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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

New Hybrid Model for Evaluating the Frequency-Dependent Leakage Inductance of a Variable Inductance Transformer (VIT)

Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the lowfrequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell’s 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell’s model with the double-2-D model. The proposed model is investigated on a variable inductance transformer (VIT)—a partially-filled transformer whose leakage inductance can be varied by moving one of the windings mechanically. The frequency-dependent leakage inductances of the VIT evaluated using the hybrid model are in excellent agreement with the corresponding finite element method (FEM) simulated and experimentally measured values, thereby validating the proposed hybrid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

prune_quant_vit

This tool provides codes to prune and quantize vision transformers (ViTs). The tool will allow developers and researchers to speed up inference of ViTs and deploy them on CPUs.

Bhardwaj, Kshitij↗
NASA NTRS1994

Vibration Isolation Technology (VIT) ATD Project

A fundamental advantage for performing material processing and fluid physics experiments in an orbital environment is the reduction in gravity driven phenomena. However, experience with manned spacecraft such as the Space Transportation System (STS) has demonstrated a dynamic acceleration environment far from being characterized as a 'microgravity' platform. Vibrations and transient disturbances from crew motions, thruster firings, rotating machinery etc. can have detrimental effects on many proposed microgravity science experiments. These same disturbances are also to be expected on the future space station. The Microgravity Science and Applications Division (MSAD) of the Office of Life and Microgravity Sciences and Applications (OLMSA), NASA Headquarters recognized the need for addressing this fundamental issue. As a result an Advanced Technology Development (ATD) project was initiated in the area of Vibration Isolation Technology (VIT) to develop methodologies for meeting future microgravity science needs. The objective of the Vibration Isolation Technology ATD project was to provide technology for the isolation of microgravity science experiments by developing methods to maintain a predictable, well defined, well characterized, and reproducible low-gravity environment, consistent with the needs of the microgravity science community. Included implicitly in this objective was the goal of advising the science community and hardware developers of the fundamental need to address the importance of maintaining, and how to maintain, a microgravity environment. This document will summarize the accomplishments of the VIT ATD which is now completed. There were three specific thrusts involved in the ATD effort. An analytical effort was performed at the Marshall Space Flight Center to define the sensitivity of selected experiments to residual and dynamic accelerations. This effort was redirected about half way through the ATD focusing specifically on the sensitivity of protein crystals to a realistic orbital environment. The other two thrusts of the ATD were performed at the Lewis Research Center. The first was to develop technology in the area of reactionless mechanisms and robotics to support the eventual development of robotics for servicing microgravity science experiments. This activity was completed in 1990. The second was to develop vibration isolation and damping technology providing protection for sensitive science experiments. In conjunction with the this activity, two workshops were held. The results of these were summarized and are included in this report.

Lubomski, Joseph F.↗
NASA NTRS1974

Reflectance and optical constants for Cer-Vit from 250 to 1050 A

The reflectance for a bowl-feed polished Cer-Vit sample was measured at nine wavelengths and five angles of incidence from 15 to 85 deg. Optical constants were derived by the reflectance-vs-angle-of-incidence method and compared to previously reported values for ultralow-expansion fused silica and several other glasses. Surface-roughness corrections of the reflectance data and optical constants are discussed.

Osantowski, J. F.↗
DOE OSTI2023

The vacuolar iron transporter mediates iron detoxification in Toxoplasma gondii

Iron is essential to cells as a cofactor in enzymes of respiration and replication, however without correct storage, iron leads to the formation of dangerous oxygen radicals. In yeast and plants, iron is transported into a membrane-bound vacuole by the vacuolar iron transporter (VIT). This transporter is conserved in the apicomplexan family of obligate intracellular parasites, including in Toxoplasma gondii . Here, we assess the role of VIT and iron storage in T. gondii . By deleting VIT, we find a slight growth defect in vitro, and iron hypersensitivity, confirming its essential role in parasite iron detoxification, which can be rescued by scavenging of oxygen radicals. We show VIT expression is regulated by iron at transcript and protein levels, and by altering VIT localization. In the absence of VIT, T. gondii responds by altering expression of iron metabolism genes and by increasing antioxidant protein catalase activity. We also show that iron detoxification has an important role both in parasite survival within macrophages and in virulence in a mouse model. Together, by demonstrating a critical role for VIT during iron detoxification in T. gondii , we reveal the importance of iron storage in the parasite and provide the first insight into the machinery involved.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sequence length scaling in vision transformers for scientific images on frontier

Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text have inspired scaling sequence lengths in ViTs, adapting these for ViTs introduces unique challenges. We develop distributed sequence parallelism for ViTs, enabling them to handle up to 1M tokens. Our approach, leveraging DeepSpeed-Ulysses and Long-Sequence-Segmentation with model sharding, is the first to apply sequence parallelism in ViT training, achieving a 94% batch scaling efficiency on 2,048 AMD-MI250X GPUs. Evaluating sequence parallelism in ViTs, particularly in models up to 10B parameters, highlighted substantial bottlenecks. We countered these with hybrid sequence, pipeline, and flash attention strategies, to scale beyond single GPU memory limits. Our method significantly enhances climate modeling accuracy by 20% in temperature predictions, marking the first training of a vision transformer model to convergence with a sequence length of 188K tokens, using full self-attention.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2025

Permeability Prediction Using Vision Transformers

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows.

58 GEOSCIENCES↗
DOE OSTI2025

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗
DOE OSTI2024

Hyperparameter Studies for Vision Transformers Trained on High-Fidelity Simulations

This library is a collection of python modules that define, train, and analyze vision-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The training data for these models is hydrodynamic simulation output in the form of numpy arrays. This library contains code to train these ViT models on the hydrodynamic simulation output with a variety of hyperparameters, and to compare the results of such models. Furthermore, the library contains definitions of simple convolutional neural network (CNN) machine learning architectures which can be trained on the same hydrodynamic simulation output. These are included as a reference point to compare the ViT models to. Additionally, the library includes trained ViT and CNN models and example input data for demonstration purposes. The code is based on the PyTorch python library.

Callis, Skylar↗
DOE OSTI2021

Novel Transformer with Variable Leakage and Magnetizing Inductances

This paper introduces the concept of a novel variable inductance transformer (VIT) whose leakage and magnetizing inductances can be varied dynamically and independently to aid the advancement of research in isolated power electronic converters. In this transformer, the leakage inductance can be varied by varying the length of overlap between the primary and secondary windings, while the magnetizing inductance can be varied by varying the air gap between the core legs. Individual controls for the two inductances ensure that one can be varied independent of the other, as needed to satisfy different power conversion objectives. In this paper, the analytical models of the variable leakage and magnetizing inductances are presented. Analytical results are compared with those obtained from Finite Element Method (FEM) and an experimental prototype of a VIT. Analytical solutions of the variable leakage inductance present an error of less than 2.35 % when compared to FEM solutions, while the analytical solutions of the variable magnetizing inductance present a maximum error of 1.82 % when compared to experimental measurements.

image method, leakage inductance, magnetizing indu↗
DOE OSTI2024

Vistransformers Explained

The Vistransformers Explained library is a collection of python notebooks that demonstrate the internal mechanics and uses of visual-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The value added by this code is in-depth explanations of the mathematics behind the sub-modules of the ViT models, including original figures. Additionally, the library contains the code necessary to implement and train the ViT models. The library does not include example training data for the models; instead, it would rely on users generating their own datasets. The code is based on the PyTorch python library. It does not include any files other than python scripts, modules, or notebooks.

Callis, Skylar↗
DOE OSTI2026

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

AC Calorimetry and Thermophysical Properties of Bulk Glass-Forming Metallic Liquids

Thermo-physical properties of two bulk metallic glass forming alloys, Ti34Zr11Cu47Ni8 (VIT 101) and Zr57Nb5Ni12.6Al10CU15.4 (VIT 106), were investigated in the stable and undercooled melt. Our investigation focused on measurements of the specific heat in the stable and undercooled liquid using the method of AC modulation calorimetry. The VIT 106 exhibited a maximum undercooling of 140 K in free radiative cooling. Specific heat measurements could be performed in stable melt down to an undercooling of 80 K. Analysis of the specific heat data indicate an anomaly near the equilibrium liquidus temperature. This anomaly is also observed in y the temperature dependencies of the external relaxation time, the specific volume, and the surface tension; it is tentatively attributed to a phase separation in the liquid state. The VIT 101 specimen exhibited a small undercooling of about 50 K. Specific heat measurements were performed in the stable and undercooled melt. These various results will be combined with ground based work such as the measurement of T-T-T curves in the electrostatic levitator and low temperature viscosity and specific heat measurements for modeling the nucleation kinetics of these alloys.

Johnson, William L.↗
DOE OSTI2023

Introduction of a Variable Inductance Transformer for the Design of Resonant Power Converters

Magnetic integration is a hot topic in power electronics that concerns the use of a transformer’s leakage and magnetizing inductances purposefully in isolated power electronic converters, thereby giving the opportunity to save the cost and footprint of any additional inductor. This is of prime interest, especially in CLLLC resonant converters which require up to three inductors. For a complete integration of these inductances, the concept of a variable inductance transformer (VIT) is introduced in this thesis. A VIT is an adaptive magnetic structure that facilitates an easy adjustment of both magnetizing and leakage inductances to meet their desired values. However, for a more promising design, an accurate estimation of these inductances is necessary. While the evaluation of magnetizing inductance is quite straightforward, the calculation of leakage inductance is rather convoluted, because the leakage inductance is influenced by both the winding layout and the operating frequency. In this thesis, three new semi-analytical methods for calculating the frequency-independent leakage inductance, and a novel semi-analytical method for evaluating the frequency-dependent leakage inductance are proposed. These methods can calculate the respective leakage inductances of a VIT within an outstanding ±5% uncertainty. Finally, a bidirectional CLLLC resonant dc-dc converter is investigated for the constant current constant voltage (CCCV) charging of the next-generation 900 V traction battery of an electric vehicle. A new voltage gain equation is derived for designing the CLLLC resonant tank, and a small-signal model is presented for designing the variable-frequency feedback controller. Furthermore, a new methodology to design a VIT is developed to overcome the challenges associated with small coupling coefficients and guarantee a complete magnetic integration of the tank inductances. All theoretical results presented herein are verified through simulations and experiments performed on hardware prototypes designed in the lab.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗