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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Towards Energy-Positive Buildings through a Quality-Matched Energy Flow Strategy

Current strategies for net-zero buildings favor envelopes with minimized aperture ratios and limiting of solar gains through reduced glazing transmittance and emissivity. This load-reduction approach precludes strategies that maximize on-site collection of solar energy, which could increase opportunities for net-zero electricity projects. To better leverage solar resources, a whole-building strategy is proposed, referred to as “Quality-Matched Energy Flows” (or Q-MEF): capturing, transforming, buffering, and transferring irradiance on a building’s envelope—and energy derived from it—into distributed end-uses. A mid-scale commercial building was modeled in three climates with a novel Building-Integrated, Transparent, Concentrating Photovoltaic and Thermal fenestration technology (BITCoPT), thermal storage and circulation at three temperature ranges, adsorption chillers, and auxiliary heat pumps. BITCoPT generated electricity and collected thermal energy at high efficiencies while transmitting diffuse light and mitigating excess gains and illuminance. The balance of systems satisfied cooling and heating demands. Relative to baselines with similar glazing ratios, net electricity use decreased 71% in a continental climate and 100% or more in hot-arid and subtropical-moderate climates. Total EUI decreased 35%, 83%, and 52%, and peak purchased electrical demands decreased up to 6%, 32%, and 20%, respectively (with no provisions for on-site electrical storage). Decreases in utility services costs were also noted. These results suggest that with further development of electrification the Q-MEF strategy could contribute to energy-positive behavior for projects with similar typology and climate profiles.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2021

A Bilevel Voltage Regulation Operation for Distribution Systems With Self-Operated Microgrids

The emerging of microgrids in distribution systems has significantly enhanced the resilience of power grids. However, the operators of a distribution system and microgrids therein can be different and have accessibility to different devices. To model the operation of such a grid, this work proposes a bilevel formulation and probes into the voltage regulation operation, considering the interaction between different systems. The proposed bilevel formulation considers the cooperation of active energy resources (AER), transformer tap-changers, and capacitor banks that are controlled by different operators. To facilitate the solution time of the target bilevel optimization, the lower-level problems with different objectives are modeled using deep neural networks (DNNs) which are then converted into a set of constraints. Hence, the bilevel problem can be reformed to a single-level problem. Lastly, the proposed solution procedures are validated using a customized joint system constructed by the IEEE 123-bus system and a real distribution system in Iowa. According to the numerical validation results, the solution time of the proposed nonlinear activation function based DNN model is 69 times faster than other methods in solving voltage regulation with a bilevel structure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

System-size dependence of the charged-particle pseudorapidity density at $\sqrt{s_{NN}}$ = 5.02 TeV for pp, p–Pb, and Pb–Pb collisions

We present the first systematic comparison of the charged-particle pseudorapidity densities for three widely different collision systems, pp, p–Pb, and Pb–Pb, at the top energy of the Large Hadron Collider ($\sqrt{s_{NN}}$ = 5.02 TeV) measured over a wide pseudorapidity range (-3.5 < η < 5), the widest possible among the four experiments at that facility. The systematic uncertainties are minimised since the measurements are recorded by the same experimental apparatus (ALICE). The distributions for p–Pb and Pb–Pb collisions are determined as a function of the centrality of the collisions, while results from pp collisions are reported for inelastic events with at least one charged particle at midrapidity. The charged-particle pseudorapidity densities are, under simple and robust assumptions, transformed to charged-particle rapidity densities. This allows for the calculation and the presentation of the evolution of the width of the rapidity distributions and of a lower bound on the Bjorken energy density, as a function of the number of participants in all three collision systems. We find a decreasing width of the particle production, and roughly a smooth ten fold increase in the energy density, as the system size grows, which is consistent with a gradually higher dense phase of matter

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Guest Editorial Special Section on Advanced Medium-Voltage Power Electronics for Grid Interactive Applications

Medium-voltage power electronics (MVPE) plays essential roles in power grid modernization and links the MV distribution grid with low-voltage consumers and prosumers. Various MVPE devices, such as solid-state transformers or circuit breakers, inverter-based resources, power flow controllers, etc., bring the benefits of voltage conversion and power regulation in small footprint, power quality and efficiency improvements, and enhancements of grid controllability, flexibility, stability, and resilience. The MVPE also makes it possible for sustainable energy systems, such as solar/wind farms and energy storage generating facilities, to directly access to MV grids without multistage conversions. With their intrinsic intelligence and communications, MVPE enables many new smart grid functions and applications, e.g., dc interconnections and electric vehicle charging, which were not envisioned by traditional power grids otherwise. In addition, the integration of physical power processing units with cyber components forms a cyber-physical system, which is essential for long-term sustainability, development, and environmental preservation. Nonetheless, technical challenges on MVPE device reliability, scalable and efficient converter topologies, control stability, large-scale modeling and simulation, to name a few, need to be addressed and advanced to the next level. In conclusion, this Special Section on Advanced MV Power Electronics for Grid Interactive Applications in IEEE Transactions on Power Electronics (TPEL) provides an insight on some of the recent advances in MVPE and emerging challenges and potential solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multiport Control with Partial Power Processing in Solid-State Transformer for PV, Storage, and Fast-Charging Electric Vehicle Integration

This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.

14 SOLAR ENERGY↗
DOE OSTI2025

Spatial Distribution and Clustering of Glycosaminoglycans in Electrospun Gelatin-Based Scaffolds

The extracellular matrix (ECM) is comprised of components like collagen, elastin, and glycosaminoglycans (GAGs). Electrospun fibrous scaffolds are designed to replicate the form and composition of the native ECM, often requiring blending of various ECM component mimics to enhance cellular responses. However, the spatial distribution of blended components within these fibers remains unclear. This study investigates the spatial distribution of chondroitin sulfate-C (CSC) in electrospun gelatin-based scaffolds. scanning electron microscopy (SEM), attenuated reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Time-of-flight Secondary Ion Mass Spectrometry (ToF-SIMS) were applied for surface and subsurface chemical characterization of the fibrous scaffolds. SEM confirmed a fibrous morphology, while ATR-FTIR and XPS analyses indicated the presence of CSC through the identification of sulfate groups. ToF-SIMS imaging, alongside K-means clustering and Ripley’s K function, revealed a nonuniform CSC distribution with higher concentrations at the top layer of the scaffold. This study demonstrates that CSC presentation at the fiber surface varies with depth and differs from bulk incorporation while reveals nanoscale clustering and spatial heterogeneity at both the surface and subsurface of electrospun gelatin fibers. These findings define an underexplored design consideration with potential to influence cell–scaffold interactions.

Animal derived food↗
DOE OSTI2021

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.

behind-the-meter↗
DOE OSTI2024

Heteroatom anchoring to enhance electrochemical reversibility for high-voltage P2-type oxide cathodes of sodium-ion batteries

P2-type cathode has received extensive attention due to its faster Na+ diffusion and a high theoretical capacity in sodium-ion batteries (SIBs). However, undesirable phase transformations have induced dramatic capacity decay of SIBs during the cycling process. In this study, heteroatom anchoring through Cu/Mg dual doping is introduced into P2-type Na 0.67 Ni 0.33 Mn 0.67 O 2 cathode to enhance high-voltage electrochemical reversibility and modulate interfacial Na + kinetics. Further, the as-prepared Na 0.67 Ni 0.23 Mg 0.05 Cu 0.05 Mn 0.67 O 2 exhibits an outstanding capacity retention (83.4% after 2000 cycles at 10C) and rate performance (73 mAh g -1 at 10C, accounting for 58.7% of that at 0.1 C) over the voltage range of 2.5–4.4 V. Intensive explorations further manifest that the modified mechanism of dual-ion doping strategy is attributed to the synergistic coupling effect of a substantial change in Na occupancy distribution and an increase in oxygen vacancy buffer. Thus, the optimized cathode expedites Na + diffusion and reduces detrimental phase transformation, which favors high-rate performance and long-term cycling stability. This study develops a route to rationally design high-voltage cathode materials for SIBs.

25 ENERGY STORAGE↗
DOE OSTI2026

Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Additively Manufacturing Nitinol Shape Memory Alloys for Advanced Actuator Designs

The objective of this research was to understand the role of feedstock production in the phase transformation behavior of additively manufactured Ni-Ti alloys for advanced actuator design. Industrial adoption of additively manufactured Ni-Ti alloys depends on the ability to produce repeatable phase transformation behavior, quantified here by the austenite to martensite transformation on heating. Small variations in the alloy composition may have a significant effect on the temperature at which this transformation occurs. This project showed that the powder characteristics play an important role in determining this behavior. Increases in the surface area per unit volume of the powder, either as a function of size distribution or morphology, have the effect of reducing the Ti content in the alloy through the formation of Ti-rich oxides on the powder surface, which has the effect of depressing the transformation temperature. Preferential Ni vaporization during additive manufacturing can partially offset this effect. To achieve repeatable results, it is important to understand the effect of powder oxidation, and to control the powder characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Power and Communications Hardware-in-the-Loop CPS Architecture and Platform for DER Monitoring and Control Applications: Preprint

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. The deployment of communications and computation has transformed the traditional physical power grid into a smart cyber-physical system (CPS). To fully understand the interdependency between physical grid and cyber netowrks, this study designed a power and communications hardware-in-the-loop (PCommHIL) CPS architecture, which enables the flexible verification of DER monitoring and control with hybrid communications architectures and Internet protocols. Design, development and case study of a PCommHIL testbed for the DER coordination are discussed in detail, and the proposed platform integrates DER devices, Advanced Metering Infrastructures (AMIs), and a suite of hybrid communications networks for distribution automation applications. Case study on DER situational awareness and Volt-Var control validates the efficacy of this proposed PCommHIL platform with hybrid communications designs. Results show that the HAN communication technologies play a critical role in hybrid designs and it is the bottleneck for DER applications. High performance communication technologies are highly recommended to be applied in the HAN for enhanced monitoring and real-time control of DERs.

AMIs↗
DOE OSTI2025

Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data

We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using nonstationary Gaussian processes. The Gaussian process prior mean and kernel functions are designed to mitigate well-known numerical challenges with the Fourier transform, including discrete measurement binning and detector windowing, while encoding fundamental yet minimal physical knowledge of the liquid structure. We demonstrate uncertainty propagation of the Gaussian process posterior to unmeasured quantities of interest. Experimental radial distribution functions of liquid argon and water with uncertainty quantification are provided as both a proof of principle for the method and a benchmark for molecular models.

Chemical structure↗
DOE OSTI2026

Robustness of the Ensemble Score Filter to the Type of Assimilated Observation Networks

Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it remains unclear how the performance of these advanced methods depends on the observation network characteristics. In this study, we present initial experiments with the surface quasi‐geostrophic model, in which we compare a recently developed ensemble filter using score‐based diffusion models with the standard Local Ensemble Transform Kalman Filter (LETKF). Our results show that the analysis solutions respond differently to the number, spatial distribution, and nonlinear fraction of assimilated observations. We also find notable changes in the multiscale characteristics of the analysis errors. Given that standard DA techniques will eventually be replaced by more advanced methods, we hope this study sets the ground for future efforts to reassess the value of Earth observing systems in the context of newly emerging algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2021

Co-precipitation induces changes to iron and carbon chemistry and spatial distribution at the nanometer scale

Association of organic matter (OM) with mineral phases via co-precipitation is expected to be a widespread process in environments with high OM input and frequent mineral dissolution and re-precipitation. In contrast to surface area-limited adsorption processes, co-precipitation may allow for greater carbon (C) accumulation. However, the potential sub-micrometer scale structural and compositional differences that affect the bioavailability of co-precipitated C are largely unknown. In this work, we used a combination of high-resolution analytical electron microscopy and bulk spectroscopy to probe interactions between a mineral phase (ferrihydrite, nominally Fe 2 O 3 •0.5H 2 O) and organic soil-derived water-extractable OM (WEOM). In co-precipitated WEOM-Fe, nanometer-scale scanning transmission electron microscopy with electron energy loss spectroscopy (STEM-EELS) revealed increased Fe(II) and less Fe aggregation relative to adsorbed WEOM-Fe. Spatially distinct lower- and higher-energy C regions were detected in both adsorbed and co-precipitated WEOM-Fe. In co-precipitates, lower-energy aromatic and/or substituted aromatic C was spatially associated with reduced Fe(II), but higher-energy oxidized C was enriched at the oxidized Fe(III) interface. Therefore, we show that co-precipitation does not constitute a non-specific physical encapsulation of C that only affects Fe chemistry and spatial distribution, but may cause a bi-directional set of reactions that lead to spatial separation and transformation of both Fe and C forms. In particular, we propose that abiotic redox reactions between Fe and C via substituted aromatic groups (e.g., hydroquinones) play a role in creating distinct co-precipitate composition, with potential implications for its mineralization.

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

Resolving local ordering and structure in Mn x Ge 1- x Te alloys through thermodynamic ensembles of pair distribution functions

Characterizing local bonding environments in complex materials is essential for understanding and optimizing their properties. Equally as important is the ability to predict local motifs as a function of synthesis conditions, enhancing chemists’ ability to design properties into materials. In this study, we present an approach to leverage statistical mechanics to generate temperature- and energy-informed ensemble averaged pair distribution functions (PDFs). This method, which we have named Thermodynamic Ensemble Averages of PDFs for Ordering and Transformations (TEAPOT), utilizes density functional theory (DFT) to relax supercells while incorporating energetic penalties for local order, enabling accurate and computationally efficient analysis of local structure. We apply this method to the neutron PDF measurements of the pseudobinary MnTe–GeTe (MGT) alloy, demonstrating its capability to resolve complex local distortions and chemical ordering. Our results reveal detailed insights into phase transformations and local distortions driven by Mn substitution. For compositions that globally present as rock salt, our analysis reveals that Ge coordination geometry is heavily impacted by synthesis temperature. We propose that high temperature synthesis conditions promote a lowered Ge polyhedra distortion, promoting high charge carrier mobility due to the alignment of local and global structure. Incorporating statistical mechanics and computation into experimental analysis thus guides synthesis of tailored local structure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Light-front puzzles

Abstract Light-front formulations of quantum field theories have many advantages for computing electroweak matrix elements of strongly interacting systems and other quantities that are used to study hadronic structure. The theory can be formulated in Hamiltonian form so non-perturbative calculations of the strongly interacting initial and final states are in principle reduced to linear algebra. These states are needed for calculating parton distribution functions and other types of distribution amplitudes that are used to understand the structure of hadrons. Light-front boosts are kinematic transformations so the strongly interacting states can be computed in any frame. This is useful for computing current matrix elements involving electroweak probes where the initial and final hadronic states are in different frames related by the momentum transferred by the probe. Finally in many calculations the vacuum is trivial so the calculations can be formulated in Fock space. The advantages of light front-field theory would not be interesting if the light-front formulation was not equivalent to the covariant or canonical formulations of quantum field theory. Many of the distinguishing properties of light-front quantum field theory are difficult to reconcile with canonical or covariant formulations of quantum field theory. This paper discusses the resolution of some of the apparent inconsistencies in canonical, covariant and light-front formulations of quantum field theory. The puzzles that will be discussed are (1) the problem of inequivalent representations (2) the problem of the trivial vacuum (3) the problem of ill-posed initial value problems (4) the problem of rotational covariance (5) the problem of zero modes and (6) the problem of spontaneously broken symmetries.

Physics↗