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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Forecast of Wildfire Potential Across California USA Using a Transformer

Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

Limber, Russell [ORNL]↗
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
DOE OSTI2023

Challenges to Rural Service Transformers on Increased Electric Vehicle Charging Infrastructure

As Electric Vehicle (EV) electric charging demand increases so to does the corresponding load requirements for charging the vehicles. The expansion of power distribution assets is therefore a critical issue to address on whether rural service transformers can handle the increase in EV load demand. Large-scale EV deployment is likely to cause problems in the localized distributions systems bringing challenges such as increased load demand, increased system losses, and additional voltage drops. This paper investigates how rural service transformer resiliency plays an important role in these challenges. EV charging can create new load peaks exceeding the service transformer's rated capacity, thereby accelerating equipment aging. EV charging can also both positively and negatively affect transformer aging. The core purpose of this paper is to investigate that change in service life of rural service transformers.

Mukherjee, Srijib↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optical Fiber Sensor Technology Development and Field Validation for Distribution Transformer and Other Grid Asset Health Monitoring

Power transformers are critical pieces of infrastructure in the electric grid that are both extremely expensive and difficult to replace. These transformers often have long lead times for replacement, and failures can create long service disruptions. This project has developed a new suite of sensors designed to give early warning of the impending failure of these important power transformers before it is too late and a major failure occurs. By using novel fiber optic sensors instead of conventional existing technologies, we are able to measure transformer characteristics indicative of impending failures in ways that were not previously possible. These new optical fiber-based sensors are completely immune to the strong magnetic fields present in power transformers, as well as being capable of using distributed measurement techniques. Distributed measurement techniques enable the fiber to return information all along its length as opposed to only collecting data at a single point; as would a thermocouple or standard pressure sensor.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Defining a Platform Approach and Market Participation: Data Driven Business Models for Solid State Transformer-Based Synthetic Inertia and Voltage Stability Controls (CRADA Final Report, Project 1, Mod 1)

The primary objective of this project is to determine the incremental value created with the medium voltage solid-state transformer (MV SST) technology to different stakeholders in view of the updated DER grid regulations. This includes studying the benefits of the MV SST technology in a range of use cases for EV and DER penetration including (1) “corridor charging” for EVs and (2) solar plus storage (FERC 2222). The potential customers of this technology include utilities for EV charging, DER installers who must meet utility interconnection requirements, balancing authorities, and DER aggregators. The traditional transformers on the grid could be a limiting factor for the EV-grid integration as the distribution transformers were not designed to handle the dynamic and fluctuating EV charging loads. Thus, the issues such as voltage fluctuations, increased losses and reduced efficiency [1] can negatively impact the grid operation. To address these challenges, transformers with flexibility and adaptability become imperative to meet the evolving energy demands. In this regard, the concept of Medium Voltage Solid-State Transformers.

14 SOLAR ENERGY↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2022

Pitch-Angle Diffusion in the Earth’s Magnetosphere Organized by the Mozer-Transformed Coordinate System

In the Earth’s dipole magnetosphere finite-gyroradius effects produce a shift of the atmospheric loss cone away from the direction of the magnetic field. This loss-cone shift is theoretically described by the “Mozer transform”, which is based upon the curvature drift of particles crossing the equatorial plane. For positive ions the northern and southern loss cones both shift westward and for electrons the northern and southern loss cones both shift eastward. This loss-cone shift is part of a coordinate-system transform, with the transformed coordinates better organizing the behavior of particle orbits in the dipole magnetic field (e.g. first adiabatic invariants, mirror heights, and bounce times). In this report it is demonstrated that the transformed coordinate system also properly organizes pitch-angle diffusion. This improved organization of the diffusion is true whether the angular scattering is produced by plasma-wave scattering or by field-line-curvature (FLC) scattering. It is shown that FLC scattering and the loss cone shift are linked, so that if FLC scattering is occurring, there is a loss cone shifted away from the magnetic-field direction and the Mozer-transformed coordinates are needed.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Multi-dimensional real Fourier transform

Four subroutines compute one-dimensional and multi-dimensional Fourier transforms for real data, multi-dimensional complex Fourier transforms, and multi-dimensional sine, cosine and sine-cosine transforms. Subroutines use Cooley-Tukey fast Fourier transform. In all but one-dimensional case, transforms are calculated in up to six dimensions.

Krogh, F. T.↗
NASA NTRS1972

Relations between Haar and Walsh/Hadamard transforms.

Relations between the Haar and Walsh/Hadamard (W/H) transforms, which are proved, show that for some applications the Haar transform performs as well as, and faster than, the W/H transform. These relations yield a family of orthogonal transforms including the Haar and W/H transforms with a common fast algorithm.

Fino, B. J.↗
NASA NTRS1973

Pseudocolor transformation of ERTS imagery

One of the photographic techniques which shows great promise as an aid in interpreting ERTS imagery is pseudocolor transformation. It is a process where each shade of gray in an original black-and-white image is seen as a different color in the transformation. The well known ERTS-1 MSS image of the Monterey Bay-San Francisco area was transformed using a technique which requires only two intermediate separations. Possible faults were delineated on an overlay of the transformation before referring to geologic maps. The results were quite remarkable in that all large active or recently active faults shown on the latest geologic map of California were interpreted from the image for all, or much, of their length. Perhaps the most interesting result was the Reliz fault. The fault is shown as covered; however, a lineation corresponding to the position of the fault is visible on the image. The usefulness of ERTS image in identifying recently active faults is demonstrable. Although the faults are also visible in the unenhanced image, they are clearly accentuated and more easily mapped on the pseudocolor transformation.

Lamar, J. V.↗
NASA NTRS1973

Magnetic materials selection for static inverter and converter transformers

A program to study magnetic materials is described for use in spacecraft transformers used in static inverters, converters, and transformer-rectifier supplies. Different magnetic alloys best suited for high-frequency and high-efficiency applications were comparatively investigated together with an investigation of each alloy's inherent characteristics. The materials evaluated were the magnetic alloys: (1) 50% Ni, 50% Fe; (2) 79% Ni, 17% Fe, 4% Mo; (3) 48% Ni, 52% Fe; (4) 78% Ni, 17% Fe, 5% Mo; and (5) 3% Si, 97% Fe. Investigations led to the design of a transformer with a very low residual flux. Tests were performed to determine the dc and ac magnetic properties at 2400 Hz using square-wave excitation. These tests were performed on uncut cores, which were then cut for comparison of the gapped and ungapped magnetic properties. When the data of many transformers in many configurations were compiled the optimum transformer was found to be that with the lowest residual flux and a small amount of air gap in the magnetic material. The data obtained from these tests are described, and the potential uses for the materials are discussed.

Mclyman, W. T.↗
NASA NTRS1974

A note on parallel and pipeline computation of fast unitary transforms

The parallel and pipeline organization of fast unitary transform algorithms such as the Fast Fourier Transform are discussed. The efficiency is pointed out of a combined parallel-pipeline processor of a transform such as the Haar transform in which 2 to the n minus 1 power hardware butterflies generate a transform of order 2 to the n power every computation cycle.

Fino, B. J.↗
NASA NTRS1975

On the use of a coordinate transformation for the solution of the Navier-Stokes equations

The equations of fluid motion have been formulated in a generalized noncartesian, nonorthogonal coordinate system. A particular coordinate transformation, which transforms a domain with an irregular lower boundary into a cube, has been constructed. The transformed system, unlike the original one, has flat boundaries and homogeneous boundary conditions. Where the topography is flat, the original and transformed system are identical, and extra terms do not appear. A finite difference scheme for solving the transformed equations has been constructed and will be described later.

Gal-Chen, T.↗
NASA NTRS1975

Parallel and pipeline computation of fast unitary transforms

The letter discusses the parallel and pipeline organization of fast-unitary-transform algorithms such as the fast Fourier transform, and points out the efficiency of a combined parallel-pipeline processor of a transform such as the Haar transform, in which (2 to the n-th power) -1 hardware 'butterflies' generate a transform of order 2 to the n-th power every computation cycle.

Fino, B. J.↗
NASA NTRS1976

The fast decoding of Reed-Solomon codes using number theoretic transforms

It is shown that Reed-Solomon (RS) codes can be encoded and decoded by using a fast Fourier transform (FFT) algorithm over finite fields. The arithmetic utilized to perform these transforms requires only integer additions, circular shifts and a minimum number of integer multiplications. The computing time of this transform encoder-decoder for RS codes is less than the time of the standard method for RS codes. More generally, the field GF(q) is also considered, where q is a prime of the form K x 2 to the nth power + 1 and K and n are integers. GF(q) can be used to decode very long RS codes by an efficient FFT algorithm with an improvement in the number of symbols. It is shown that a radix-8 FFT algorithm over GF(q squared) can be utilized to encode and decode very long RS codes with a large number of symbols. For eight symbols in GF(q squared), this transform over GF(q squared) can be made simpler than any other known number theoretic transform with a similar capability. Of special interest is the decoding of a 16-tuple RS code with four errors.

Reed, I. S.↗
NASA NTRS1977

Transformation theory and translation factors in inelastic atomic collisions

It is shown through the use of transformation theory that unique semiclassical atomic scattering states which obey the asymptotic conditions of formal scattering theory can be derived by transforming 'nontraveling' atomic states, ie., states whose coordinate variables are referred to a stationary origin, to frames at rest with respect to the incoming or outgoing particles. An overview of the problem of properly defining such scattering states is presented. The operator which carries out the necessary transformation from inertial to noninertial frames is derived and its properties are discussed. The relation of this transformation operator to the 'translation factor' discussed in the literature is presented. The application of this operator to transform the time-dependent Schroedinger equation from an inertial to a noninertial frame is presented and shown to introduce new terms in the resulting equation. The implications of these new terms to scattering problems are discussed.

Schmid, G. B.↗
NASA NTRS1978

Performance evaluation for transform coding using a nonseparable covariance model

Intraframe transform coding of pictures for the case of a nonseparable covariance model is considered. Performances of the Walsh-Hadamard, discrete-cosine, and Karhunen-Loeve transforms are compared based on the compaction of signal energy in the transform components and the degree of decorrelation of the data. The results demonstrate that the performances of the discrete-cosine and Karhunen-Loeve transforms compare closely, as is the case with a separable covariance model. The corresponding performance of the Walsh-Hadamard transform is inferior.

Natarajan, T. R.↗