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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NSTX-U research advancing the physics of spherical tokamaks

Abstract The objectives of NSTX-U research are to reinforce the advantages of STs while addressing the challenges. To extend confinement physics of low- A , high beta plasmas to lower collisionality levels, understanding of the transport mechanisms that set confinement performance and pedestal profiles is being advanced through gyrokinetic simulations, reduced model development, and comparison to NSTX experiment, as well as improved simulation of RF heating. To develop stable non-inductive scenarios needed for steady-state operation, various performance-limiting modes of instability were studied, including MHD, tearing modes, and energetic particle instabilities. Predictive tools were developed, covering disruptions, runaway electrons, equilibrium reconstruction, and control tools. To develop power and particle handling techniques to optimize plasma exhaust in high performance scenarios, innovative lithium-based solutions are being developed to handle the very high heat flux levels that the increased heating power and compact geometry of NSTX-U will produce, and will be seen in future STs. Predictive capabilities accounting for plasma phenomena, like edge harmonic oscillations, ELMs, and blobs, are being tested and improved. In these ways, NSTX-U researchers are advancing the physics understanding of ST plasmas to maximize the benefit that will be gained from further NSTX-U experiments and to increase confidence in projections to future devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Divertor heat load estimates on NSTX and DIII-D using new and open-source 2D inversion analysis code

A thermography inversion algorithm has been developed in the open-source Python-based computer code, HYPERION, to calculate the heat flux incident on plasma-facing components (PFCs) in axisymmetric tokamaks. The chosen mesh size at the surface significantly affects the calculated transient heat flux results. The calculated transient heat flux will exceed the real value when the mesh size tends to zero but will underestimate the real value when the mesh size is large. A criterion for determining the appropriate mesh size for the transient heat flux calculation will be discussed. The numerical scheme for HYPERION uses a 2D fully implicit finite-difference approach, allowing temperature-dependent thermal properties of PFC materials. The inversion algorithm is benchmarked against established heat flux calculation codes, TACO and THEODOR, based on thermography data from NSTX and DIII-D respectively. The primary benefits of HYPERION compared to TACO and THEODOR are that it is open-source and it allows for the optimization of mesh thickness along the substrate. The algorithm also accounts for the thermal properties of thin surface layers that characteristically form on PFCs due to plasma-material interactions. The agreement between HYPERION and THEODOR is excellent, as the percent difference between the codes is ~5% on average in the case of the DIII-D data for moderate to high heat flux. Verification tests with TACO show slightly higher average percent differences of 8% and 12%. In using HYPERION to study filaments in heat flux, the initial results indicate that small ELMs filaments significantly broaden the divertor heat flux, and decrease divertor peak flux. Compared to the inter-ELM, the small ELM filaments decrease the divertor peak surface temperature. With intermittent divertor filaments, the divertor heat flux width is comparable with that found in L-mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Observation of stationary filaments with resonant magnetic perturbations in NSTX

The application of resonant magnetic perturbations (RMPs) induces striated heat fluxes on the divertor plate in tokamaks. These striated heat fluxes were previously considered strike point splitting by the intersection between the divertor and predicted magnetic lobe structures. Recently, the existence of stationary 3D filaments has been observed for the first time in National Spherical Torus Experiment (NSTX) plasmas with toroidal mode number n = 3 RMPs. Magnetic field line tracing of these filaments from the midplane to the divertor is consistent with the quantity and the radial and toroidal location of striated heat fluxes observed on the divertor. This study suggests that divertor striated heat flux patterns are linked to these stationary filaments rather than to strike point splitting or magnetic lobe structures for NSTX. This finding will help guide future efforts to understand the heat flux striations in NSTX with RMPs in order to control and reduce the divertor peak heat flux.

RMPs↗
DOE OSTI2026

Spherical tokamak physics research in preparation for the operation of NSTX-U

The National Spherical Torus Experiment Upgrade (NSTX-U) is preparing to resume operation, representing a crucial step toward realizing compact, cost-effective fusion pilot plants. In advance of this, extensive modeling and data analysis have been conducted to advance the physics basis for low-aspect-ratio, high-performance plasma regimes, focusing on three core objectives: confinement and stability, power and particle handling, and steady-state operation. Significant progress has been made in understanding the electron temperature flattening in high-β plasmas, which is shown to be driven by a complex interplay of magnetohydrodynamic instabilities (e.g. non-resonant infernal modes), fast-ion-driven Alfvén eigenmodes, and electron and ion-scale micro-instabilities, particularly Kinetic Ballooning Modes (KBMs), whose destabilization is strongly dependent on parallel magnetic field fluctuations (δB ∥ ). Furthermore, a new gyrokinetic critical pedestal model was developed, accurately predicting pedestal structure by identifying KBMs as the primary stability limit, offering a critical constraint for future high-confinement scenarios. To address the challenge of high heat flux, novel liquid lithium plasma-facing components were modeled. The analysis confirmed that lithium vapor shielding is a self-regulating mechanism for heat mitigation, while also emphasizing that strong main ion parallel flow is essential to minimize core lithium contamination. Finally, progress toward steady-state operation was anchored by developing the required physics basis and control tools. This includes predictive modeling for reversed magnetic shear sustainment, demonstrating that magnetic island-induced bootstrap current reduction is negligible in STs, and advancing real-time control and disruption avoidance capabilities. The development of high-speed surrogate models (e.g. MMMNet) provides computationally efficient tools vital for non-inductive scenario optimization and integrated, low-disruptivity operations planned for NSTX-U.

NSTX-U↗
DOE OSTI2026

Impact of toroidal magnetic field direction on integrated ELM-stable operation and divertor power exhaust via boron powder injection in EAST

We report the first in-depth comparison of the impact of toroidal magnetic field direction on solid boron injection used for Edge-Localized Mode (ELM) control, power exhaust, and core high-Z impurity control in the Experimental Advanced Superconducting Tokamak. With favorable ion ∇B drift towards the upper X-point in an upper-single-null configuration, boron injection effectively suppresses ELMs, produces a detachment of the inner divertor target, and leads to improved energy confinement. ELM suppression in this configuration is accompanied by the excitation of an Edge Harmonic Mode. In contrast, with unfavorable ion ∇B drift away from the upper X-point, boron injection also suppresses ELMs but leads to a more symmetric detachment state of both the inner and outer divertor targets, while plasma energy confinement is slightly degraded despite similar boron injection levels; a different low-frequency coherent mode without multiple harmonics is observed. Measurements from toroidally separated views show that the divertor response to boron injection is essentially toroidally symmetric, supporting the use of two-dimensional SOLPS-ITER modeling with a toroidally uniform impurity source. These experimental observations are qualitatively consistent with SOLPS-ITER simulations, which highlight the critical role of E × B drift effects in setting the Bt-dependent in–out asymmetry of detachment and in asymmetrically transporting particles and injected impurities within the scrape-off layer and private-flux region. These findings underscore the importance of drift physics and real-time wall conditioning in controlling low-Z impurity transport and optimizing edge solutions for integrated, ELM-stable, high-performance tokamak operation.

E × B drift↗
DOE OSTI2026

Simultaneous ELM suppression and divertor detachment via synergistic boron powder and neon injection in EAST

A novel approach for simultaneous power exhaust and edge-localized mode (ELM) control is presented in the Experimental Advanced Superconducting Tokamak discharges, which utilize an ITER-like tungsten divertor. Real-time injection of boron (B) powder and neon (Ne) gas overcomes their limitations encountered when used separately. Pure Ne seeding leads to a narrow operational window constrained by core impurity accumulation and H-mode to L-mode back transitions, while pure solid B injection (SBI) is insufficient for effective divertor cooling. In comparison, their combined use achieves a stable, stationary, ELM-suppressed H-mode with adequate power exhaust. This synergistic scenario features partial energy detachment at the outer divertor while maintaining good plasma confinement (H 98 ∼ 1) with minimal degradation. Two key features of this scenario are: (1) the SBI triggers a persistent Edge Harmonic Mode (EHM), which provides a crucial continuous particle transport channel, preventing Ne and tungsten/molybdenum accumulation without flushing out by ELM, and (2) the B + Ne mixture allows for active optimization of the radiated power profile. Core radiation can be reduced by substituting a portion of the Ne with B, leveraging their complementary non-coronal equilibrium radiation efficiencies. This combined B + Ne injection scheme presents a promising pathway toward integrated core-edge scenarios, offering the potential to minimize total impurity throughput while leveraging an actuator (powder injection) already being considered for ITER.

Boron↗
DOE OSTI2025

A novel liquid argon purity monitor based on 207 Bi

A novel liquid argon purity monitor based on a 207 Bi radioactive source, emitting monochromatic internal-conversion electrons, is presented. This new monitor allows for a very precise and fast measurement of the electronegative impurities concentration in liquid argon. It can be operated continuously in liquid argon TPC experiments without interfering with the main detector operation. Different drift lengths can be assembled for the proposed device, to assess a large range of liquid argon purities while minimizing systematic uncertainties. Two prototypes have been built and successfully operated in dedicated test stands. The results and performance are reported.

double-phase↗
DOE OSTI2023

Greenhouse gas emissions embodied in the U.S. solar photovoltaic supply chain

Abstract Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO 2 e kWh −1 of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO 2 e kWh −1 .

14 SOLAR ENERGY↗
DOE OSTI2025

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

hurricane↗
DOE OSTI2021

A cross-study analysis of drug response prediction in cancer cell lines

Abstract To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗
DOE OSTI2020

A public website for the automated assessment and validation of SARS-CoV-2 diagnostic PCR assays

Abstract Summary Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay-screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true-positive rate (recall) to simplify assay comparisons. Availability and implementation The assay evaluation website and supporting software are Open Source and freely available at https://covid19.edgebioinformatics.org/#/assayValidation, https://github.com/jgans/thermonucleotide BLAST and https://github.com/LANL-Bioinformatics/assay_validation. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The impact of curation errors in the PDBBind Database on machine learning predictions of protein–protein binding affinity

The PDBBind database has been widely utilized for the computational prediction of protein–protein binding affinities. While the accuracy of the PDBBind-curated equilibrium dissociation constants (K D ) has been reported for the protein–ligand subset of the PDBBind database, the curation accuracy has not been reported for the protein–protein subset. Here, we present a detailed manual analysis for the subset of PDBBind records with PubMed Central Open Access primary publications and find that ~19% of these records had K D values that were not supported by their primary publications. The impact of these putative curation errors on the machine learning-based prediction of K D from experimental protein–protein 3D structures was evaluated and correcting the curation errors improved the Pearson correlation coefficient between measured and random forest-predicted log 10 (K D ) values by ~8 percentage points. This finding underscores the importance of dataset accuracy for computational modelling and highlights the need for more stringent curation processes when extracting information from the scientific literature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The Crown Pearl: a draft genome assembly of the European freshwater pearl mussel Margaritifera margaritifera (Linnaeus, 1758)

Abstract Since historical times, the inherent human fascination with pearls turned the freshwater pearl mussel Margaritifera margaritifera (Linnaeus, 1758) into a highly valuable cultural and economic resource. Although pearl harvesting in M. margaritifera is nowadays residual, other human threats have aggravated the species conservation status, especially in Europe. This mussel presents a myriad of rare biological features, e.g. high longevity coupled with low senescence and Doubly Uniparental Inheritance of mitochondrial DNA, for which the underlying molecular mechanisms are poorly known. Here, the first draft genome assembly of M. margaritifera was produced using a combination of Illumina Paired-end and Mate-pair approaches. The genome assembly was 2.4 Gb long, possessing 105,185 scaffolds and a scaffold N50 length of 288,726 bp. The ab initio gene prediction allowed the identification of 35,119 protein-coding genes. This genome represents an essential resource for studying this species’ unique biological and evolutionary features and ultimately will help to develop new tools to promote its conservation.

Gomes-dos-Santos, André (ORCID:0000000199734861)↗
DOE OSTI2020

Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches

We describe the use of in silico approaches to improve the process of molecular assay development and reduce time and cost by utilizing available databases of whole genome pathogen sequences combined with modern bioinformatics and physical modeling tools. Well-characterized assays are needed for accurately detecting pathogens in environmental and patient samples and also for evaluation of the efficacy of a medical countermeasure that may be administered to patients. The polymerase chain reaction (PCR) remains the gold standard for pathogen detection due to the simplicity of its instrumentation, low cost of reagents, and outstanding limit of detection (LOD), sensitivity, and specificity. However, creation of such PCR assays often involves iterations of design, preliminary testing, and thorough validation with clinical isolates and testing in relevant matrices, which can be time consuming, costly, and result in suboptimal assays. Since formal validation (e.g., for Emergency Use Authorization [EUA] or Food and Drug Administration [FDA] licensure) of an infectious disease assay can be very expensive and can require extensive time of development, having a well-designed assay up front is a critical first step. Yet, many assays described in the literature utilized limited design capabilities and many initially promising assays fail the validation process, resulting in increased costs and timelines for successful product development. While the computational approaches outlined in this document by no means obviate the need for wet lab testing, they can reduce the amount of effort wasted on empirical optimization and iterative redesigns and also guide validation studies. The proposed computational approaches also result in higher performing assays with better sensitivity, specificity, and lower LOD and reduce the possibility of assay failure due to signature erosion. To provide clarity, an extensive glossary of defined terms is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Precision Local Burnup Assessment Through Dynamic Peak Fitting in Atom Probe Tomography for Depleted, Enriched, and Irradiated Metallic and Ceramic Fuels

Abstract Burnup estimation in nuclear fuels is vital for evaluating fuel performance, transportation, and safe fuel storage. Accurate assessments of burnup from service period and spent fuels involve tracking the consumption of fissile isotopes of uranium (U) offering a direct insight into energy changes within the fuels especially for thermal spectrum reactors. In current approach, mass spectroscopic technique in atom probe tomography (APT) is utilized for accurate quantification of U isotopes. Quantification of U peaks in mass spectrum is performed on asymmetric shapes due to delayed signals, known as thermal tails, particularly for poorly conducting samples analyzed in laser mode. In this study, we introduce a novel quantification tool for isotopic analysis from APT datasets by developing a fitting algorithm based on shapes of the peaks. A MATLAB-based dynamic peak fitting toolbox is developed and designed to adapt to various peak shapes, ensuring accurate quantification of U isotopes. The effectiveness of this approach is demonstrated in standard Ni-Cr sample, depleted and enriched U samples, and U-based fuels with different burnup levels. The viability of this approach for isotopic quantification is demonstrated on both metallic and ceramic fuels.

Burnup↗
DOE OSTI2023

TOI-908: a planet at the edge of the Neptune desert transiting a G-type star

ABSTRACT We present the discovery of an exoplanet transiting TOI-908 (TIC-350153977) using data from TESS sectors 1, 12, 13, 27, 28, and 39. TOI-908 is a T = 10.7 mag G-dwarf (Teff = 5626 ± 61 K) solar-like star with a mass of 0.950 ± 0.010 M⊙ and a radius of 1.028 ± 0.030 R⊙. The planet, TOI-908 b, is a 3.18 ± 0.16 R⊕ planet in a 3.18 d orbit. Radial velocity measurements from HARPS reveal TOI-908 b has a mass of approximately 16.1 ± 4.1 M⊕, resulting in a bulk planetary density of $2.7^{+0.2}_{-0.4}$ g cm−3. TOI-908 b lies in a sparsely populated region of parameter space known as the Neptune desert. The planet likely began its life as a sub-Saturn planet before it experienced significant photoevaporation due to X-rays and extreme ultraviolet radiation from its host star, and is likely to continue evaporating, losing a significant fraction of its residual envelope mass.

Hawthorn, Faith (ORCID:000000028675182X)↗
DOE OSTI2025

Measurement of spin-density matrix elements in 𝜙⁡(1020) → 𝐾$^{0}_{𝑆}$⁢𝐾$^{0}_{𝐿}$ photoproduction with a linearly polarized photon beam at 𝐸 𝛾 = 8.2–8.8 GeV

We measure the spin-density matrix elements (SDMEs) for the photoproduction of 𝜙⁡(1020) off of the proton in its decay to 𝐾$^{0}_{𝑆}$⁢𝐾$^{0}_{𝐿}$, using 105 pb −1 of data collected with a linearly polarized photon beam using the GlueX experiment. The SDMEs are measured in nine bins of the squared four-momentum transfer 𝑡 in the range −𝑡=0.15−1.0 GeV 2 , providing the first measurement of their 𝑡 dependence for photon beam energies of 𝐸 𝛾 = 8.2−8.8 GeV. We confirm the dominance of Pomeron exchange in this region and put constraints on the contribution of other Regge exchanges. We also find that helicity amplitudes where the helicity of the photon and 𝜙⁡(1020) differ by two units are negligible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗