SEARCH · Search NASA

Results for “Predictive Analytic Software”

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 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS1981

Design of a fuel-efficient guidance system for a STOL aircraft

In the predictive mode, the system synthesizes a horizontal path from an initial aircraft position and heading to a desired final position and heading and then synthesizes a fuel-efficient speed-altitude profile along the path. In the track mode, the synthesized trajectory is reconstructed and tracked automatically. An analytical basis for the design of the system is presented and a description of the airborne computer implementation is given. A detailed discussion of the software, which should be helpful to those who use the actual software developed for these tests, is also provided.

Mclean, J. D.↗

DOE OSTI2024

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Altogether, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical-thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

analysis and design of components↗

DOE OSTI2017

Coupling Big Data Analytics and Reactive Transport Modeling for Cost-effective Groundwater Monitoring - 17163

This study presents an innovative approach for sustainable and cost-effective groundwater monitoring. This approach takes advantage of recent advances in various technologies: (1) in situ autonomous sensors, (2) big data analytics, and (3) parallel high-performance computing for flow and reactive transport modeling. In situ sensors are used to periodically measure the key variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control contaminant mobility and the plume spatial and temporal distribution. Based on a limited number of groundwater sampling, the data analytics methods-data mining and machine learning-allow us to identify and quantify the correlations between the in situ-measured variables and contaminant concentrations, and also to detect significant changes associated with the plume mobility. In addition, a state-of-the-art parallel numerical flow and reactive transport simulator Amanzi, and uncertainty quantification software Agni are used to provide an improved physical and mechanistic understanding of the contaminated groundwater system behavior, and to predict the long-term plume distribution for optimizing and adapting the monitoring strategy. Amanzi and Agni were developed as part of the Advanced Simulation Capability for Environmental Management (ASCEM) program of the DOE Office of Environmental Management. Such modeling is critical, particularly, for assessing the impact of climate change and associated hydrological shifts. The developed approach is expected to significantly reduce the groundwater sampling frequency and associated cost. In addition, the real-time information on plume mobility serves as an early warning system, improving the resiliency of contaminated or potentially contaminated sites. We demonstrate this approach using as an example the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

XRootD popularity on hadoop clusters

Performance data and metadata of the computing operations at the CMS experiment are collected through a distributed monitoring infrastructure, currently relying on a traditional Oracle database system. This paper shows how to harness Big Data architectures in order to improve the throughput and the efficiency of such monitoring. A large set of operational data - user activities, job submissions, resources, file transfers, site efficiencies, software releases, network traffic, machine logs - is being injected into a readily available Hadoop cluster, via several data streamers. The collected metadata is further organized running fast arbitrary queries; this offers the ability to test several Map&Reduce-based frameworks and measure the system speed-up when compared to the original database infrastructure. By leveraging a quality Hadoop data store and enabling an analytics framework on top, it is possible to design a mining platform to predict dataset popularity and discover patterns and correlations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗

DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗

NASA NTRS1985

Application of a personal computer for the uncoupled vibration analysis of wind turbine blade and counterweight assemblies

A research effort was undertaken to develop personal computer based software for vibrational analysis. The software was developed to analytically determine the natural frequencies and mode shapes for the uncoupled lateral vibrations of the blade and counterweight assemblies used in a single bladed wind turbine. The uncoupled vibration analysis was performed in both the flapwise and chordwise directions for static rotor conditions. The effects of rotation on the uncoupled flapwise vibration of the blade and counterweight assemblies were evaluated for various rotor speeds up to 90 rpm. The theory, used in the vibration analysis codes, is based on a lumped mass formulation for the blade and counterweight assemblies. The codes are general so that other designs can be readily analyzed. The input for the codes is generally interactive to facilitate usage. The output of the codes is both tabular and graphical. Listings of the codes are provided. Predicted natural frequencies of the first several modes show reasonable agreement with experimental results. The analysis codes were originally developed on a DEC PDP 11/34 minicomputer and then downloaded and modified to run on an ITT XTRA personal computer. Studies conducted to evaluate the efficiency of running the programs on a personal computer as compared with the minicomputer indicated that, with the proper combination of hardware and software options, the efficiency of using a personal computer exceeds that of a minicomputer.

White, P. R.↗

DOE OSTI2017

Computational Fluid Dynamics Modeling of the Fuel Reactor in NETL's 50 kW th Chemical Looping Facility

The National Energy Technology Laboratory (NETL) has explored chemical looping in its 50 kW th facility using a number of oxygen carriers. In this work, the results for methane conversion in the fuel reactor with a hematite iron ore as the oxygen carrier are analyzed. The experimental results are compared to predictions using CPFD's barracuda computational fluid dynamics (CFD) code with kinetics derived from the analysis of fixed bed data. It has been found through analytical techniques from thermal gravimetric analysis data as well as the same fixed bed data that the kinetics for the methane–hematite reaction follows a nucleation and growth or Johnson–Mehl–Avrami (JMA) reaction mechanism. barracuda does not accept nucleation and growth kinetics; however, there is enough sufficient variability of the solids dependence within the software such that the nucleation and growth behavior can be mimicked. This paper presents the method to develop the pseudo-JMA kinetics for barracuda extracted from the fixed bed data and then applies these values to the fuel reactor data to compare the computational results to experimental data obtained from 50 kW th unit for validation. Lastly, a fuel reactor design for near complete conversion is proposed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1981

Identification and evaluation of software measures

A large scale, systematic procedure for identifying and evaluating measures that meaningfully characterize one or more elements of software development is described. The background of this research, the nature of the data involved, and the steps of the analytic procedure are discussed. An example of the application of this procedure to data from real software development projects is presented. As the term is used here, a measure is a count or numerical rating of the occurrence of some property. Examples of measures include lines of code, number of computer runs, person hours expended, and degree of use of top down design methodology. Measures appeal to the researcher and the manager as a potential means of defining, explaining, and predicting software development qualities, especially productivity and reliability.

Card, D. N.↗

NASA NTRS1998

Euler Calculations at Off-Design Conditions for an Inlet of Inward Turning RBCC-SSTO Vehicle

The inviscid performance of an inward turning inlet design is calculated computationally for the first time. Hypersonic vehicle designs based on the inward turning inlets have been shown analytically to have increased effective specific impulse and lower heat load than comparably designed vehicles with two-dimensional inlets. The inward turning inlets are designed inversely from inviscid stream surfaces of known flow fields. The computational study is performed on a Mach 12 inlet design to validate the performance predicted by the design code (HAVDAC) and calculate its off-design Mach number performance. The three-dimensional Euler equations are solved for Mach 4, 8, and 12 using a software package called SAM, which consists of an unstructured mesh generator (SAMmesh), a three-dimensional unstructured mesh flow solver (SAMcfd), and a CAD-based software (SAMcad). The computed momentum averaged inlet throat pressure is within 6% of the design inlet throat pressure. The mass-flux at the inlet throat is also within 7 % of the value predicted by the design code thereby validating the accuracy of the design code. The off-design Mach number results show that flow spillage is minimal, and the variation in the mass capture ratio with Mach number is comparable to an ideal 2-D inlet. The results from the inviscid flow calculations of a Mach 12 inward turning inlet indicate that the inlet design has very good on and off-design performance which makes it a promising design candidate for future air-breathing hypersonic vehicles.

Takashima, N.↗

DOE OSTI2024

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2012

Utilization of the Generalized Method of Cells to Analyze the Deformation Response of Laminated Ceramic Matrix Composites

In order to practically utilize ceramic matrix composites in aircraft engine components, robust analysis tools are required that can simulate the material response in a computationally efficient manner. The MAC/GMC software developed at NASA Glenn Research Center, based on the Generalized Method of Cells micromechanics method, has the potential to meet this need. Utilizing MAC/GMC, the effective stiffness properties, proportional limit stress and ultimate strength can be predicted based on the properties and response of the individual constituents. In this paper, the effective stiffness and strength properties for a representative laminated ceramic matrix composite with a large diameter fiber are predicted for a variety of fiber orientation angles and laminate orientations. As part of the analytical study, methods to determine the in-situ stiffness and strength properties of the constituents required to appropriately simulate the effective composite response are developed. The stiffness properties of the representative composite have been adequately predicted for all of the fiber orientations and laminate configurations examined in this study. The proportional limit stresses and strains and ultimate stresses and strains were predicted with varying levels of accuracy, depending on the laminate orientation. However, for the cases where the predictions did not have the desired level of accuracy, the specific issues related to the micromechanics theory were identified which could lead to difficulties that were encountered that could be addressed in future work.

Goldberg, Robert K.↗

NASA NTRSDate not supplied

Methods for Analytic Reflected Radiant Flux Modeling in Observing Space Vehicles

During NASA’s Artemis I mission, the Orion Optical Navigation (OpNav) camerawas used in a flight test to capture images of the Interim Cryogenic Propulsion Stage(ICPS) at long range. Novel methods in reflectance modeling were developed to esti-mate the reflected light flux as a function of range to inform the camera settings usedto obtain the images. Postflight analysis was performed with the obtained imagesand telemetry. Techniques were developed using a specialized tool developed fromCOTS star identification and attitude determination software to localize ICPS andpositively identify it. The images were then used to verify the preflight prediction ofreflected radiant flux as a function of range and to provide insight into the vehicle’sattitude dynamics.

Spacecraft↗

NASA NTRSDate not supplied

Methods for Analytic Reflected Radiant Flux Modeling in Observing Space Vehicles

spacecraft↗

DOE OSTI2021

Measuring residual stress in Ti-6Al-4V with HR-EBSD, using reference patterns from annealed material

In this study, elastic strain and residual stress values in an engineering material were determined by high-resolution EBSD (HR-EBSD) using reference patterns from annealed, recrystallized material. Specifically, rolled Ti-6Al-4V sheet was analyzed using HR EBSD in both an as-annealed and a strained state, the latter established via a static two-point bend fixture. The commercial software CrossCourt4 (CC4) was used to evaluate the relative intragranular strain tensor components of the alpha grains mapped by HR-EBSD. The intragranular reference patterns identified in each grain by the software were then replaced with similarly-oriented patterns from scans of the as-annealed, recrystallized material. The CC4 analysis was then repeated to provide calibrated strain and residual stress values. Elastic strains on the order of 10–3 were detected, with corresponding residual stress values on the order of 1 GPa. Subsequent analyses using high energy synchrotron x-ray diffraction showed good correlation with the elastic strains measured by HR-EBSD using the annealed reference patterns. Additionally, the maximum macroscopic stress analytically predicted for two-point bending was in agreement with the residual stress observed. Good repeatability of the methodology and insight into the state of individual grains were also observed. The findings provide encouraging evidence that accurate intragranular and long-range residual stress measurements of engineering significance can be made with HR-EBSD using a physical reference pattern library.

36 MATERIALS SCIENCE↗

DOE OSTI2022

How to calculate the rate constants for nonradiative transitions between the M S components of spin multiplets?

Predicting the rates of spin-dependent processes characterized by nonradiative transitions between electronic states with different spin multiplicities is important for understanding the mechanisms of many photochemical and catalytic reactions. To calculate these rates, it is necessary to define the spin state representation and the couplings between these states that drives the interstate transitions. In this work, we describe three different approaches to calculating the spin-orbit coupling (SOC), transition probabilities, and rate constants between the MS components of the electronic states with different spin multiplicities. We implemented these approaches in our nonadiabatic statistical theory (NAST) software package, which predicts the transition probabilities and rate constants of spin-dependent processes using information obtained from electronic structure calculations. Here, we discuss the advantage and drawbacks of each approach and, as an example, calculate the rate constants for transitions between the spin states in the active site model of the protein rubredoxin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1992

Fracture mechanics expert system

Attention is given to fracture mechanics, an analytical method used extensively in the National Space Transportation System to conservatively predict the remaining service life of an article when a flaw or a material defect is detected. These analyses are performed on hardware containing material defects that have been detected by various nondestructive inspection techniques. An expert system being developed to streamline the process so that hardware dispositions may be obtained in a timely and consistent manner is discussed. The expert system reduces the potential for errors due to the manual transcription between the various software programs involved in completing a fracture mechanics analysis. NEXPERT Object, the expert system development shell selected for this purpose, allows the various software programs used in fracture mechanics analyses to be accessed and manipulated from the same platform.

Powers, E.↗