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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Normative Ranges for, and Interrater Reliability of, Rotational Vestibular and Balance Tests in U.S. Military Service Members and Veterans

Purpose: The objectives of this study were to (a) describe normative ranges—expressed as reference intervals (RIs)—for vestibular and balance function tests in a cohort of Service Members and Veterans (SMVs) and (b) to describe the interrater reliability of these tests. Method: As part of the Defense and Veterans Brain Injury Center (DVBIC)/Traumatic Brain Injury Center of Excellence 15-year Longitudinal Traumatic Brain Injury (TBI) Study, participants completed the following: vestibulo-ocular reflex suppression, visual-vestibular enhancement, subjective visual vertical, subjective visual horizontal, sinusoidal harmonic acceleration, the computerized rotational head impulse test (crHIT), and the sensory organization test. RIs were calculated using nonparametric methods and interrater reliability was assessed using intraclass correlation coefficients between three audiologists who independently reviewed and cleaned the data. Results: Reference populations for each outcome measure comprised 40 to 72 individuals, 19 to 61 years of age, who served either as noninjured controls (NIC) or injured controls (IC) in the 15-year study; none had a history of TBI or blast exposure. A subset of 15 SMVs from the NIC, IC, and TBI groups were included in the interrater reliability calculations. RIs are reported for 27 outcome measures from the seven rotational vestibular and balance tests. Interrater reliability was considered excellent for all tests except the crHIT, which was found to have good interrater reliability. Conclusion: This study provides clinicians and scientists with important information regarding normative ranges and interrater reliability for rotational vestibular and balance tests in SMVs.

Audiology & Speech-Language Pathology↗
DOE OSTI2023

Impact of globally spin-aligned vector mesons on the search for the chiral magnetic effect in heavy-ion collisions

In high-energy heavy-ion collisions, the chiral magnetic effect (CME) is predicted to arise from the interplay between the chirality imbalance of quarks in the nuclear medium and the intense magnetic field, and will cause a charge separation along the magnetic field direction. While the search for the CME is still ongoing in experiments at Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC), the CME-sensitive observables need to be scrutinized to exclude the non-CME contributions. In this work, we examine the influence of globally spin-aligned ρ mesons on the γ 112 correlator, the R Ψ 2 (ΔS) correlator, and the signed balance functions, via a toy model and a multiphase transport model (AMPT). The global spin alignment of vector mesons could originate from non-CME mechanisms in heavy-ion collisions, and is characterized by the 00-component of the spin density matrix, ρ 00 . Here, we find that the CME observables show similar dependence on ρ 00 , and could receive a positive (negative) contribution from ρ-decay pions, if the ρ 00 of ρ mesons is larger (smaller) than 1/3. Since pions are the most abundant particles in such collisions, the ρ 00 measurements for ρ mesons become crucial to the interpretation of the CME data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Influence of globally spin-aligned vector mesons to the measurements of the chiral magnetic effect in heavy-ion collisions

The chiral magnetic effect (CME) in high-energy heavy-ion collisions arises from the interplay between the chirality imbalance and the intense magnetic field and will cause a charge separation along the magnetic field direction. While the CME search is still ongoing in experiments, the non-CME contributions need to be excluded from the CME observables. In this work, we examine the influence of globally spin-aligned ρ mesons on the γ 112 correlator, the R Ψ 2 (ΔS) correlator, and the signed balance functions, via a toy model and a multiphase transport model (AMPT). We find that the CME observables are sensitive to the 00-component of the spin density matrix, ρ 00 : they receive positive (negative) contributions when ρ 00 is larger (smaller) than 1/3.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Investigation of experimental observables in search of the chiral magnetic effect in heavy-ion collisions in the STAR experiment *

The chiral magnetic effect (CME) is a novel transport phenomenon, arising from the interplay between quantum anomalies and strong magnetic fields in chiral systems. In high-energy nuclear collisions, the CME may survive the expansion of the quark-gluon plasma fireball and be detected in experiments. Over the past two decades, experimental searches for the CME have attracted extensive interest at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). The main goal of this study is to investigate three pertinent experimental approaches: the $γ$ correlator, the R correlator, and the signed balance functions. We exploit simple Monte Carlo simulations and a realistic event generator (EBE-AVFD) to verify the equivalence of the core components among these methods and to ascertain their sensitivities to the CME signal and the background contributions for the isobar collisions at the RHIC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Effect of vector meson spin coherence on the observables for the chiral magnetic effect in heavy-ion collisions

The chiral magnetic effect (CME) in heavy-ion collisions reflects the local violation of P and CP symmetries in strong interactions and manifests as electric charge separation along the direction of the magnetic field created by the wounded nuclei. The experimental observables for the CME, such as the γ 112 correlator, the R Ψ$_2$ ⁡ (Δ⁢S) correlator, and the signed balance functions, however, are also subject to non-CME backgrounds, including those from resonance decays. A previous study showed that the CME observables are affected by the diagonal component of the spin density matrix, the ρ 00 for vector mesons. Here, in this work, we study the contributions from the other elements of the spin density matrix using a toy model and a multiphase transport model. We find that the real part of the ρ 1-1 component, Re ⁡ρ 1-1 , affects the CME observables in a manner opposite to that of the ρ 00 . All three aforementioned CME observables show a linear dependence on Re ⁡ρ 1-1 in the model calculations, supporting our analytical derivations. The rest elements of the spin density matrix do not contribute to the CME observables. The off-diagonal terms in the spin density matrix indicate spin coherence and may be nonzero in heavy-ion collisions due to local spin polarization or spin-spin correlations. Thus, Re ⁡ρ 1-1 , along with ρ 00 , could play a significant role in interpreting measurements in search of the CME.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗
DOE OSTI2020

Structural determinants of protocadherin-15 mechanics and function in hearing and balance perception

The vertebrate inner ear, responsible for hearing and balance, is able to sense minute mechanical stimuli originating from an extraordinarily broad range of sound frequencies and intensities or from head movements. Integral to these processes is the tip-link protein complex, which conveys force to open the inner-ear transduction channels that mediate sensory perception. Protocadherin-15 and cadherin-23, two atypically large cadherins with 11 and 27 extracellular cadherin (EC) repeats, are involved in deafness and balance disorders and assemble as parallel homodimers that interact to form the tip link. Here we report the X-ray crystal structure of a protocadherin-15 + cadherin-23 heterotetrameric complex at 2.9-Å resolution, depicting a parallel homodimer of protocadherin-15 EC1-3 molecules forming an antiparallel complex with two cadherin-23 EC1-2 molecules. In addition, we report structures for 10 protocadherin-15 fragments used to build complete high-resolution models of the monomeric protocadherin-15 ectodomain. Molecular dynamics simulations and validated crystal contacts are used to propose models for the complete extracellular protocadherin-15 parallel homodimer and the tip-link bond. Steered molecular dynamics simulations of these models suggest conditions in which a structurally diverse and multimodal protocadherin-15 ectodomain can act as a stiff or soft gating spring. These results reveal the structural determinants of tip-link–mediated inner-ear sensory perception and elucidate protocadherin-15’s structural and adhesive properties relevant in disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the Stability Analysis of Astrophysical Cooling Functions

To model the temperature evolution of optically thin astrophysical environments at MHD scales, radiative and collisional cooling rates are typically either pretabulated or fit into a functional form and then input into MHD codes as a radiative loss function. Thermal balance requires estimates of the analogous heating rates, which are harder to calculate, and due to uncertainties in the underlying dissipative heating processes these rates are often simply parameterized. The resulting net cooling function defines an equilibrium curve that varies with density and temperature. Such cooling functions can make the gas prone to thermal instability (TI), which will cause departures from equilibrium. There has been no systematic study of thermally unstable parameter space for nonequilibrium states. Motivated by our recent finding that there is a related linear instability, catastrophic cooling instability, that can dominate over TI, here we carry out such a study. We show that Balbus instability criteria for TI can be used to define a critical cooling rate, Λ c , that permits a nonequilibrium analysis of cooling functions through the mapping of TI zones. We furthermore illustrate how thermal conduction modifies the shape of TI zones. Upon applying a Λ c -based stability analysis to coronal loop simulations, we find that loops undergoing periodic episodes of coronal rain formation are linearly unstable to catastrophic cooling instability, while TI is stabilized by thermal conduction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Photocatalytic Ammonia Synthesis using Fe-Based MOFs: The Role of Ligand Functionalization

Photocatalytic ammonia (NH 3 ) synthesis offers a carbon-neutral alternative to the Haber−Bosch process, which generates 42 million metric tons of CO 2 equivalent emissions annually. However, solar-to-ammonia conversion with contemporary photocatalysts remains far from practical requirements, and understanding the limiting factors in systems with well-defined active sites is crucial. Here, we show how the μ 3 -oxo-centered trinuclear Fe cluster in MIL-101(Fe) functions as the catalytic motif for N 2 -to-NH 3 conversion through combined experimental and computational investigations. Comparative studies with a molecular analogue demonstrate that the cluster is stabilized within the MOF framework, sustaining redox cycling and maintaining high catalytic activity. We systematically functionalized the dicarboxylate ligands of MIL-101(Fe) with −NH 2 , −Br, −NO 2 , −F, and −CF 3 to probe how ligand chemistry modulates Fe electron density, N 2 adsorption capacity, and proton availability, correlating these properties with catalytic performance using spectroscopic and surface characterization techniques alongside timeresolved infrared to assess excited-state lifetimes. F-functionalization optimally balances N 2 activation, proton availability at Fe active sites, and excited-state lifetimes, boosting NH 3 production by ∼ 60% relative to unmodified MIL-101(Fe). This study of ligandfunctionalized MIL-101(Fe) MOFs uncovers the underlying structure-activity relationships and advances design principles for solardriven NH 3 synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Collateral benefits: River flow normalization for endangered fish enabled riparian rejuvenation

Abstract Like most rivers worldwide, the transboundary North American Kootenay/i River has experienced multiple impacts including watershed developments, river channelization, and floodplain clearing, draining, and diking. Construction of Libby Dam was authorized by the 1964 Columbia River Treaty (CRT) between the United States and Canada, and in 1975 began regulating downstream flows for flood risk management and hydropower generation. Following cumulative impacts, the endemic Kootenai River White Sturgeon population collapsed and was designated as endangered in 1994 (U.S. Endangered Species Act). Subsequent Biological Opinions from the U.S. Fish and Wildlife Service prescribed Libby Dam operations to provide springtime flow pulses for sturgeon spawning. These provided the unanticipated benefit of substantial seedling recruitment of native and introduced riparian cottonwoods and willows. The regulated flow regime was further adaptively managed to provide a more normative (natural) regime, to balance ecological functions with flood risk management and hydropower generation. The broadened ecological considerations would be consistent with the proposed priorities for the modernization of the international CRT. The observed responses revealed that (1) diverse aquatic and riparian organisms are dependent on common river flow characteristics; (2) a normalized flow regime provided substantial ecological benefits; and (3) due to multiple influences, hybrid ecosystems develop along regulated rivers, with a blending of natural and altered processes and communities. For other regulated rivers, we recommend that (1) high springtime flows be allowed, as feasible; (2) followed by the gradual post‐peak recession; and (3) the maintenance of sufficient flows through the warm and dry interval of mid to late summer.

Rood, Stewart B.↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Promiscuous G-protein activation by the calcium-sensing receptor

The human calcium-sensing receptor (CaSR) detects fluctuations in the extracellular Ca 2+ concentration and maintains Ca 2+ homeostasis. It also mediates diverse cellular processes not associated with Ca 2+ balance. The functional pleiotropy of CaSR arises in part from its ability to signal through several G-protein subtypes. Here, we determined structures of CaSR in complex with G proteins from three different subfamilies: G q , G i and G s . We found that the homodimeric CaSR of each complex couples to a single G protein through a common mode. This involves the C-terminal helix of each Gα subunit binding to a shallow pocket that is formed in one CaSR subunit by all three intracellular loops (ICL1–ICL3), an extended transmembrane helix 3 and an ordered C-terminal region. G-protein binding expands the transmembrane dimer interface, which is further stabilized by phospholipid. The restraint imposed by the receptor dimer, in combination with ICL2, enables G-protein activation by facilitating conformational transition of Gα. We identified a single Gα residue that determines G q and G s versus G i selectivity. The length and flexibility of ICL2 allows CaSR to bind all three Gα subtypes, thereby conferring capacity for promiscuous G-protein coupling.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complete biosynthesis of QS-21 in engineered yeast

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host’s native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families—a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases—from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure–activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Trajectory Balance with Asynchrony

Finetune large language models (LLMs) quicker via parallelization. Specifically, this implements asynchronous reinforcement learning with a trajectory balance objective function

Bartoldson, BrianR [Lawrence Livermore National La↗
DOE OSTI2025

Bayesian Optimization for Reactor Design Optimization

This study present a test case in which the Bayesian Optimization method is applied to a simulation-based reactor core design optimization problem. The test case aims to showcase the potential of an automated design optimization algorithm for reactor designs by streamlining the reactor core design workflow, given the high computational cost of simulations. The contributions of this work are threefold. First, the existing HTGR model is converted into a simulation-based design optimization test case by developing a pipeline that enables modification of key design parameters and evaluates design performance based on simulation outputs. Second, Bayesian Optimization is implemented and adapted to demonstrate the feasibility of automatic design optimization for nuclear reactor core. Proposed approach leverages Gaussian Process models to characterize the relationship between design variables and performance metrics, while incorporating novel acquisition functions that balance exploration of the design space with exploitation of promising configurations. This implementation lays the foundation for the future developments of reactor design optimization algorithms.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Leveraging In-Network Computing and Programmable Switches for Streaming Analysis of Scientific Data

With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.

Sankaran, Ganesh↗