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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

eQuilibrator 3.0: a database solution for thermodynamic constant estimation

Abstract eQuilibrator (equilibrator.weizmann.ac.il) is a database of biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts around 1,000 distinct monthly users, its design could not accommodate larger compound databases and it lacked a scalable Application Programming Interface (API) for integration into other tools developed by the systems biology community. Here, we report on the recent updates to the database as well as the addition of a new Python-based interface to eQuilibrator that adds many new features such as a 100-fold larger compound database, the ability to add novel compounds, improvements in speed and memory use, and correction for Mg2+ ion concentrations. Moreover, the new interface can compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modelling. We foresee that these improvements will make thermodynamic modelling more accessible and facilitate the integration of eQuilibrator into other software platforms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DNA Sequence-Based Identification of Fusarium : A Work in Progress

Accurate species-level identification of an etiological agent is crucial for disease diagnosis and management because knowing the agent’s identity connects it with what is known about its host range, geographic distribution, and toxin production potential. This is particularly true in publishing peer-reviewed disease reports, where imprecise and/or incorrect identifications weaken the public knowledge base. This can be a daunting task for phytopathologists and other applied biologists that need to identify Fusarium in particular, because published and ongoing multilocus molecular systematic studies have highlighted several confounding issues. Paramount among these are: (i) this agriculturally and clinically important genus is currently estimated to comprise more than 400 phylogenetically distinct species (i.e., phylospecies), with more than 80% of these discovered within the past 25 years; (ii) approximately one-third of the phylospecies have not been formally described; (iii) morphology alone is inadequate to distinguish most of these species from one another; and (iv) the current rapid discovery of novel fusaria from pathogen surveys and accompanying impact on the taxonomic landscape is expected to continue well into the foreseeable future. To address the critical need for accurate pathogen identification, our research groups are focused on populating two web-accessible databases (FUSARIUM-ID v.3.0 and the nonredundant National Center for Biotechnology Information nucleotide collection that includes GenBank) with portions of three phylogenetically informative genes (i.e., TEF1, RPB1, and RPB2) that resolve at or near the species level in every Fusarium species. The objectives of this Special Report, and its companion in this issue ( Torres-Cruz et al. 2022 ), are to provide a progress report on our efforts to populate these databases and to outline a set of best practices for DNA sequence-based identification of fusaria.

Plant Sciences↗
DOE OSTI2025

Antihydrogen chemistry

A survey of antimatter reactions is presented, including the formation of the antihydrogen atom and anionic, cationic, and molecular species by collisional and radiative processes. Our approach is rooted in the detailed knowledge available for many matter counterpart (hydrogenic) reactions, due to their importance in controlling early Universe chemistry. We point out that the availability of trapped antihydrogen at densities similar to those pertaining to the epoch of hydrogen chemistry will soon be available. In addition, using modern atomic physics techniques, it should be feasible to control antimatter in the laboratory to facilitate antihydrogen chemistry. Our purpose is to summarize what is known from hydrogen chemistry that is of relevance for antimatter and to indicate, based on possible reaction rates, which processes may be fruitful to pursue to create new antimatter entities as probes of fundamental symmetries. We include antihydrogen, positrons, and antiprotons in our discussion and additionally the electron due to its propensity to form positronium and perhaps to participate in certain reactions. We attempt to indicate whether further theoretical/computational work is necessary to add to the assessment of reaction rates, and we discount processes where the projected rates are too low to be of interest, given foreseeable experimental capabilities.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Ultrahigh-energy tau neutrino cross sections with GRAND and POEMMA

Next-generation neutrino experiments will push the limits in our understanding of astroparticle physics in the neutrino sector to energies orders of magnitude higher than the current state-of-the-art high-energy neutrino experiment, IceCube. These experiments will use neutrinos to tell us about the most extreme environments in the Universe while simultaneously leveraging these extreme environments as probes of neutrino properties at the highest energies accessible in the foreseeable future: $E \sim 10^9$ GeV. At these energies neutrinos are readily absorbed in Earth. Assuming an isotropic distribution, by looking at how the flux varies as a function of the angle through Earth, we show here that it is possible to extract the $ν_τ$-N cross section with precision at the ~20% level (1σ assuming Wilks' theorem) given $N_{events} \sim 100$ events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2021

Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1%) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this work, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers — bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77% faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.

Li, Ang↗
DOE OSTI2025

Assessing the Role of Hydrodynamics in Enhancing Height-Above-the-Nearest-Drainage Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan

The conventional approach to generating synthetic rating curves (SRC) using the Height-Above-the-Nearest-Drainage (HAND) method typically relies on the assumption of uniform flow, such as Manning's equation, to establish stage-discharge ratings. The zero-physics application of the uniform flow equation is insufficient for capturing detailed hydraulic features (e.g., backwater effect) and neglects the hydraulic effects from adjacent channels. This lack of hydrodynamic computation can impact the accuracy and effectiveness of riverine flood risk estimation and management. To reduce this foreseeable error, we introduce the HAND-hd workflow, which integrates sophisticated hydrodynamic computations in the production of HAND-based SRC with hydrodynamic features (SRC hd ). The results indicate that SRC hd demonstrates consistent agreement with both gauge observations and benchmark solutions. Additionally, the comparative analysis suggests that SRC hd provides notable improvements in stage-discharge ratings over conventional HAND-based SRCs, particularly in channels with mild bed gradients, where it reduces water stage prediction errors and percent biases. In steeper channel segments, SRC hd maintains comparable accuracy to conventional methods. The comprehensive evaluation in this study emphasizes the potential discrepancies and inaccuracies associated with the adoption of the uniform flow assumption in the conventional HAND-SRCs and addresses the necessity of including hydrodynamic physics in the application of HAND-based SRC (e.g., inundation map) in channels with mild gradients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings: Preprint

Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand-side management (DSM) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs. It is important to account for occupant preferences in HEMS to ensure occupant satisfaction while participating in DSM programs. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foreseeTM HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful for controlling the electrical grid and developing HEMS solutions.

carbon footprint↗
DOE OSTI2021

Flipped SU(5) GUT phenomenology: proton decay and $\mathbf {g_\mu - 2}$

We consider proton decay and $\mathbf {g_\mu - 2}$ in flipped SU(5) GUT models. We first study scenarios in which the soft supersymmetry-breaking parameters are constrained to be universal at some high scale M in above the standard GUT scale where the QCD and electroweak SU(2) couplings unify. In this case the proton lifetime is typically > rsim10 36 years, too long to be detected in the foreseeable future, and the supersymmetric contribution to $\mathbf {g_\mu - 2}$ is too small to contribute significantly to resolving the discrepancy between the experimental measurement and data-driven calculations within the Standard Model. However, we identify a region of the constrained flipped SU(5) parameter space with large couplings between the 10- and 5-dimensional GUT Higgs representations where p → e + π 0 decay may be detectable in the Hyper-Kamiokande experiment now under construction, though the contribution to $\mathbf {g_\mu - 2}$ is still small. A substantial contribution to $\mathbf {g_\mu - 2}$ is possible, however, if the universality constraints on the soft supersymmetry-breaking masses are relaxed. We find a ‘quadrifecta’ region where observable proton decay co-exists with a (partial) supersymmetric resolution of the $\mathbf {g_\mu - 2}$ discrepancy and acceptable values of m h and the relic LSP density.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Electrochemical Imaging of Interfaces in Energy Storage via Scanning Probe Methods: Techniques, Applications, and Prospects

Developing a deeper understanding of dynamic chemical, electronic, and morphological changes at interfaces is key to solving practical issues in electrochemical energy storage systems (EESSs). To unravel this complexity, an assortment of tools with distinct capabilities and spatiotemporal resolutions have been used to creatively visualize interfacial processes as they occur. This review highlights how electrochemical scanning probe techniques (ESPTs) such as electrochemical atomic force microscopy, scanning electrochemical microscopy, scanning ion conductance microscopy, and scanning electrochemical cell microscopy are uniquely positioned to address these challenges in EESSs. We describe the operating principles of ESPTs, focusing on the inspection of interfacial structure and chemical processes involved in Li-ion batteries and beyond. We discuss current examples, performance limitations, and complementary ESPTs. Finally, we discuss prospects for imaging improvements and deep learning for automation. We foresee that ESPTs will play an enabling role in advancing EESSs as we transition to renewable energies.

25 ENERGY STORAGE↗
DOE OSTI2021

Investigation of the effects of turbulence modeling on the prediction of compression-ignition combustion unsteadiness

Adverse effects of global warming due to the greenhouse gas emissions is changing the actual paradigm for the use energy resources. In the absence of a mid-term solution for reducing these emissions in transportation, internal combustion (IC) engines are going to coexist in the social spheres in the foreseeable future. Therefore, the study of other IC engine-related problems remains relevant to ensuring the health of the society. In this investigation, a numerical methodology for comprehensive understanding of Noise, Vibration and Harshness in internal combustion engines is proposed. Due to its inherent complexity and lack of awareness, the main objective is to evaluate the impact of the turbulence modeling framework on the in-cylinder acoustic field recreation. Modal decomposition methods have been applied to isolate the coherent flow structures and to analyze how they change with the turbulence approach. Results demonstrate that the choice of the turbulence model is a critical aspect for noise modeling. Unsteady Reynolds-Averaged Navier-Stokes schemes predict a raw estimation of the internal acoustic field with the added value of being computationally less expensive. However, the use of more complex turbulence approaches such us large eddy simulation offers an accurate prediction of the acoustic structures and their cyclic dispersion.

Broatch, A.↗
DOE OSTI2024

Practical low-temperature gasoline combustion for very high efficiency off-road, medium- and heavy-duty engines

Low-temperature gasoline combustion (LTGC) with additive-mixing fuel injection (AMFI) is a new combustion strategy that has been demonstrated to deliver 9%–25% better brake thermal efficiency than similar-sized market-leading diesel engines over the operating map. Moreover, the LTGC-AMFI engine shows near-zero smoke, and NOx emissions are 4–100 times lower than those of a diesel, sufficiently low that no aftertreatment, or only passive NOx aftertreatment, would be sufficient (diesel exhaust fluid is not required). LTGC-AMFI combustion is based on kinetically controlled compression ignition of a dilute charge with a variable amount of low-to-moderate fuel stratification. Fast combustion control is provided by adding minute amounts of an ignition-enhancing additive into the fuel each engine cycle to control its reactivity. This strategy was used to operate a medium-duty (MD) LTGC-AMFI engine at loads from idle to 16.3 bar BMEP and speeds from 600 to 2400 rpm with regular E10 gasoline, which covers nearly the entire operating map of a typical MD engine. Turbine-out temperatures were sufficient for an oxidation catalyst to control hydrocarbon and CO emissions. Autonomie simulations over the GEM ARB Transient and the GEM 55 mph Cruise driving cycles for class-6 trucks using this technology showed fuel economies of 8.1 and 11.4 mpg-gasoline-equivalent, respectively, corresponding to 18.6% and 13.4% improvements over a similar-size diesel engine. Engine-out NOx emissions were 0.024 and 0.01 g/bhp-h, respectively, well below current U.S. emission standards. These results show that switching from diesel to LTGC-AMFI engines would greatly reduce greenhouse gas (GHG) emissions for off-road, MD and HD applications, which will continue to rely on combustion engines because electrification is not practical in the foreseeable future. Finally, with their reduced fuel consumption, the lower cost of gasoline compared to diesel fuel, and much lower aftertreatment costs, LTGC-AMFI engines also offer a significantly lower total cost of ownership.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. In conclusion, we study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant.

classical optimizers↗
DOE OSTI2023

Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates

The COVID-19 pandemic represents a global challenge that has impacted and is expected to continue to impact the lives and health of people across the world for the foreseeable future. The rollout of vaccines has provided highly anticipated relief, but effective therapeutics are required to further reduce the risk and severity of infections. Monoclonal antibodies have been shown to be effective as therapeutics for SARS-CoV-2, but as new variants of concern (VoC) continue to emerge, their utility and use have waned due to limited or no efficacy against these variants. Furthermore, cumbersome systemic administration limits easy and broad access to such drugs. As well, concentrations of systemically administered antibodies in the mucosal epithelium, a primary site of initial infection, are dependent on neonatal Fc receptor mediated transport and require high drug concentrations. To reduce the viral load more effectively in the lung, we developed an inhalable formulation of a SARS-CoV-2 neutralizing antibody binding to a conserved epitope on the Spike protein, ensuring pan-neutralizing properties. Administration of this antibody via a vibrating mesh nebulization device retained antibody integrity and resulted in effective distribution of the antibody in the upper and lower respiratory tract of non-human primates (NHP). In comparison with intravenous administration, significantly higher antibody concentrations can be obtained in the lung, resulting in highly effective reduction in viral load post SARS-CoV-2 challenge. This approach may reduce the barriers of access and uptake of antibody therapeutics in real-world clinical settings and provide a more effective blueprint for targeting existing and potentially emerging respiratory tract viruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 4.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 1.0 in 2017, followed by 2.0 in 2018, and 3.0 in 2021. This new release of FRED 4.0 now has 213,941 observations of 238 root traits, for a combined total of roughly 3.4 million data fields for root traits and ancillary data together. FRED 4.0 has 39.8% more root trait observations than FRED 3.0 and a 34.4% increase in unique data sources. This release of FRED 4.0 also includes significant increases in geographic regions that have long been underrepresented in global datasets, notably in the tropical low latitudes. Ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently with root trait observations. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. Despite this fine-root focus, FRED accepts data collected from roots of all sizes and contains observations of many root classes including coarse roots. Data collection will continue for the foreseeable future. The FRED4_Entire_Database_2026.csv file is the flat csv data file for FRED 4.0, and the FRED4_dd.csv file is the data dictionary of all columns available in FRED, including column IDs, column names, definitions, and unit (where applicable).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

General Approach to Physics Limits of Ultimate Colliders

The future of the particle physics is critically dependent on feasibility of future energy frontier colliders. The concept of the feasibility is complex and includes at least three factors: feasibility of energy, feasibility of luminosity, and feasibility of cost and construction time. Here we discuss major beam physics limits of ultimate accelerators, take a look into ultimate energy reach of possible future colliders. We also foresee a looming paradigm change for the HEP research as the thrust for higher energies by necessity will mean lower luminosity.

Shiltsev, Vladimir D.↗
DOE OSTI2022

Magnetron R&D Progress for High Efficiency CW RF Sources of Industrial Accelerators

After the demonstration of using high efficiency magnetron power to combine and aim to drive a radio frequency accelerator at 2450MHz in CW mode [1], we have used trim coils adding to a water-cooled magnetron and three amplitude modulation methods in an open-loop control to further suppress the 120Hz side-band noise to -46.7dBc level. We have also successfully demonstrated the phase-locking to an industrial grade cooking magnetron transmitter at 915MHz with a 75kW CW power delivered to a water load by using a -26.6dBc injection signal. The sideband noise at 360Hz from the 3-Phase SCRs DC power supply can be reduced to -16.2dBc level. Their power combing scheme and higher power application to industrial accelerators are foreseeing.

Wang, H.↗