NASA NTRS1994

A performance analysis method for distributed real-time robotic systems: A case study of remote teleoperation

Robot coordination and control systems for remote teleoperation applications are by necessity implemented on distributed computers. Modeling and performance analysis of these distributed robotic systems is difficult, but important for economic system design. Performance analysis methods originally developed for conventional distributed computer systems are often unsatisfactory for evaluating real-time systems. The paper introduces a formal model of distributed robotic control systems; and a performance analysis method, based on scheduling theory, which can handle concurrent hard-real-time response specifications. Use of the method is illustrated by a case of remote teleoperation which assesses the effect of communication delays and the allocation of robot control functions on control system hardware requirements.

Lefebvre, D. R.↗
NASA NTRS2008

SEE Rate Estimation: Model Complexity and Data Requirements

Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.

Ladbury, Ray↗
NASA NTRS1981

Some aspects of algorithm performance and modeling in transient analysis of structures

The status of an effort to increase the efficiency of calculating transient temperature fields in complex aerospace vehicle structures is described. The advantages and disadvantages of explicit algorithms with variable time steps, known as the GEAR package, is described. Four test problems, used for evaluating and comparing various algorithms, were selected and finite-element models of the configurations are described. These problems include a space shuttle frame component, an insulated cylinder, a metallic panel for a thermal protection system, and a model of the wing of the space shuttle orbiter. Results generally indicate a preference for implicit over explicit algorithms for solution of transient structural heat transfer problems when the governing equations are stiff (typical of many practical problems such as insulated metal structures).

Adelman, H. M.↗
NASA NTRS1999

A Preliminary Model for Spacecraft Propulsion Performance Analysis Based on Nuclear Gain and Subsystem Mass-Power Balances

Rapid transportation of human crews to destinations throughout the solar system will require propulsion systems having not only very high exhaust velocities (i.e., I(sub sp) >= 10(exp 4) to 10(exp 5) sec) but also extremely low mass-power ratios (i.e., alpha <= 10(exp -2) kg/kW). These criteria are difficult to meet with electric propulsion and other power-limited systems, but may be achievable with propulsion concepts that use onboard power to produce a net gain in energy via fusion or some other nuclear process. This paper compares the fundamental performance of these gain-limited systems with that of power-limited systems, and determines from a generic power balance the gains required for ambitious planetary missions ranging up to 100 AU. Results show that energy gain reduces the required effective mass-power ratio of the system, thus enabling shorter trip times than those of power-limited concepts.

Chakrabarti, S.↗