SEARCH · Search NASA

Results for “Application Models”

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.

SourceResearch connectionFrom yearThrough year

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

At least 379 records · Page 21

All sources NASA NTRS DOE OSTI

NASA NTRS1997

Systems Engineering Metrics: Organizational Complexity and Product Quality Modeling

Innovative organizational complexity and product quality models applicable to performance metrics for NASA-MSFC's Systems Analysis and Integration Laboratory (SAIL) missions and objectives are presented. An intensive research effort focuses on the synergistic combination of stochastic process modeling, nodal and spatial decomposition techniques, organizational and computational complexity, systems science and metrics, chaos, and proprietary statistical tools for accelerated risk assessment. This is followed by the development of a preliminary model, which is uniquely applicable and robust for quantitative purposes. Exercise of the preliminary model using a generic system hierarchy and the AXAF-I architectural hierarchy is provided. The Kendall test for positive dependence provides an initial verification and validation of the model. Finally, the research and development of the innovation is revisited, prior to peer review. This research and development effort results in near-term, measurable SAIL organizational and product quality methodologies, enhanced organizational risk assessment and evolutionary modeling results, and 91 improved statistical quantification of SAIL productivity interests.

Mog, Robert A.↗

DOE OSTI2021

Fast All-sky Radiation Model for Solar applications (FARMS) [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗

DOE OSTI2021

MIRACL Co‐Simulation platform for control and operation of distributed wind in microgrid

Abstract This paper presents a co‐simulation platform (CSP) developed as a part of the “Microgrids, Infrastructure Resilience and Advanced Controls Launchpad (MIRACL)” project, hereafter called MIRACL‐CSP, to allow simulation‐based testing, demonstration, and evaluations of distributed wind under various grid operating conditions. MIRACL‐CSP provides modular interactions among the power distribution system, distributed wind, and utility decision‐making framework. A hierarchical engine for large‐scale infrastructure co‐simulations (HELICS) is used as the core engine of MIRACL‐CSP to establish time and information coordination among the MIRACL‐CSP modules. In this study, MIRACL‐CSP performance is demonstrated using the IEEE 123‐node test distribution grid modelled in GridLAB‐D and the utility decision support application modelled in Python. The functionality of MIRACL‐CSP is demonstrated through various grid operational scenarios in a microgrid and networked microgrid environment. A co‐simulation study is described that demonstrated MIRACL‐CSP capabilities for the microgrid operational scenario in the IEEE 123‐node test system. MIRACL‐CSP is a generic platform that facilitates distributed wind research for different test systems, applications, and valuation of distributed wind.

17 WIND ENERGY↗

NASA NTRS1982

Conference on Complex Turbulent Flows: Comparison of Computation and Experiment, Stanford University, Stanford, CA, September 14-18, 1981, Proceedings. Volume 2 - Taxonomies, reporters' summaries, evaluation, and conclusions

Computational techniques for simulating turbulent flows were explored, together with the results of experimental investigations. Particular attention was devoted to the possibility of defining a universal closure model, applicable for all turbulence situations; however, conclusions were drawn that zonal models, describing localized structures, were the most promising techniques to date. The taxonomy of turbulent flows was summarized, as were algebraic, differential, integral, and partial differential methods for numerical depiction of turbulent flows. Numerous comparisons of theoretically predicted and experimentally obtained data for wall pressure distributions, velocity profiles, turbulent kinetic energy profiles, Reynolds shear stress profiles, and flows around transonic airfoils were presented. Simplifying techniques for reducing the necessary computational time for modeling complex flowfields were surveyed, together with the industrial requirements and applications of computational fluid dynamics techniques.

Kline, S. J.↗

DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗

DOE OSTI2023

Fast All-sky Radiation Model for Solar applications (FARMS) 2023 [SWR-16-18]

Xie, Yu↗

NASA NTRS2004

Space-Based Sensor Web for Earth Science Applications: An Integrated Architecture for Providing Societal Benefits

There is a significant interest in the Earth Science research and user remote sensing community to substantially increase the number of useful observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal, spectral, and spatial coverage of the area(s) under investigation. However, there is little analysis available in terms of the benefits, costs and the optimal set of sensors needed to make the necessary observations. Classic observing system solutions may no longer be applicable because of their point design philosophy. Instead, a new intelligent data collection system paradigm employing both reactive and proactive measurement strategies with adaptability to the dynamics of the phenomena should be developed. This is a complex problem that should be carefully studied and balanced across various boundaries including: science, modeling, applications, and technology. Modeling plays a crucial role in making useful predictions about naturally occurring or human-induced phenomena In particular, modeling can serve to mitigate the potentially deleterious impacts a phenomenon may have on human life, property, and the economy. This is especially significant when one is interested in learning about the dynamics of, for example, the spread of forest fires, regional to large-scale air quality issues, the spread of the harmful invasive species, or the atmospheric transport of volcanic plumes and ash. This paper identifies and examines these challenging issues and presents architectural alternatives for an integrated sensor web to provide observing scenarios driving the requisite dynamic spatial, spectral, and temporal characteristics to address these key application areas. A special emphasis is placed on the observing systems and its operational aspects in serving the multiple users and stakeholders in providing societal benefits. We also address how such systems will take advantage of technological advancement in small spacecraft and emerging information technologies, and how sensor web options may be realized and made affordable. Specialized detector subsystems and precision flying techniques may still require substantial innovation, development time and cost: we have presented the considerations for these issues. Finally, data and information gathering and compression techniques are also briefly described.

Habib, Shahid↗

DOE OSTI2022

MEUMAPPS (C++ Version)

Many materials, metal alloys in particular, have features on the on micrometer or nanometer scale that have a large impact on the properties of the material. These features are known as the microstructure of the material. Understanding why and how the microstructure forms in a material is of fundamental scientific interest as well as of significant technological interest. The capability to predict microstructure evolution in a material allows the intentional design of microstructures and hence the intentional design of material properties. The phase-field method is one of the leading methods for predicting microstructure evolution. One of the most significant problems for phase-field models is their computational expense. Even limited phase-field simulations can easily require thousands of CPU core-hours to complete, which significantly limits their use. This code provides both a general framework for creating scalable, GPU-accelerated phase-field model applications as well as several applications themselves. The code is capable of using hundreds of GPUs efficiently, which greatly reduces the time required to perform simulations. The code is written with an emphasis on performance portability, that is the ability for the code to run efficiently on a number of different computing architectures without modification of the source code. The performance portability of this code is primarily enabled through the use of two libraries, Kokkos (performance portable data structures and execution patterns) and heFFTe (performance portable distributed 3D fast Fourier transforms). The code consists of a core library, applications, and tests. The core library includes shared functionality between applications. This includes interfaces with fast Fourier transform (FFT) libraries such as heFFTe, data structures based on Kokkos, file input and output capabilities, and a solver for infinitesimal strain mechanical equilibrium problems. Five applications are included in the code. The flagship application is the MEUMAPPS-SS application, which implements the Kim-Kim-Suzuki phase-field model for precipitation for an arbitrary number of phases and components in a metal alloy. Five simpler applications are also included that solve the Eshelby inclusion problem, Allen-Cahn equation, the coupled Allen-Cahn and diffusion equations, and the Cahn-Hilliard equation. The code includes two applications to solve the Cahn-Hilliard equation, one with constant-step-size first-order time integration and the second with adaptive high-order time integration.

DeWitt, Stephen [Oak Ridge National Lab. (ORNL), O↗

DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

An information-matching approach to optimal experimental design and active learning

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher information matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. Here, we demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an active learning (AL) loop for materials science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly AL in large machine-learning models.

Materials science↗

DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

Multistep modeling of protein structure: application to bungarotoxin

Modelling of bungarotoxin in atomic details is presented in this article. The model-building procedure utilizes the low-resolution crystal coordinates of the c-alpha atoms of bungarotoxin, sequence homology within the neurotoxin family, as well as high-resolution x-ray diffraction data of cobratoxin and erabutoxin. Our model-building procedure involves: (a) principles of comparative modelling, (b) embedding procedures of distance geometry, and (c) use of molecular mechanics for optimizing packing. The model is not only consistent with the c-alpha coordinates of crystal structure, but also agrees with solution conformational features of the triple-stranded beta sheet as observed by NOE measurements.

NASA Discipline Exobiology↗

DOE OSTI2023

A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1996

A dynamic response model for pressure sensors in continuum and high Knudsen number flows with large temperature gradients

This paper develops a dynamic model for pressure sensors in continuum and rarefied flows with longitudinal temperature gradients. The model was developed from the unsteady Navier-Stokes momentum, energy, and continuity equations and was linearized using small perturbations. The energy equation was decoupled from momentum and continuity assuming a polytropic flow process. Rarefied flow conditions were accounted for using a slip flow boundary condition at the tubing wall. The equations were radially averaged and solved assuming gas properties remain constant along a small tubing element. This fundamental solution was used as a building block for arbitrary geometries where fluid properties may also vary longitudinally in the tube. The problem was solved recursively starting at the transducer and working upstream in the tube. Dynamic frequency response tests were performed for continuum flow conditions in the presence of temperature gradients. These tests validated the recursive formulation of the model. Model steady-state behavior was analyzed using the final value theorem. Tests were performed for rarefied flow conditions and compared to the model steady-state response to evaluate the regime of applicability. Model comparisons were excellent for Knudsen numbers up to 0.6. Beyond this point, molecular affects caused model analyses to become inaccurate.

Whitmore, Stephen A.↗

NASA NTRS1999

MEMS reliability assurance guidelines for space applications

This guideline was developed as an aid to help in the understanding of Microelectromechanical Systems (MEMS) reliability and to facilitate the insertion of this technology into high reliability applications. Modeled after the GaAs MMIC Reliability Assurance Guideline for Space Applications by Kayali, et al., is was felt that a guideline would be more advantageous to people than a strict specification.

microelectromechanical↗

DOE OSTI2024

Fuel Performance Evaluation of THOR-C Experiments

The Temperature Heatsink Overpower Response Commissioning (THOR-C) and THOR-Metal (THOR-M) experiments will be performed as part of an ongoing project for testing sodium fast reactor fuels with the Japan Atomic Energy Agency (JAEA). The THOR-C experiments consist of fresh metallic fuel pins and have been analyzed using the ABAQUS, Ansys codes and the BISON fuel performance code. THOR-M-Loss of Flow-1 (THOR-M-LOF-1) is designed to test an EBR-II irradiated fuel pin under LOF conditions. Simulation of the THOR-MLOF-1 experiment required first simulating the base irradiation of the fuel pin in EBR-II. MFUEL module of SAS4A/SASSYS-1 [1] is a physics-based metallic fuel performance model applicable to the normal operation, transient scenarios and fuel failure modeling including scenarios with bulk fuel melting. The model has been validated using EBR-II normal operation, separate effect transient tests as well as TREAT M-Series transient tests [2]. In this study, MFUEL models has been utilized together with a new capsule heat transfer model developed in this project. The new heat transfer model was necessary due to (1) significant amount of heat losses that required 2D heat transfer, (2) the presence of a titanium heat sink, rejecting a significant amount of heat, and (3) stagnant coolant conditions, which are inconsistent with SAS4A/SASSYS-1 (SAS) heat transfer model. Updates to SAS4A/SASSYS-1 and MFUEL has been described below, followed by a preliminary validation effort using the results from THOR-C-2 fresh fuel capsule experiment. A previous study for THOR-C-2 analysis using BISON code is also utilized in this study to model this test [3]. [1] D. O’Grady, A. J. Brunett, L. Ibarra, A. Karahan, T. Kim, T. S. Sumner, R. Thomas, T. H. Fanning, “The SAS4A/SASSYS-2 Version 5.7 Safety Analysis Code System,” Argonne National Laboratory,ANL/NSE-SAS/5.7, (2023). [2] A. Karahan, T. Kim, T. Fanning, D. O’Grady, “Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1,” Argonne National Laboratory, ANL/NSE-23/11, (2023). [3] M. Mihelish, A. Zabriskie, K. Paaren, P. Medvedev, C. Jensen, “Fuel Performance Predictions for the TREAT THOR-C Experiments,” Idaho National Laboratory, INL/RPT-23-73397, Revision 0, (2023)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗