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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Step-stool conformation of a cyclic Schiff base derived from ethylenediamine and heptane-2,4,6-trione: evidence for partial hydrolysis in metal coordination

A Schiff base derived from ethylenediamine and heptane-2,4,6-trione, namely, 5,9,14,18-tetramethyl-1,4,10,13-tetraazacyclooctadeca-5,8,14,17-tetraene-7,16- dione (C 18 H 28 N 4 O 2 ), abbreviated H 4 daaden, was prepared and characterized for the first time by single-crystal X-ray diffraction. The atoms of the Schiff base occupy two different planes and thus the molecule is essentially nonplanar. An axis running through the C–C atoms of the ethylenediamine groups separate the two planes and these two planes are connected by bridging ethylene groups showing an angle of 117.34 (8)°. As a result, the side view of the molecule shows a ‘step-stool’ conformation. The nonplanar nature of the Schiff base plays an important role in metal coordination, which leads to partial hydrolysis of the ring structure

crystal structure↗
DOE OSTI2023

Forced Power Systems Oscillations Due to Cyberattacks: Threats, Detection and Partial Mitigation

Forced oscillations in power systems can be caused by misconfigured controllers at generator stations. They can also be caused by cyberattacks against the exciters or governors. This paper explores the effects of forced oscillations from cyberattacks on generator excitation and governor systems and the effectiveness of a novel control system for a static var compensator in mitigating those oscillations to enhance transmission system resilience. A brief overview of oscillations, especially forced oscillations, within power systems is presented, along with an overview of cyberattacks on power systems. This paper also examines and implements FACTS devices to partially mitigate the forced oscillations created by cyberattacks by reducing the magnitude of the oscillations caused by the attack. The proposed approach is more effective against attacks targeting exciters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Solenoid With Partial Yoke for the Dune Near Detector

The Deep Underground Neutrino Experiment (DUNE) at Fermilab is one the most challenging next-generation experiments in the field of neutrino physics. It will feature two detectors for a detailed study of neutrino oscillations using an unprecedentedly intense neutrino beam. The two detectors are a Near Detector located on the Fermilab site, 574 m away from the neutrino generation, and a Far Detector in South Dakota, 1300 km away. The Near Detector consists of three subdetectors, based on different technologies in order to achieve the best understanding of the neutrino beam. One key element of the Near Detector is a High Pressure Argon TPC surrounded by a calorimeter. This detector will need a 0.5 Tesla magnetic field transverse to the neutrino beam direction, with a 7 m diameter, 8 m long warm bore. A thin superconducting solenoid with a partial yoke, needed in order to minimise the amount of material along the path particles take crossing the different elements of the Near Detector is proposed. In this paper we present a detailed magnetic analysis and a preliminary study of the cooling, the cable and the mechanics for this magnet.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A partial-volume correction for quantitative spectral x-ray radiography

The technique of spectral radiography has been shown to accurately discriminate a materials elemental composition by using known x-ray physics. This is achieved with a state-of-the-art pixelated spectrometer and a high-fidelity physical model. Similar to other radiographs, the system suffers from partial volume attenuation where the sample projection onto a pixel is only a fraction of the pixel area, leaving the rest unattenuated. This study updates the physical model to account for fractional area which presents new challenges to the existing algorithm. We present how this update is made and explore approaches to stabilize the algorithm through parameter scaling, constrained optimization, and hyperparameter selection.

Gillis, Wesley C.↗
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 OSTI2024

A Large Language Model for Determining Partial Tripping of Distributed Energy Resources

Knowing the status of individual distributed energy resources, i.e., being tripped or not, after a contingency can inform the development of an aggregated DER model. Here, this letter presents a large language model application to determine the partial tripping of distributed energy resources depending on the types, locations, and duration of faults in the transmission network. The large language model, or more specifically BERT-based approach can streamline the fault information into tokenized input, which not only reduces the complexity of the machine learning model but also demonstrates a robust performance with only limited data sets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However, low flammability and a propensity for high nitrogen oxide (NO x ) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NO x emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigated in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NO x concentrations below those of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing nonadiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and postflame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors' past work, this arrangement has demonstrated the significant sensitivity of exit NO x to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and nonpremixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NO x emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have been shown to create conditions that are conducive to increased N 2 O formation in the lean stage. In conclusion, the results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia.

Combustion↗
DOE OSTI2024

A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems

Abstract In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.

DeWitte, Lisa↗
DOE OSTI2024

Operando study of HfO 2 atomic layer deposition on partially hydroxylated Si(111)

The introduction of atomic layer deposition (ALD), to the microelectronics industry has introduced a large number of new possible materials able to be deposited in layers with atomic thickness control. One such material is the high-κ oxide HfO 2 ; thermally stable and ultrathin HfO 2 films deposited by ALD are a significant contender to replace SiO 2 as the gate oxide in capacitor applications. We present a mechanistic study of the first deposition cycle of HfO 2 on the Si(111) surface using tetrakis(dimethylamido) hafnium (TDMAHf) and water as precursors using operando ambient pressure x-ray photoelectron spectroscopy. Here, we show that the hydroxylation of the clean Si(111) surface by residual water vapor, resulting in a 0.3 monolayer coverage of hydroxyls, leads to instantaneous full surface coverage of TDMAHf. The change in the atomic ratio of Hf to C/N found during the first deposition half-cycle, however, does not match the assumed immediate ligand loss through reaction with surface hydroxyls. One would expect an immediate loss of ligands, indicated by a Hf:N ratio of approximately 1:3 as TDMAHf deposits onto the surface; however, a Hf:N ratio of 1:3.6 is observed. The partial hydroxylation on the Si(111) surface leads to binding through the TDMAHf ligand N atoms resulting in both N and CH 3 being found remaining on the surface post water half-cycle. Although there is evidence of ligand exchange reactions occurring at Si–OH sites, it also seems that N binding can occur on bare Si, highlighting the complexity of the substrate/precursor reaction even when hydroxyls are present. Moreover, the initial low coverage of Si–OH/Si–H appears to severely limit the amount of Hf deposited, which we hypothesize is due to the specific geometry of the initial arrangement of Si–OH/Si–H on the rest- and adatoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnitude faster compared to conventional PDE solvers, setting a previously unexplored paradigm for modeling and simulation of nonlinear and nonequilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Identifying a next-generation antimalarial trioxolane in a landscape of artemisinin partial resistance

For over two decades, artemisinin-based combination therapy (ACT) has been the standard of care for the treatment of uncomplicated falciparum malaria. However, artemisinin partial resistance (ART-R) is now prevalent in Southeast Asia and has emerged in eastern Africa, threatening ACT efficacy. Artefenomel, a synthetic 1,2,4-trioxolane, exhibits an extended pharmacokinetic exposure profile that predicts for efficacy against ART-R parasites. Unfortunately, the development of artefenomel was halted recently after almost a decade in the clinic. Here, we describe studies of an artefenomel-adjacent chemotype that combines potent in vitro activity against clinical ART-R parasites, an extended pharmacokinetic profile with single-exposure efficacy in a murine malaria model, and enhanced stability in human microsomes and hepatocytes. Overall, our studies reveal a heretofore underexplored trioxolane chemotype with the potential to address ART-R in a next-generation trioxolane development candidate.

Science & Technology - Other Topics↗
DOE OSTI2022

SARS-CoV-2 Diverges from Other Betacoronaviruses in Only Partially Activating the IRE1α/XBP1 Endoplasmic Reticulum Stress Pathway in Human Lung-Derived Cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed over 6 million individuals worldwide and continues to spread in countries where vaccines are not yet widely available or its citizens are hesitant to become vaccinated. Therefore, it is critical to unravel the molecular mechanisms that allow SARS-CoV-2 and other coronaviruses to infect and overtake the host machinery of human cells. Coronavirus replication triggers endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR), a key host cell pathway widely believed to be essential for viral replication. We examined the master UPR sensor IRE1α kinase/RNase and its downstream transcription factor effector XBP1s, which is processed through an IRE1α-mediated mRNA splicing event, in human lung-derived cells infected with betacoronaviruses. We found that human respiratory coronavirus OC43 (HCoV-OC43), Middle East respiratory syndrome coronavirus (MERS-CoV), and murine coronavirus (MHV) all induce ER stress and strongly trigger the kinase and RNase activities of IRE1α as well as XBP1 splicing. In contrast, SARS-CoV-2 only partially activates IRE1α through autophosphorylation, but its RNase activity fails to splice XBP1. Moreover, while IRE1α was dispensable for replication in human cells for all coronaviruses tested, it was required for maximal expression of genes associated with several key cellular functions, including the interferon signaling pathway, during SARS-CoV-2 infection. Our data suggest that SARS-CoV-2 actively inhibits the RNase of autophosphorylated IRE1α, perhaps as a strategy to eliminate detection by the host immune system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Metagenomes from 25 Low-Abundance Microbes in a Partial Nitritation Anammox Microbiome

Microbial communities using anammox bacteria to remove nitrogen are increasingly important in wastewater treatment. We report on 25 metagenome-assembled genomes of low-abundance microbes from a partial nitritation anammox bioreactor system that have not been described previously. These data add to the body of information about this important wastewater treatment system.

Beach, Natalie K.↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2025

Stability-preserving Lossy Compression for Large-scale Partial Differential Equations

Checkpoint/Restart (C/R) strategies are vital for fault tolerance in PDE-based scientific simulations, yet traditional checkpointing incurs significant I/O overhead. Lossy compression offers a scalable solution by reducing checkpoint data size, but conventional methods often lack control over physical invariants (e.g., energy), leading to instability such as oscillations or divergence in Partial Differential Equations (PDE) systems. This paper introduces a stability-preserving compression approach tailored for PDE simulations by explicitly controlling kinetic and potential energy perturbations to ensure stable restarts. Extensive experiments conducted across diverse PDE configurations demonstrate that our method maintains numerical stability with minimal error magnification—even across multiple checkpoint-restart cycles—outperforming state-of-the-art lossy compressors. Parallel evaluations on the Frontier supercomputer show up to 8.4× improvement in checkpoint write performance and 6.3× in read performance, while maintaining relative L2 errors ∼ 2e-6 throughout continued simulation. These results provide practical guidance for balancing compression accuracy, stability, and computational efficiency in large-scale PDE applications.

Gong, Qian [ORNL] (ORCID:0000000235704142)↗
DOE OSTI2022

Review–From LiMn 2 O 4 to Partially-Disordered Li 2 MnNiO 4 : The Evolution of Lithiated-Spinel Cathodes for Li-Ion Batteries

This personal narrative relates to the discovery in 1981 of stoichiometric LiM 2 O 4 spinel electrodes (where M = metal cation) for lithium-ion batteries by the Goodenough group at Oxford University, UK, and the subsequent identification and evolution of stable lithiated-spinel Li 2 M 2 O 4 (or LiMO 2 ) cathodes at the Council for Scientific and Industrial Research in South Africa and Argonne National Laboratory (USA). In this work, the competition that occurs between the formation of lithiated-spinel and layered (LiMO 2 ) structures at 400 °C, and recent efforts to design high-capacity, Mn-rich composite cathodes with partial disorder, are discussed. The paper is written in honor of John Goodenough's 100th birthday.

25 ENERGY STORAGE↗
DOE OSTI2024

Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries

Recently, deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a novel physics-informed neural operator method to solve parameterized boundary value problems without labeled data. By reformulating the PDEs into boundary integral equations (BIEs), we can train the operator network solely on the boundary of the domain. This approach reduces the number of required sample points from $O(N^d)$ to $O(N^{d-1}$), where $d$ is the domain’s dimension, leading to a significant acceleration of the training process. Additionally, our method can handle unbounded problems, which are unattainable for existing physics-informed neural networks (PINNs) and neural operators. Finally, our numerical experiments show the effectiveness of parameterized complex geometries and unbounded problems.

97 MATHEMATICS AND COMPUTING↗