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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

FOCAL Campaign IV: Integrated System Control: Turbine + Hull

Campaign IV of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of a floating offshore wind turbine with turbine and hull controls. The turbine considered in this campaign is the scaled IEA 15MW Reference Wind Turbine similar to the setup considered in FOCAL Campaign I. This turbine is mounted on the VolturnUS platform with integrated hull controls similar to the hull considered in FOCAL Campaign II/III. This system is deployed for a fully-coupled wind and wave testing campaign at the University of Maine's Harold Alfond Wind and Wave (W2) facility. System dynamics and turbine characteristics are measured to determine global performance and assess the effect of the control strategies employed. NREL's reference open-source controller (ROSCO) is used for active blade pitch control and hull structural control is examined through the use of tuned mass dampers (TMD's) tuned to the systems pitch and tower-bending natural frequency. The naming convention of the load cases considered are described in the data details section. Detailed properties on the modeled system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaign IV: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Platform. UMaine ASCC Report Number 23-57-1183.

17 WIND ENERGY↗
DOE OSTI2022

Seeing behind black hole horizons in SYK

We present an explicit reconstruction of the interior of an AdS2 black hole in Jackiw-Teitelboim gravity, that is entirely formulated in the dual SYK model and makes no direct reference to the gravitational bulk. We do this by introducing a probe “observer” in the right wormhole exterior and using the prescription of [arXiv:2009.04476] to transport SYK operators along the probe’s infalling worldline and into the black hole interior, using an appropriate SYK modular Hamiltonian. Our SYK computation recovers the precise proper time at which signals sent from the left boundary are registered by our observer’s apparatus inside the wormhole. The success of the computation relies on the universal properties of SYK and we outline a promising avenue for extending it to higher dimensions and applying it to the computation of scattering amplitudes behind the horizon.

2D Gravity↗
DOE OSTI2022

Phase stability, mechanical properties, and ion irradiation effects in face-centered cubic CrFeMnNi compositionally complex solid-solution alloys at high temperatures

We report two CrFeMnNi face-centered cubic complex concentrated solid-solution alloys (CSA) have been evaluated for phase stability, mechanical properties, and radiation damage effects from heavy ions. Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe 35 Mn 15 Ni 35 were predicted by thermodynamic calculations to phase separate and maintain a single phase at 700 °C, respectively. Aging experiments at this temperature confirmed varying degrees of precipitation of a body-centered cubic phase in both Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe 35 Mn 15 Ni 35 . The alloys showed promising strength in tensile deformation at room temperature, with yield strengths of 155 MPa and 151 MPa for Cr 18 Fe 27 Mn 27 Ni 28 and Cr 15 Fe3 5 Mn 15 Ni 35 , respectively. At 500 °C, the yield strength of Cr 18 Fe 27 Mn 27 Ni 28 fell to 93 MPa, and to 100 MPa in Cr 15 Fe3 5 Mn 15 Ni 35 . Unlike Cr 18 Fe 27 Mn 27 Ni 28 , Cr 15 Fe 35 Mn 15 Ni 35 gained some ductility at 500 °C compared to room temperature. The two CSAs were irradiated to 75 dpa at 500 °C in the plateau region of the displacement curve using 3.7 MeV Ni 2+ ions, alongside model alloy 709 as a reference. Irradiation results produced similar densities and sizes of dislocations loops in the two CSAs compared to the reference. However, while large voids form in the plateau region of Cr 18 Fe 27 Mn 27 Ni 28 , small voids form just beyond the displacement peak of Cr 15 Fe 35 Mn 15 Ni 35 . Atom probe tomography and energy dispersive X-ray spectroscopy-equipped scanning transmission electron microscopes were used to characterize the alloys for changes in chemical distribution.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Impact of pairing and neutron-excess on suppression of the above-barrier fusion cross-section in 19 O + 12 C

Fusion excitation functions for 19 O and 19 F + 12 C were simultaneously measured along with 20 O + 12 C using the active-target detector MuSIC@Indiana. Examination of the cross-section just above the barrier reveals a significant suppression of fusion for the 19 O + 12 C system starting at E 12 MeV. This suppression is not simply due to the reduced binding of the unpaired neutron in 19 O as 17 O + 12 C does not exhibit the same sharp suppression of the fusion cross-section. Multiple mean-field models are used as a reference to understand the measured fusion excitation functions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Sensitivity of Carbonaceous Aerosol Properties to the Implementation of a Dynamic Aging Parameterization in the Regional Climate Model RegCM

Freshly emitted soot is hydrophobic, but condensation of secondary aerosols and coagulation with other particles modify its hygroscopic optical properties. This conversion is referred to as “aerosol aging.” Many climate models represent this aging process with a fixed aging time scale, whereas in reality, it is a dynamic process that depends on environmental conditions. Here, we implement a dynamic aging parameterization scheme in the regional climate model RegCM4 in place of the fixed aging timescale of 1.15 days (~27.6 h) and examine its impact on the aerosol life cycle over the Indian subcontinent. The conversion from hydrophobic to hydrophilic aerosol is usually lower than 27.6 h over the entire landmass and lower than 10 h over the polluted Indo-Gangetic Basin (IGB), with seasonal variability. Due to the implementation of the dynamic aging scheme, the column burden and surface mass concentration of carbonaceous aerosols increase during the drier season (December–February) when washout is negligible. The burden is reduced during the wet season (June–September) due to a more efficient washout except over the IGB, where a reduction in precipitation as a result of radiative feedbacks increases the aerosol concentrations. Over the polluted IGB, surface dimming increases due to the dynamic aging scheme, with the top of the atmosphere forcing remaining mostly unchanged. As a result, atmospheric heating increases by at least 1.2 W/m 2 . Our results suggest that climate models should incorporate dynamic aging for a more realistic representation of aerosol simulations, especially in highly polluted regions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Confronting Large‐Eddy Simulations With Stereo Camera Data by Means of Reconstructed Hemispheric Cloud Size Distributions

High-resolution hemispheric camera images at a meteorological site in western Germany are used to analyze the multi-dimensional spatial characteristics of continental cumulus cloud fields, and to evaluate Large-Eddy Simulations on this aspect. Traditional non-hemispheric cloud-detecting instruments provide additional reference data. The main model-observation comparison focuses on cloud size distributions (CSDs), employing two methods: (a) directly using three-dimensional model fields, direct CSDs, and (b) using rendered hemispheric images of the model fields as produced by a camera simulator based on path-tracing. In the latter method, both the real and rendered images are used to three-dimensionally reconstruct the cloud fields, yielding hemispheric CSDs. Advantages of hemispheric comparisons over more classic approaches include (a) fair comparisons between model and data, and (b) full use of the enhanced resolutions and hemispheric spatial coverage of the camera imagery. Basic evaluation of the simulations demonstrates good agreement on thermodynamic structure and its diurnal cycle. Cloud heights and cloud cover are intercompared between the model, camera data and other instrumentation, providing insight into their structural differences. A consistent alignment is found between the hemispheric CSDs from both the model and the cameras. Power law fits reveal structurally lower exponents in hemispheric CSDs compared to non-hemispheric CSDs, which particularly caution against directly comparing hemispheric CSDs to non-hemispheric distributions. This result is robust for sample size and fitting method. These findings inform future use of hemispheric camera systems for studying cumulus cloud field morphology and model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Urbanization drives convergence in soil profile texture and carbon content

Urban development has driven extensive modification of the global landscape. This shift in land use and land cover alters ecological functioning, and thereby affects sustainable management agendas. Urbanization fundamentally reshapes the soils that underlay landscapes, and throughout the soil profile, extends impacts of urbanization far below the landscape surface. The impacts of urbanization on deeper soils that are beyond the reach of regular land management are largely unknown, and validation of general theories of convergent ecosystem properties are thwarted by a dearth of both level of measurement effort and the substantial heterogeneity in soils and urban landscapes. Here, we examined two soil properties with strong links to ecological functioning—carbon and mineral-fraction particle size—measured in urban soils, and compared them to their pre-urbanization conditions across a continental gradient encompassing global soil diversity. We hypothesized that urbanization drove convergence of soils properties from heterogeneous pre-urban conditions towards homogeneous urban conditions. Based on our observations, we confirm the hypothesis. Both soil carbon and particle size converged toward an intermediate value in the full data distribution, from pre-urban to urban conditions. These outcomes in urban soils were observed to uniformly be fine textured soils with overall lower carbon content. Although these properties are desirable for supporting urban infrastructure (e.g. buildings, pipes), they constrain the potential to render ecosystem services. Since soil profile texture and carbon content were convergent and observed across 11 cities, we suggest that these property profiles can be used as a universal urban soil profile to: 1) provide a clear prediction for how urbanization will shift soil properties from pre-urban conditions, 2) facilitate the adoption of commonly-accepted soil profiles for process models, and 3) offer a reference point to test against urban management strategies and how they impact soil resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Binary pseudo-random array standard for extreme ultraviolet lithography tool characterization

Extreme ultraviolet (EUV) imaging tools play a crucial role in EUV lithography. Achieving high accuracy in EUV metrology is essential for advanced semiconductor manufacturing. A thorough characterization of the instrumentation in use is required. Binary pseudo-random arrays (BPRAs) are an established standard for calibrating and characterizing optical instruments in the frequency domain. Here, we expand the BPRA standard to applications in EUV imaging. To extend the technology to the EUV spectral range, a high-resolution BPRA target with the smallest feature size of 40 nm is developed. The EUV BPRA target establishes an in situ and portable calibration and alignment standard for EUV imaging. The target is patterned by means of electron-beam lithography, using a nickel absorber with a thickness of 39 nm. The substrate is a 4″ silicon wafer with a molybdenum/silicon multilayer coating. To demonstrate the efficacy of the target and develop the instrument calibration protocol, the target is imaged on the Sharp Hyper-NA Actinic Reticle Review Project EUV mask microscope. Power spectral density (PSD) data are presented. The characteristics of the imaging system are imprinted on the PSD. The modulation transfer function is extracted from the PSD data. A partially coherent imaging model is used as a reference to the experimental results.

BPRA↗
DOE OSTI2024

Hyperparameter Studies for Vision Transformers Trained on High-Fidelity Simulations

This library is a collection of python modules that define, train, and analyze vision-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The training data for these models is hydrodynamic simulation output in the form of numpy arrays. This library contains code to train these ViT models on the hydrodynamic simulation output with a variety of hyperparameters, and to compare the results of such models. Furthermore, the library contains definitions of simple convolutional neural network (CNN) machine learning architectures which can be trained on the same hydrodynamic simulation output. These are included as a reference point to compare the ViT models to. Additionally, the library includes trained ViT and CNN models and example input data for demonstration purposes. The code is based on the PyTorch python library.

Callis, Skylar↗
DOE OSTI2026

Linear viscoelastic measurements of commerical therapy putties for design and intuition

This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation. Additional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material. The collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.

C. Marsh, Maxwell↗
DOE OSTI2021

Testing in Support of the Development of EPP Plus SMT Design Method at ORNL (FY2021)

Experiments in support of the development of the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY21. This report focuses on the methodology for developing the EPP+SMT creep-fatigue (CF) design curves at low strain ranges. The creep damage-based method and dissipated work-based method were used to evaluate the available CF data at the low strain range region. A set of failure criteria were determined, and a simple extrapolation method was developed to predict the CF life cycles at low strain ranges that are not accessible by experiments due to the extraordinarily long failure times at the low strain region (thousands to hundreds of thousands of years) and the inability of the test machine to control these small strain ranges due to the signal to noise issues. An experimental method with the concept of block-strain range CF testing was proposed to generate the information needed to extrapolate the CF design curves to low strain ranges. Based on this new testing approach, a preliminary EPP+SMT CF design curve was developed for Alloy 617 at 950°C with tension hold time of 100 s. The analysis in this report shows the potential of generating a set of EPP+SMT CF design curves with different hold times within a reasonable amount of time and testing effort. Based on such a progress, a hold time extrapolation procedure for low strain ranges will be developed in FY22 and critical testing will be carried out to complete the development of the EPP+SMT CF design curves for Alloy 617.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Control of a Three-Phase Grid-Connected Voltage-Sourced Converter Using Long Short-Term Memory Networks

With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCCs) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for a GCC such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model

Semiconductor manufacturing is a highly complex. Fabrication plants must deal with re-entrant flows to support multiple types of wafers being produced simultaneously, each with their own deadlines and specifications. The manufacturing process itself depends upon the ability to control and programmatically adjust a variety of environmental conditions including temperature, humidity, and air quality. In addition, wafer fabrication consumes large amounts of electricity. Emerging technologies may help reduce the energy footprint of such facilities but can introduce cybersecurity risks. Therefore, this paper presents a modeling and simulation framework to quantify tradeoffs between operational measures of performance, energy consumption, and cybersecurity controls. We augment the Intel Minifab model, with the Purdue Enterprise Reference Architecture (PERA) for cybersecurity as well as tool-level energy consumption data from a real-world semiconductor manufacturing testbed. In this manner, we intend to provide stakeholders with a systematic, data-driven approach to evaluate emerging risks within the manufacturing process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Fast Charging of Li-Ion Cells: Part V. Design and Demonstration of Protocols to Avoid Li-Plating

Fast charging of Li-ion batteries would make “fueling” of electric vehicles comparable in time to fueling of gasoline-powered cars, increasing consumer appeal of the new technology. Taking the US Department of Energy goal of safe 6 C charging to 80% capacity as a guide, we describe approaches that can mitigate Li plating on the graphite anode. To make this possible, a variable-rate anode potential charging protocol has been implemented by using a microprobe reference electrode to continuously monitor and adjust the current, in this way avoiding low anode potentials that favor Li deposition. Various implementations of the anode potential control are considered using electrochemical modeling and compared with the experimental data. For charge to 80% capacity at 30 °C, an average C-rate of 4.97 C was obtained for an NCM523/graphite cell with 70 μ m thick graphite electrode and 7.40 C for a cell with 47 μ m thick graphite electrode. Our electrochemical model accounts for these observations and provides a means to extrapolate the approach to other cell designs and operation regimes, drawing the maximum average fast charging rates that can still avoid Li plating.

25 ENERGY STORAGE↗
DOE OSTI2020

Control of hybrid boosting in highly diluted internal combustion engines

A novel hybrid system enabling both flexible supercharging and limited torque assist is studied to mitigate transient response challenges of a turbocharged spark ignition engine equipped with low pressure exhaust gas recirculation. The hybrid system, called a power split supercharger (SC), is configured with a planetary gear set that splits the supercharging power between a small low-voltage electric motor and the engine crankshaft. The air path controller relies on the coordination of four actuators on the engine side and three low-level actuators on the hybrid boosting device. A decentralized control scheme is used in this work, in which the master–slave structure of the boost pressure controller decreases the turbo-lag associated with exhaust gas recirculation, while minimizing the SC operation for fuel economy. A vector reference governor is used to prevent compressor surge during engine tip-outs. In addition, the desired intake manifold pressure is modified to provide time for evacuating the exhaust gas recirculation and avoiding misfires when transitioning to low load, while the hybrid capability of the power split SC is used to recuperate the unwanted generated power. All controllers are validated on both a mean value engine model and a high-fidelity engine model. Practical challenges of implementing the vector reference governor to the highly nonlinear engine air path with pressure pulsations originating from the engine reciprocation are discussed and some solutions are proposed.

Engineering↗