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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Mars vertical axis wind machines: The design of a tornado vortex machine for use on Mars

Ever since Viking 1 and 2 landed on the surface of Mars in the summer of 1976, man has yearned to go back. But before man steps foot upon the surface of Mars, unmanned missions such as the Martian Soft Lander and Martian Subsurface Penetrator will precede him. Alternative renewable power sources must be developed to supply the next generation of surface exploratory spacecraft, since RTG's, solar cells, and long-life batteries all have their significant drawbacks. One such alternative is to take advantage of the unique Martian atmospheric conditions by designing a small scale, Martian wind power generator, capable of surviving impact and fulfilling the long term (2-5 years), low-level power requirements (1-2 Watts) of an unmanned surface probe. After investigation of several wind machines, a tornado vortex generator was chosen based upon its capability of theoretically augmenting and increasing the available power that may be extracted from average Martian wind speeds of approximately 7.5 m/s. The Martian Tornado Vortex Wind Generator stands 1 meter high and has a diameter of 0.5 m. Martian winds enter the base and shroud of the Tornado Vortex Generator at 7.5 m/s and are increased to an exit velocity of 13.657 m/s due to the vortex that is created. This results in a rapid pressure drop of 4.56 kg/s(exp 2) m across the vortex core which aids in producing a net power output of 1.1765 Watts. The report contains the necessary analysis and requirements needed to feasibly operate a low-level powered, unmanned, Martian surface probe.

Carlin, Daun↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
DOE OSTI2022

Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy

To recycle the mixed plastic wastes (MPW), it is important to obtain the compositional information online in real time. We present a sensing framework based on a convolutional neural network (CNN) and mid-infrared spectroscopy (MIR) for the rapid and accurate characterization of MPW. The MPW samples are placed on a moving platform to mimic the industrial environment. The MIR spectra are collected at the rate of 100 Hz, and the proposed CNN architecture can reach an overall prediction accuracy close to 100%. Therefore, the proposed method paves the way toward the online MPW characterization in industrial applications where high throughput is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗
DOE OSTI2024

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗
DOE OSTI2018

Metal Amorphous Nanocomposite Soft Magnetic Material-Enabled High Power Density, Rare Earth Free Rotational Machines [Metal Amorphous Nanocomposite (MANC) Soft Magnetic Material (SMM) Enabled High Power Density, Rare Earth Free Rotational Machines]

Metal amorphous nanocomposites (MANCs) are promising soft magnetic materials (SMMs) for power electronic applications offering low power loss at high frequency and maintaining a relatively high flux density. While applications in certain motor designs have been recently modeled, their widespread application awaits scaled manufacturing of MANC materials and proliferation of new higher speed motor designs. A hybrid motor design based on permanent magnets and doubly salient stator and rotor is reported here to develop a compact (a factor of 10 smaller than currently possible in Si steels), high-speed (>1 kHz, electrical), high-power (>2.5 kW) motor by incorporating low loss (<10 W/kg at 1 kHz) MANCs such as recently reported Fe-Ni-based alloys. A feature of this motor design is flux focusing from the permanent magnet allowing use of lower energy permanent magnet chosen from among non-rare earth containing compositions and attractive due to constraints posed by rare earth criticality. A 2-D finite element analysis model reported here indicates that a 2.5 kW hybrid motor may be built with a permanent magnet with a 0.4 T remanence at a rotor speed of 6000 rpm. At a magnetic switching frequency of 1.4 kHz, the core loss may be limited to <3 W by selecting an appropriate MANC SMM. The projected efficiency exceeds 96% not including power loss in the controller. Under full load conditions, the flux density distributions for the SMM stay predominantly <1.3 T, the saturation magnetization of optimized FeNi-based MANC alloys. As a result, the maximum demagnetizing field in the permanent magnet is less than 2.2 × 10 5 A/m sustainable, for example, with a high-grade hard ferrite magnet.

42 ENGINEERING↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Biochemistry & Molecular Biology↗