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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Taxogenomic Analysis of a Novel Yeast Species, Lachancea rosae Sp. Nov. F.A., Isolated From the Wild Rose Rosa californica

A novel Saccharomycotina yeast strain, yHQL494, was isolated from the rose hip of the wild rose Rosa californica from Castle Crags State Park, California, USA. Phylogenetic analyses of both whole genome data and the sequences from the D1/D2 region of the large ribosomal subunit (LSU) rRNA gene placed strain yHQL494 within the genus Lachancea and grouped it into a clade with Lachancea lanzarotensis and Lachancea meyersii. Taxogenomic analyses were conducted on publicly available genome sequences to gain a deeper insight into the carbon and nitrogen gene-trait associations across the Lachancea clade. The results of these analyses were found to be consistent across Lachancea species. Growth assays and microscopic analyses were conducted to determine the physiological characteristics of strain yHQL494, including the presence of hyphae or pseudohyphae, ascospore formation, fermentation abilities, and assimilation of carbon and nitrogen compounds. Based on the phenotypic and genomic characteristics of the strain yHQL494 T (=NRRL Y-64858 T , =CBS 18,574 T ), we propose a new species, Lachancea rosae sp. nov. f.a.

Bajgain, Yakendra [Villanova Univ., PA (United Sta↗
DOE OSTI2023

Breaking the Ice: A Review of Phages in Polar Ecosystems

Bacteriophages, or phages, are viruses that infect and replicate within bacterial hosts, playing a significant role in regulating microbial populations and ecosystem dynamics. However, phages from extreme environments such as polar regions remain relatively understudied due to challenges such as restricted ecosystem access and low biomass. In this study, understanding the diversity, structure, and functions of polar phages is crucial for advancing our knowledge of the microbial ecology and biogeochemistry of these environments. In this review, we will explore the current state of knowledge on phages from the Arctic and Antarctic, focusing on insights gained from -omic studies, phage isolation, and virus-like particle abundance data. Metagenomic studies of polar environments have revealed a high diversity of phages with unique genetic characteristics, providing insights into their evolutionary and ecological roles. Phage isolation studies have identified novel phage–host interactions and contributed to the discovery of new phage species. Virus-like particle abundance and lysis rate data, on the other hand, have highlighted the importance of phages in regulating bacterial populations and nutrient cycling in polar environments. Overall, this review aims to provide a comprehensive overview of the current state of knowledge about polar phages, and by synthesizing these different sources of information, we can better understand the diversity, dynamics, and functions of polar phages in the context of ongoing climate change, which will help to predict how polar ecosystems and residing phages may respond to future environmental perturbations.

09 BIOMASS FUELS↗
DOE OSTI2022

Chapter 38: Aerodynamics of Wake Steering

This chapter discusses the mechanisms that enable wake steering within a wind farm with a focus on wake steering performed using yaw misaligned turbines as this is the most popular approach to wake steering, although there are others. Wake steering is a type of wind farm control in which wind turbines in a wind farm operate with an intentional yaw misalignment to mitigate the effects of its wake on downstream turbines in order to increase overall combined wind farm energy production. This chapter goes into detail regarding the dominant aerodynamic characteristics that are present when a turbine operates in yaw misaligned conditions and suggests analytical models that can capture these effects. A detailed analysis of large-scale flow structures generated in wind farm control through yaw misalignment is presented. A collection of counter-rotating vortices, produced from a misaligned turbine, deforms the shape of the wake and produces asymmetric effects with oppositely signed yaw angles. These vortices generated by an upstream misaligned turbine can also deflect wakes of downstream non-misaligned turbines. This chapter also addresses the importance of modeling these counter-rotating vortices in analytical models for wind farm control design and for accurately quantifying the impacts of wake steering on gains in power production in larger wind farms.

modeling↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2022

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exploring Gradient-Based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn linear directions or individual channels that control semantic attributes in the image space. However, they often suffer from imperfect disentanglement, or are unable to obtain multi-directional controls. Furthermore, in this work, in light of the above challenges, we propose a novel approach that discovers nonlinear controls, which enables multi-directional manipulation as well as effective disentanglement, based on gradient information in the learned GAN latent space. More specifically, we first learn interpolation directions by following the gradients from classification networks trained separately on the attributes, and then navigate the latent space by exclusively controlling channels activated for the target attribute in the learned directions. Empirically, with small training data, our approach is able to gain fine-grained controls over a diverse set of bi-directional and multi-directional attributes, and we showcase its ability to achieve disentanglement significantly better than state-of-the-art methods both qualitatively and quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Compatibility of Wrought Superalloys with Supercritical CO2

Supercritical CO2 (sCO2) power cycles, particularly direct-fired cycles, have the possibility of revolutionizing clean fossil energy with peak temperatures above 700 °C and wrought precipitation strengthened alloys like Haynes 282™ for structural components. At temperatures <650 °C, it would be desirable to use less expensive alloys, however, steels are known to be susceptible to carburization. Laboratory 300 bar sCO2 autoclave results were collected on a range of alloys including less expensive Ni-based alloys like 825 compared to advanced austenitic steels like alloy 709 at 600 °C. Both alloys 825 and 709 formed thin, protective Cr-rich oxides after 1,000 h. Alloy 825 also was exposed for 1,000 h in sCO2 at 800 °C and compared to a range of Ni-based alloys. Comparing alloys 625, 825, and 282, the mass gain increased with increasing alloy Ti content under these conditions. High Al superalloys did not perform significantly better under these conditions at 800 °C.

Pint, Bruce↗
DOE OSTI2023

Towards a Standard Process Management Infrastructure for Workflows Using Python

Orchestrating the execution of ensembles of processes lies at the core of scientific workflow engines on large scale parallel platforms. This is usually handled using platform-specific command line tools, with limited process management control and potential strain on system resources. The PMIx standard provides a uniform interface to system resources. The low level C implementation of PMIx has hampered its use in workflow engines, leading to the development of Python binding that has yet to gain traction. In this paper, we present our work to harden the PMIx Python client, demonstrating its usability using a prototype Python driver to orchestrate the execution of an ensemble of processes. We present experimental results using the prototype on the Summit supercomputer at Oak Ridge National Laboratory. This work lays the foundation for wider adoption of PMIx for workflow engines, and encourages wider support of more PMIx functionality in vendor provided system software stacks.

Elwasif, Wael↗
DOE OSTI2024

IRIS Reimagined: Advancements in Intelligent Runtime System for Task-Based Programming

Task-based programming models are gaining traction in scientific computing. IRIS is a portable runtime system that exploits multiple heterogeneous programming systems and can discover available resources and manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, and OpenMP) simultaneously. It accounts for the constraints of task dependencies and provides customizable scheduling policies to map those tasks to heterogeneous devices. In this paper, we present new capabilities added to IRIS to improve its portability for heterogeneous programming, build-friendliness, and performance efficiency. The new additions include vendor-specific kernel support, a runtime system with a foreign function interface to eliminate writing wrapper or boilerplate code for heterogeneous kernels, an easy-to-use and configurable CMake-based build environment, automatic and efficient data transfers and orchestration, and the Hunter and DAGGER toolchains to evaluate IRIS’s task scheduling algorithms.

Miniskar, Narasinga Rao↗
DOE OSTI2025

Role of Renewables in Grid Resilience

This chapter focuses on revealing the impact of renewable energies, especially distributed energy resources, on modern power system resilience. It is widely recognized that the increasing adoption of renewable energy is bringing more uncertainty and variability, posing new challenges to grid resilience. However, both supply and demand are becoming more active in grid resilience management. The communities themselves would benefit from the utilization of local resources and flexible management to gain resilience during the extreme events.

grid resilience↗
DOE OSTI2025

ReSpike: A Co-Design Framework for Evaluating SNNs on ReRAM-Based Neuromorphic Processors

With Moore’s law approaching its end, traditional von Neumann architectures are struggling to keep up with the exceeding performance and memory requirements of artificial intelligence and machine learning algorithms. Unconventional computing approaches such as neuromorphic computing that leverage spiking neural networks (SNNs) to perform computation are gaining traction and seek the paradigm shift necessary to sustain the increasing demands of modern applications. Novel memory technologies, such as resistive RAM (ReRAM), employ a crossbar architecture that possesses the inherent capability of efficiently computing vector-matrix multiplication—a dominant operation in SNNs. The prospect of naturally mapping SNNs to the crossbar structures provides a unique opportunity for achieving a high-performance, power-efficient neuromorphic system. In this work, we present ReSpike, which is a new framework, behavioral simulator, and architectural design based on ReRAM crossbar architectures, enabling modeling and co-design to achieve efficient execution of SNNs. We drive this co-design forward by quantifying the impact that ReRAM cell nonidealities have on the corresponding accuracy of an SNN application.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗
DOE OSTI2026

Profile Generation for GPU Targets

GPU accelerators are ubiquitous, but their ecosystem is far less evolved than the host one. Compiler heuristics are often tuned for CPUs and reused for GPU. Similarly, tooling and more evolved optimization techniques are historically not available on GPU targets. In this work, we address one of these shortcomings and enable profile generation and profile-guided optimizations (PGO) for GPU targets. While this is only a single step towards a CPU equivalent ecosystem for offload devices, it shows how old misconceptions on the limitations of GPUs are often not warranted anymore. Through our implementation in LLVM/Offload, we enable device-side PGO for full scientific applications and open up tooling opportunities, including code coverage analysis and compiler-built-in roofline analysis. Our evaluation highlights the performance implications of profile generation, the insights gained from these profiles, and the (missed) opportunities in utilizing the information for GPU compilation.

McDonough, Ethan Luis [Lawrence Livermore National↗
DOE OSTI2024

Measurements of long-range two-particle correlation over a wide pseudorapidity range in p–Pb collisions at $ \sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

Correlations in azimuthal angle extending over a long range in pseudorapidity between particles, usually called the “ridge” phenomenon, were discovered in heavy-ion collisions, and later found in pp and p–Pb collisions. In large systems, they are thought to arise from the expansion (collective flow) of the produced particles. Extending these measurements over a wider range in pseudorapidity and final-state particle multiplicity is important to understand better the origin of these long-range correlations in small collision systems. In this Letter, measurements of the long-range correlations in p–Pb collisions at $\sqrt{^SNN}$ = 5.02 TeV are extended to a pseudorapidity gap of Δη ~ 8 between particles using the ALICE forward multiplicity detectors. After suppressing non-flow correlations, e.g., from jet and resonance decays, the ridge structure is observed to persist up to a very large gap of Δη ~ 8 for the first time in p–Pb collisions. This shows that the collective flow-like correlations extend over an extensive pseudorapidity range also in small collision systems such as p–Pb collisions. The pseudorapidity dependence of the second-order anisotropic flow coefficient, v 2 (η), is extracted from the long-range correlations. The v 2 (η) results are presented for a wide pseudorapidity range of –3.1 < η < 4.8 in various centrality classes in p–Pb collisions. To gain a comprehensive understanding of the source of anisotropic flow in small collision systems, the v 2 (η) measurements are compared with hydrodynamic and transport model calculations. The comparison suggests that the final-state interactions play a dominant role in developing the anisotropic flow in small collision systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Long-lived vectors from electromagnetic cascades at SHiP

We simulate dark-vector, V, production from electromagnetic cascades at the recently approved SHiP experiment. The cascades (initiated by photons from π$^{0}$ → γγ) can lead to 3–4 orders of magnitude increase of the event rate relative to using primary production alone. We provide new SHiP sensitivity projections for dark photons and electrophilic gauge bosons, which are significantly improved compared to previous literature. The main gain in sensitivity occurs for long-lived dark vectors with masses below ~ 50 − 300 MeV. The dominant production mode in this parameter space is low-energy annihilation e$^{+}$e$^{−}$ → V (γ). This motivates a detailed study of backgrounds and efficiencies in the SHiP experiment for sub-GeV signals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Kinetic mixing, dark photons and extra dimensions. Part III. Brane localized dark matter

Extra dimensions have proven to be a very useful tool in constructing new physics models. In earlier work, we began investigating toy models for the 5-D analog of the kinetic mixing/vector portal scenario where the interactions of dark matter, taken to be, e.g., a complex scalar, with the brane-localized fields of the Standard Model (SM) are mediated by a massive U(1) D dark photon living in the bulk. These models were shown to have many novel features differentiating them from their 4-D analogs and which, in several cases, avoided some well-known 4-D model building constraints. However, these gains were obtained at the cost of the introduction of a fair amount of model complexity, e.g., dark matter Kaluza-Klein excitations. In the present paper, we consider an alternative setup wherein the dark matter and the dark Higgs, responsible for U(1) D breaking, are both localized to the ‘dark’ brane at the opposite end of the 5-D interval from where the SM fields are located with only the dark photon now being a 5-D field. The phenomenology of such a setup is explored for both flat and warped extra dimensions and compared to the previous more complex models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Polarized fragmenting jet functions in inclusive and exclusive jet production

Abstract In this work, we present a complete theoretical framework for analyzing the distribution of polarized hadrons within jets, with and without measuring the transverse momentum relative to the standard jet axis. Using soft-collinear effective theory (SCET), we derive the factorization and provide the theoretical calculation of both semi-inclusive and exclusive fragmenting jet functions (FJFs) under longitudinal and transverse polarization. With the polarized FJFs, one gains access to a variety of new observables that can be used for extracting both collinear and transverse momentum dependent parton distribution functions (PDFs) and fragmentation functions (FFs). As examples, we provide numerical results for the spin asymmetry$$ {A}_{TU,T}^{\cos \left({\phi}_S-{\hat{\phi}}_{S_h}\right)} $$ A TU , T cos ϕ S − ϕ ̂ S h from polarized semi-inclusive hadron-in-jet production in polarizedppcollisions at RHIC kinematics, where a transversely polarized quark would lead to the transverse spin of the final-state hadron inside the jet and is thus sensitive to the transversity fragmentation functions. Similarly, another spin asymmetry,$$ {A}_{TU,L}^{\cos \left({\phi}_q-{\phi}_S\right)} $$ A TU , L cos ϕ q − ϕ S from polarized exclusive hadron-in-jet production in polarizedepcollisions at EIC kinematics would allow us to access the helicity fragmentation functions. These observables demonstrate promising potential in investigating transverse momentum dependent PDFs and FFs and are worthwhile for further measurements.

Physics↗
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2026

Gaussian processes for inferring parton distributions

The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results. We study a framework based on Gaussian Process Regression (GPR) to reconstruct PDFs from lattice QCD matrix elements. Within a Bayesian framework, Gaussian processes serve as flexible priors that encode uncertainties, correlations, and constraints without imposing rigid functional forms. We investigate a wide range of kernel choices, mean functions, and hyperparameter treatments. We quantify information gained from the data using the Kullback-Leibler divergence. Synthetic data tests demonstrate the consistency and robustness of the method. Our study establishes GPR as a systematic and non-parametric approach to PDF reconstruction, offering controlled uncertainty estimates and reduced model bias in lattice QCD analyses.

hadronic spectroscopy↗