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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Corrosion of rebar in concrete. Part IV. On the theoretical basis of the chloride threshold

A theory is developed for calculating the chloride threshold (CT) from the Point Defect Model (PDM). CT is defined as the ratio [Cl - ]/[OH - ] at which the passivity breakdown voltage (E b ) equals the corrosion potential, ECP. CT is sensitive to the T, ECP, pH, and E b , because all four appear in the exponent X in CT = 10 X . Small uncertainties in these parameters translate into a large uncertainty in CT, emphasizing the need for accurate T, ECP, pH, and E b data. We demonstrate the theory by successfully calculating CT using data for selected systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions

We present an implementation of all-electron density-functional theory for massively parallel GPU-based platforms, using localized atom-centered basis functions and real-space integration grids. Special attention is paid to domain decomposition of the problem on non-uniform grids, which enables compute- and memory-parallel execution across thousands of nodes for real-space operations, e.g. the update of the electron density, the integration of the real-space Hamiltonian matrix, and calculation of Pulay forces. To assess the performance of our GPU implementation, we performed benchmarks on three different architectures using a 103-material test set. We find that operations which rely on dense serial linear algebra show dramatic speedups from GPU acceleration: in particular, SCF iterations including force and stress calculations exhibit speedups ranging from 4.5 to 6.6. For the architectures and problem types investigated here, this translates to an expected overall speedup between 3–4 for the entire calculation (including non-GPU accelerated parts), for problems featuring several tens to hundreds of atoms. Additional calculations for a 375-atom Bi2Se3 bilayer show that the present GPU strategy scales for large-scale distributed-parallel simulations.

42 ENGINEERING↗
DOE OSTI2024

Conservative discontinuous Galerkin interpolation: Sheared boundary conditions

Local studies of accretion disks and laboratory magnetized plasmas employ analytical coordinate mappings that introduce sheared boundary conditions (BCs). Here, we present a discontinuous Galerkin (DG) algorithm to apply such BCs based on projections and quadrature-free integration. The procedure is high-order accurate, preserves moments exactly and works in multiple dimensions. Tests of increasing complexity are provided, beginning with translations of one and two dimensional fields, followed by 3D and 5D simulations with sheared (twist-shift) BCs. Results show that the algorithm is (p + 1)-order accurate in the DG representation and (p + 2) -order accurate in the cell averages, with p being the order of the polynomial basis. Quantification of the algorithm's hyperdiffusion and discussion of aliasing errors are given. This technique enables conservative local simulations of plasma turbulence with DG, not possible until now.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Photodissociation dynamics of xylene isomers C 6 H 4 (CH 3 ) 2 at 157nm using an ultracompact velocity map imaging spectrometer – The C 7 H 7 channel

Here, we investigated the photodissociation dynamics of three xylene isomers C 6 H 4 (CH 3 ) 2 at 157nm. The center-of-mass translational energy distributions of C 7 H 7 radicals were found to peak at 26kJmol −1 . Although the ionization energy of the C 7 H 7 tolyl fragment exceeds the energy of a 157nm photon, C 7 H 7 + was observed as a result of the photoionization of vibrationally ‘hot’ tolyl (C 7 H 7 ) radicals and/or two-photon ionization. The formation of rovibrationally excited tolyl fragments was discussed. Our experiments suggest the presence of tolyl radicals in the interstellar medium as a precursor to methylated polycyclic aromatic hydrocarbons upon reaction with vinylacetylene (C 4 H 4 ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Lethality is Local, but Survival is Systemic: Temporal and Multi-Organ Responses to Chlorine Gas Exposure in a Murine Model

Chlorine gas (Cl2) is a highly toxic chemical associated with both localized lung injury and systemic health effects. While pulmonary damage has been well characterized, the systemic inflammatory and metabolic responses remain poorly understood. We aimed to define the temporal and multi-organ responses to Cl2 exposure in a murine model, with a focus on identifying spatiotemporal inflammation and its impact on survival and lethality. SKH1 mice were exposed for 10 min to varying concentrations of Cl2 (94.4–810 ppm, representative of non-lethal, LD10, and LD50 doses) and monitored for respiratory function, perfusion, and acidosis using organ-specific imaging. At multiple time points (40 min, 6 h, 24 h, and 7 d), we measured phosphoproteins, cytokines, chemokines, growth factors, and metabolic hormones in the lungs, heart, cortex, and plasma. Statistical modeling and logistic regression were used to identify biomarkers associated with lethality and survival. We found that lung injury was the primary cause of potential lethality, particularly via early phosphoprotein signaling disruptions. However, survival correlated with early systemic coordination of inflammatory and metabolic signals across organs. Perfusion and acidosis imaging were strongly associated with chemokine and hormone responses. Key survival-associated plasma biomarkers included decreased insulin, increased ghrelin, and decreased eotaxin. While potential lethality from Cl2 exposure is locally driven by pulmonary injury, survival depends on systemic, multi-organ responses that occur rapidly post-exposure. Within this model, our findings identify a potential therapeutic window to enhance survival and suggest candidate biomarkers that may be explored translationally for both triage and treatment of chlorine-related incidents.

chlorine gas↗
DOE OSTI2021

Characterizing Long COVID: Deep Phenotype of a Complex Condition

Background: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. Methods: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. Funding: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Interpretation: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice

Background: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. Methods: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. Findings: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. Interpretation: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases

Background Large language models (LLMs) are increasingly used medicine for diverse applications including differential diagnostic support. The training data used to create LLMs such as the Generative Pretrained Transformer (GPT) predominantly consist of English-language texts, but LLMs could be used across the globe to support diagnostics if language barriers could be overcome. Initial pilot studies on the utility of LLMs for differential diagnosis in languages other than English have shown promise, but a large-scale assessment on the relative performance of these models in a variety of European and non-European languages on a comprehensive corpus of challenging rare-disease cases is lacking. Methods We created 4917 clinical vignettes using structured data captured with Human Phenotype Ontology (HPO) terms with the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema. These clinical vignettes span a total of 360 distinct genetic diseases with 2525 associated phenotypic features. We used translations of the Human Phenotype Ontology together with language-specific templates to generate prompts in English, Chinese, Czech, Dutch, French, German, Italian, Japanese, Spanish, and Turkish. We applied GPT-4o, version gpt-4o-2024-08-06, and the medically fine-tuned Meditron3-70B to the task of delivering a ranked differential diagnosis using a zero-shot prompt. An ontology-based approach with the Mondo disease ontology was used to map synonyms and to map disease subtypes to clinical diagnoses in order to automate evaluation of LLM responses. Findings For English, GPT-4o placed the correct diagnosis at the first rank 19.9% and within the top-3 ranks 27.0% of the time. In comparison, for the nine non-English languages tested here the correct diagnosis was placed at rank 1 between 16.9% and 20.6%, within top-3 between 25.4% and 28.6% of cases. The Meditron3 model placed the correct diagnosis within the first 3 ranks for 20.9% of cases in English and between 19.9% and 24.0% for the other nine languages. Interpretation The differential diagnostic performance of LLMs across a comprehensive corpus of rare-disease cases was largely consistent across the ten languages tested. This suggests that the utility of LLMs in clinical settings may extend to non-English clinical settings.

Artificial intelligence↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2025

Different chemical scaffolds bind to L-phe site in Mycobacterium tuberculosis Phe-tRNA synthetase

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mt), is one of the deadliest infectious diseases. The rise of multidrug-resistant strains represents a major public health threat, requiring new therapeutic options. Bacterial aminoacyl-tRNA synthetases (aaRS) have been shown to be highly promising drug targets, including for TB treatment. These enzymes play an essential role in translating the DNA gene code into protein sequence by attaching specific amino acid to their cognate tRNAs. They have multiple binding sites that can be targeted for inhibitor discovery: amino acid binding pocket, ATP binding pocket, tRNA binding site and an editing domain. Recently we reported several high-resolution structures of M. tuberculosis phenylalanyl-tRNA synthetase (MtPheRS) complexed with tRNA Phe and either L-Phe or a nonhydrolyzable phenylalanine adenylate analog. Here, in this study, using Nucleic Magnetic Resonance (NMR) and Surface Plasmon Resonance (SPR) we identified fragments that bind to MtPheRS and we determined crystal structures of their complexes with MtPheRS/tRNA Phe . All the binders interact with the L-Phe amino acid binding site. The analysis of interactions of the new compounds combined with adenylate analog structure provides insights for the rational design of antituberculosis drugs. The 3 ' arm of the tRNA Phe in all the structures was disordered with exception of one complex with D-735 compound. In this structure the 3' CCA end of the acceptor stem is observed in the editing domain of MtPheRS providing insights regarding the post-transfer editing activity of class II aaRS.

Gade, Priyanka [Univ. of Chicago, IL (United State↗
DOE OSTI2023

Unveiling the transport properties of protic ionic liquids: Lithium ion dynamics modulated by the anion fluorine reservoir

Protic ionic liquids (PILs) show great potential as electrolyte components for energy storage devices. A comprehensive understanding of their transport properties must be achieved to optimize the design of safer and efficient electrolytes. This study focuses on a series of PILs based on the DBUH + cation (protonated 1,8-diazabicyclo[5,4,0]–undec-7-ene superbase) and three anions derived from strong acids: TFO – (triflate), IM14 – (perfluorobutyl-trifluoromethylsulfonylimide) and TFSI – (bis(trifluoromethylsulfonyl)imide). Neat PILs and PILs doped with LiTFO, LiIM14, and LiTFSI were studied using temperature-dependent NMR diffusion and relaxation techniques. The ionicity of these systems was also evaluated. Results revealed that the dynamic behaviour of lithium ions, as well as ionicity, strongly depend on the structural features of the anions, particularly in the case of IM14 – , whose main feature is the uneven distribution of the fluorinated sidegroups. The 19 F relaxation rates in IM14 – provide insights into the rotational reorientation of that anion. DBUH-IM14 exhibited diffusion coefficients lower than the expected ones on the basis of its viscosity, likely due to fluorophilic intermolecular interactions involving the fluorinated terminal groups. The presence of Li + in the DBUH-IM14 electrolyte led to unexpected and relatively faster translational mobility of Li + ions, resulting in a higher lithium apparent transference number. However, the trends observed in ionicity indicate a more complex interplay between intermolecular interactions and ion correlations. While DBUH-TFSI showed minimal effect of Li + addition, DBUH-TFO and DBUH-IM14 exhibited a significant decrease in ionicity, possibly attributed to strong interactions between ions.

25 ENERGY STORAGE↗
DOE OSTI2024

Impact of reactor architecture and design parameters on the performance of microbial electrolysis cells revealed by the electrode potential slope analysis

Microbial electrolysis cells (MECs) are appealing for recovering the chemical energy contained in domestic and industrial liquid wastes as hydrogen gas. Despite several years of research in the field, there is still a lack of critical analysis of how the reactor architecture dictates the electrochemical performance of the cell. In this study, internal resistance and onset voltage from the electrode potential slope analysis (EPS) were used in combination with current density, hydrogen production rate, reactor packing density, electrode spacing, membrane type and composition from 23 different studies to identify the reactor design parameters that primarily govern electrochemical performance of MECs. Using anion exchange membranes resulted in smaller internal resistances (AEM R int = 41± 40 mΩ m 2 ) and larger current density (18 ± 14 A m −2 ) compared to single chamber reactors (SC R int = 68 ± 58 mΩ m 2 ; 22 ± 16 A m −2 ) or MECs with cation exchange membranes (CEM R int = 376 ± 280 mΩ m 2 ; 3.0 ± 2.1 A m −2 ). Higher electrochemical performance for AEM- and SC-MECs translated in larger hydrogen gas production rates (0.122 mL H 2 C −1 for AEM vs 0.117 mL H 2 C −1 for SC), but only when inhibitors against hydrogen scavengers were added in single chamber systems (0.080 mL H 2 C −1 for SC without inhibitors). Following membrane type and composition, maintaining a small electrode spacing was the most critical parameter to improve MEC performance, indicating that the low conductivity of the media primarily limit performance by increasing ohmic resistance. Here, reactor volume and electrode surface area negatively correlated with internal resistance and current density, indicating that better performance of scaled-up reactors can likely be obtained by stacking multiple smaller units rather than just increasing reactor size. Although challenges remain in the implementation of MECs for hydrogen production from liquid wastes, advances in electrochemical engineering of the reactors can facilitate scale up and performance prediction at scale.

Electrochemistry↗
DOE OSTI2026

Extremized nonlinear and linearized responses in soft metamaterials enabled by gradient-based design and grayscale digital light processing

In this study, we develop a gradient-based design approach that exploits grayscale digital light processing (DLP) 3D printing for extremizing the nonlinear and linearized response of soft metamaterials — materials that harness engineered geometric instabilities to undergo large and programmable changes in configuration. Grayscale DLP approaches modulate local mechanical properties at the pixel scale by tuning the light intensity within a single grayscale image, unlocking an exceptionally large design space. To effectively navigate this space, we develop smooth mappings between local light intensity values and global quantities of interest that characterize the behavior of soft metamaterials. Enabling these smooth mappings are robust and differentiable nonlinear finite element simulations powered by a trust region solver. A PDE-constrained optimization problem is then solved to invert these mappings and produce light intensity distributions that endow the printed part with varying stiffness and flexibility in distinctive regions. It is shown that optimizing the distribution of soft and stiff phases throughout a metamaterial structure results in markedly different buckling and self-contact configurations to drive extremized nonlinear compression and linearized vibration responses. Optimized light intensity distributions are translated to grayscale images and directly used to print soft metamaterial samples, showing remarkable agreement between the buckling and self-contact response in simulated and measured deformed configurations.

Additive manufacturing↗
DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A fast and accurate modeling approach for water and steam thermodynamics with practical applications in district heating system simulation

In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. Additionally, for an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1-1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. Here we compare battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗