Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data transfer”

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.

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

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. Here, we devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data

Abstract Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R 2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Estimating soil thermal inertia profiles from the passive equilibration of a temperature probe: Supporting data and code

This dataset contains measurements of soil thermal properties and organic layer thickness at three field sites on the Seward Peninsula near Nome, Alaska, USA. Code is also provided for estimation of soil thermal properties from temperature data, including a forward heat transfer mode, a library of simulated temperature data, and an inversion scheme to retrieve thermal properties from temperature measurements (Lamb et al., 2025). This dataset and code is published in support of the manuscript “Estimating Soil Thermal Inertia Profiles from the Passive Equilibration of a Temperature Probe” by Lamb et al., 2025, in which an experimental thermal measurements technique is developed. The dataset contains measurements of soil thermal properties and organic layer thickness obtained with an industry standard thermal properties analyzer and direct observations, as well as data generated using the experimental technique to estimate thermal properties and soil structure from temperature observations. All data is reported in .csv format, and all code is written in Python 3.7 using standard packages. This research was performed as a part of the NGEE Arctic project, which aims to advance model predictions of arctic carbon cycle responses to a changing climate over the 21st Century.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

58 GEOSCIENCES↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
DOE OSTI2024

The g-code file

The slicing process for additive manufacturing (AM) involves many steps, including slicing the object to be printed and fitting toolpaths to each resulting layer. The toolpaths define the trajectories that the machine must follow during printing to construct the object. Once the slicing is complete and all toolpaths have been found and properly planned, this data must be transferred to the 3D printer in a way the printer can use to construct the object. This is achieved by exporting a file, usually a g-code file, from the slicing software. The g-code file is a text-based file that contains instructions for the machine, controlling the system and defining the motions for each toolpath. The file, which often contains thousands of lines of code, is mostly composed of numbers that command positions for axis motions. This chapter will outline the contents of a g-code file, discuss various commands and their implementation, then provide example codes and files for object construction.

Roschli, Alex↗
DOE OSTI2025

Rate Limiting Regimes in Photochemical H2 Generation by Complexes of Colloidal CdS Nanorods and Hydrogenase

Driving redox enzyme catalysis with photoexcited semiconductor nanocrystals is a compelling approach for chemical conversion. We examined how the interplay of the many chemical steps involved determines the rates of photochemical H2 production with complexes of colloidal CdS nanorods and an [FeFe]-hydrogenase. We elucidated the roles of three critical and previously elusive processes-scavenging of photoexcited holes from nanorods, back-electron transfer, and H2 oxidation. Kinetic Monte Carlo simulations and fitting to experimental data revealed that hole transfer becomes the rate-limiting step at high illumination intensities. Comparisons of simulations to experimental H2 production showed that both back-electron transfer and H2 oxidation play an efficiency-limiting role at high catalyst loadings. This work provides guiding principles for tuning experimental parameters to minimize energy-wasting pathways and optimize photochemical product formation. More broadly, we demonstrate how critical but elusive chemical steps in photochemical reactions can be probed with a combination of experiments and simulations.

08 HYDROGEN↗
DOE OSTI2025

Inclusive studies of two- and three-nucleon short-range correlations in 3 H and 3 He

Inclusive electron scattering at carefully chosen kinematics can isolate scattering from the high-momentum nucleons in short-range correlations (SRCs). SRCs are produced by the hard, short-distance interactions of nucleons in the nucleus, and because the two-nucleon (2N) SRCs arise from the same N-N interaction in all nuclei, the cross section in the SRC-dominated regime is identical up to an overall scaling factor. This scaling behavior has been used to identify SRC dominance and to measure the contribution of SRCs in a wide range of nuclei. We examine this scaling behavior over a range of momentum transfers using new data on 2 H, 3 H, and 3 He, and find an expanded scaling region compared to heavy nuclei. Motivated by this improved scaling, we examine the 3 H and 3 He data in kinematics where three-nucleon SRCs may play an important role. The data for the largest struck nucleon momenta are consistent with isolation of scattering from three-nucleon SRCs, and suggest that the very highest momentum nucleons in 3 He have a nearly isospin-independent momentum configuration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Physicochemical and Molecular Insights into the Boundary Layer and Free Troposphere Aerosol Interactions over the Southern Great Plains

Ambient aerosols’ vertical profiles are critical for evaluating the role of aerosols in atmospheric chemistry and radiative transfer, but limited data on these profiles hinders our ability to fully assess their impact on the Earth's radiative balance. Here, in this study, we investigated the size-, time-, and altitude resolved composition of individual particles and bulk molecular composition of particle samples collected by an uncrewed aerial system–ArcticShark over the Southern Great Plains. Single particle microanalysis shows that, the free tropospheric (FT) samples are dominated (56-66%) by carbonaceous sulfate particles, while boundary layer (BL) samples are dominated (57-74%) by carbonaceous particles. Back trajectory simulations suggest that FT particles are likely influenced by long-range transport and have undergone aqueous-phase processing. Conversely, in-situ size distribution data shows evidence of particle growth in the upper BL and just below the FT. This observation may indicate vertical transport of particles from an elevated aerosol layer in the FT, possibly linked to a new particle formation event. This observation is further supported by high resolution molecular composition data, which reveals particle volatility increasing with increasing size, which aligns with the growth event. This study aids in fundamental understanding of the compositional and molecular specificity of vertically resolved organic aerosols to provide insights into particle size evolution for future atmospheric models.

ArcticShark↗
DOE OSTI2024

Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

Energy absorbing efficiency is a key determinant of a structure’s ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

42 ENGINEERING↗
DOE OSTI2021

Detecting operons in bacterial genomes via visual representation learning

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

On wavelength-dependent exciton lifetime distributions in reconstituted CP29 antenna of the photosystem II and its site-directed mutants

To provide more insight on the excitonic structure and exciton lifetimes of the wild type (WT) CP29 complex of Photosystem II (PSII) we measured high-resolution (low temperature) absorption, emission, and hole burned spectra for the A2 and B3 mutants, which lack chlorophylls a612 and b614 (Chls), respectively. Experimental and modeling results obtained for the WT CP29 and A2/B3 mutants provide new insight on the mutation-induced changes at the molecular level and shed more light on energy transfer dynamics. Simulations of the A2 and B3 optical spectra, using the second-order non-Markovian theory, and comparison with improved fits of WT CP29 optical spectra, provide more insight on their excitonic structure, mutation induced changes, and frequency-dependent distributions of exciton lifetimes (T 1 ). A new Hamiltonian obtained for WT CP29 reveals that deletion of Chls a612 or b614 induces changes in the site-energies of all remaining Chls. Hamiltonians obtained for A2 and B3 mutants are discussed in the context of the energy landscape of chlorophylls, excitonic structure and the transfer kinetics. Our data suggest that the lowest exciton states in A2 and B3 mutants are contributed by a611(57%), a610(17%), a615(15%) and a615(58%), a611(20%), a612(15%) Chls, respectively, although other compositions of lowest energy states are also discussed. Lastly, we argue that the calculated exciton decay times are consistent with both the hole-burning and recent transient absorption measurements. Wavelength-dependent T 1 distributions offer more insight into the interpretation of kinetic traces commonly described by discrete exponentials in global analysis/global fitting of transient absorption experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

Rodiles Delgado, Brian G.↗
DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2022

The mitochondrial Cu + transporter PiC2 (SLC25A3) is a target of MTF1 and contributes to the development of skeletal muscle in vitro

The loading of copper (Cu) into cytochrome c oxidase (COX) in mitochondria is essential for energy production in cells. Extensive studies have been performed to characterize mitochondrial cuproenzymes that contribute to the metallation of COX, such as Sco1, Sco2, and Cox17. However, limited information is available on the upstream mechanism of Cu transport and delivery to mitochondria, especially through Cu-impermeable membranes, in mammalian cells. The mitochondrial phosphate transporter SLC25A3, also known as PiC2, binds Cu + and transports the ion through these membranes in eukaryotic cells, ultimately aiding in the metallation of COX. We used the well-established differentiation model of primary myoblasts derived from mouse satellite cells, wherein Cu availability is necessary for growth and maturation, and showed that PiC2 is a target of MTF1, and its expression is both induced during myogenesis and favored by Cu supplementation. PiC2 deletion using CRISPR/Cas9 showed that the transporter is required for proliferation and differentiation of primary myoblasts, as both processes are delayed upon PiC2 knock-out. The effects of PiC2 deletion were rescued by the addition of Cu to the growth medium, implying the deleterious effects of PiC2 knockout in myoblasts may be in part due to a failure to deliver sufficient Cu to the mitochondria, which can be compensated by other mitochondrial cuproproteins. Co-localization and co-immunoprecipitation of PiC2 and COX also suggest that PiC2 may participate upstream in the copper delivery chain into COX, as verified by in vitro Cu + -transfer experiments. These data indicate an important role for PiC2 in both the delivery of Cu to the mitochondria and COX, favoring the differentiation of primary myoblasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗