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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sulfonamidoboronic Acids as “Cross-Class” Inhibitors of an Expanded-Spectrum Class C Cephalosporinase, ADC-33, and a Class D Carbapenemase, OXA-24/40: Strategic Compound Design to Combat Resistance in Acinetobacter baumannii

Acinetobacter baumannii is a Gram-negative organism listed as an urgent threat pathogen by the World Health Organization (WHO). Carbapenem-resistant A. baumannii (CRAB), especially, present therapeutic challenges due to complex mechanisms of resistance to β-lactams. One of the most important mechanisms is the production of β-lactamase enzymes capable of hydrolyzing β-lactam antibiotics. Co-expression of multiple classes of β-lactamases is present in CRAB; therefore, the design and synthesis of “cross-class” inhibitors is an important strategy to preserve the efficacy of currently available antibiotics. To identify new, nonclassical β-lactamase inhibitors, we previously identified a sulfonamidomethaneboronic acid CR167 active against Acinetobacter-derived class C β-lactamases (ADC-7). The compound demonstrated affinity for ADC-7 with a K i = 160 nM and proved to be able to decrease MIC values of ceftazidime and cefotaxime in different bacterial strains. Herein, we describe the activity of CR167 against other β-lactamases in A. baumannii: the cefepime-hydrolysing class C extended-spectrum β-lactamase (ESAC) ADC-33 and the carbapenem-hydrolyzing OXA-24/40 (class D). These investigations demonstrate CR167 as a valuable cross-class (C and D) inhibitor, and the paper describes our attempts to further improve its activity. Five chiral analogues of CR167 were rationally designed and synthesized. The structures of OXA-24/40 and ADC-33 in complex with CR167 and select chiral analogues were obtained. The structure activity relationships (SARs) are highlighted, offering insights into the main determinants for cross-class C/D inhibitors and impetus for novel drug design.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Development and Demonstration of a Class 6 Range-Extended Electric Vehicle for Commercial Pickup and Delivery Operation: Preprint

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing representative workday drive cycles for Class 6 operation and an adapted cycle to enable vehicle track testing. A novel, automated driving system was utilized by Southwest Research Institute (SwRI) to improve the repeatability of the track testing conducted to quantify vehicle energy consumption. Cummins used a drivetrain model to design the hybrid control system for increased fuel savings. The control system functionality and fuel savings objective of 50% or more were confirmed by track testing described in this paper.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

algebraic multigrid↗
DOE OSTI2023

New structures of Class II Fructose-1,6-Bisphosphatase from Francisella tularensis provide a framework for a novel catalytic mechanism for the entire class

Class II Fructose-1,6-bisphosphatases (FBPaseII) (EC: 3.1.3.11) are highly conserved essential enzymes in the gluconeogenic pathway of microorganisms. Previous crystallographic studies of FBPasesII provided insights into various inactivated states of the enzyme in different species. Presented here is the first crystal structure of FBPaseII in an active state, solved for the enzyme from Francisella tularensis (FtFBPaseII), containing native metal cofactor Mn 2+ and complexed with catalytic product fructose-6-phosphate (F6P). Another crystal structure of the same enzyme complex is presented in the inactivated state due to the structural changes introduced by crystal packing. Analysis of the interatomic distances among the substrate, product, and divalent metal cations in the catalytic centers of the enzyme led to a revision of the catalytic mechanism suggested previously for class II FBPases. We propose that phosphate-1 is cleaved from the substrate fructose-1,6-bisphosphate (F1,6BP) by T89 in a proximal α-helix backbone (G88-T89-T90-I91-T92-S93-K94) in which the substrate transition state is stabilized by the positive dipole of the $\langle$-helix backbone. Once cleaved a water molecule found in the active site liberates the inorganic phosphate from T89 completing the catalytic mechanism. Additionally, a crystal structure of Mycobacterium tuberculosis FBPaseII (MtFBPaseII) containing a bound F1,6BP is presented to further support the substrate binding and novel catalytic mechanism suggested for this class of enzymes.

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

Spatial and Temporal Analysis of the Total Cost of Ownership for Class 8 Tractors and Class 4 Parcel Delivery Trucks

The medium- and heavy-duty transportation sector is experiencing rapid changes in powertrain technology innovation, with recent announcements of battery electric and fuel cell electric trucks being offered. The economics of these alternative powertrain vehicles are uncertain and difficult to compare directly. This analysis seeks to provide a rigorous, techno-economic analysis of all of these alternative powertrain vehicles within the same analytic framework. Specifically, this report evaluates the total cost of ownership (TCO) of six different truck powertrain technologies (diesel, diesel hybrid electric, plug-in hybrid electric, compressed natural gas, battery electric, and fuel cell electric) for three different truck vocations (Class 8 long haul [750-mile range and 500-mile range], Class 8 short haul [300-mile range], and Class 4 parcel delivery [120-mile range]), for three different time frames (2018, 2025, and Ultimate).

08 HYDROGEN↗
DOE OSTI2023

A New Class of Proton Conductors with Dramatically Enhanced Stability and High Conductivity for Reversible Solid Oxide Cells

Reversible solid oxide cells based on proton conductors (P-ReSOCs) have potential to be the most efficient and low-cost option for large-scale energy storage and power generation, holding promise as an enabler for the implementation of intermittent renewable energy technologies and the widespread utilization of hydrogen. Here, the rational design of a new class of hexavalent Mo/W-doped proton-conducting electrolytes with excellent durability while maintaining high conductivity is reported. Specifically, BaMo(W) 0.03 Ce 0.71 Yb 0.26 O 3-δ exhibits dramatically enhanced chemical stability against high concentrations of steam and carbon dioxide than the state-of-the-art electrolyte materials while retaining similar ionic conductivity. In addition, P-ReSOCs based on BaW 0.03 Ce 0.71 Yb 0.26 O 3-δ demonstrate high peak power densities of 1.54, 1.03, 0.72, and 0.48 W cm –2 at 650, 600, 550, and 500 °C, respectively, in the fuel cell mode. During steam electrolysis, a high current density of 2.28 A cm –2 is achieved at a cell voltage of 1.3 V at 600 °C, and the electrolysis cell can operate stably with no noticeable degradation when exposed to high humidity of 30% H 2 O at –0.5 A cm –2 and 600 °C for over 300 h. Altogether, this work demonstrates the promise of donor doping for obtaining proton conductors with both high conductivity and chemical stability for P-ReSOCs.

25 ENERGY STORAGE↗
DOE OSTI2021

Comparative evaluation of deep learning workloads for leadership-class systems

Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.

Yin, Junqi↗
DOE OSTI2020

Mercury Chalcogenide Nanoplatelet–Quantum Dot Heterostructures as a New Class of Continuously Tunable Bright Shortwave Infrared Emitters

Despite broad applications in imaging, energy conversion, and telecommunications, few nanoscale moieties emit light efficiently in the shortwave infrared (SWIR, 1000–2000 nm or 1.24–0.62 eV). We report quantum-confined mercury chalcogenide (HgX, where X = Se or Te) nanoplatelets (NPLs) can be induced to emit bright (QY > 30%) and tunable (900–1500+ nm) infrared emission from attached quantum dot (QD) “defect” states. Here, we demonstrate near unity energy transfer from NPL to these QDs, which completely quench NPL emission and emit with a high QY through the SWIR. This QD defect emission is kinetically tunable, enabling controlled midgap emission from NPLs. Spectrally resolved photoluminescence demonstrates energy-dependent lifetimes, with radiative rates 10–20 times faster than those of their PbX analogues in the same spectral window. Coupled with their high quantum yield, midgap emission HgX dots on HgX NPLs provide a potential platform for novel optoelectronics in the SWIR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

Abstract Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras, where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption, while the other classes of objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗