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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Constructing Regulatory Networks to Compare Axenic and Interspecies Microbial Gene Transcription

In this preliminary study, we constructed gene regulatory networks (GRNs) from transcriptional expression data of axenic and interspecies microbial cultures with the goal of predicting how cocultivation affected greenhouse gas respiration by these species. The specific strains of Methylotuvimicrobium alkaliphilum 20Z, a methylotroph, and Cyanobacterium stanieri HL-69, a phototroph, were chosen for their viability in industrial bioprocessing. We ranked directed interactions between gene pairs based on the ability of the input gene to predict the expression of a target gene relative to their transcriptomes. While we were able to identify topological differences between conditions, our initial findings require validation through experimental analysis and further modeling. We aimed to develop a systematic thresholding approach to optimize the accuracy of our networks. We filtered out trial networks separately from top gene interactions of the scored rankings. Parameters of unfiltered and filtered networks were used to test and develop thresholding approaches. Knee point detection of edge weight distributions was explored as an approach for separating significant interactions from insignificant interactions in unfiltered networks. While knee detection failed to produce analogous networks for broad cross-condition comparisons, the results informed us about the proportions of significant edges present in unfiltered networks. We also calculated the average mean degree for nodes in a selection of trial networks to find a thresholding value characteristic to all groups. While we did not reach a definitive conclusion, we gained insight into the coregulatory structures of our groups and made critical evaluations of systematic methods for filtering networks. We recommend an iterative process for the inference of GRNs, where the most significant results from preliminary explorations are used to improve the efficiency with which regulatory motifs are chosen for experimental characterization. Experimental results can then inform the framework of adjusted models to improve broad interpretations of GRNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Analog Systems for Edge Optimization

Over the past decade, analog computing has the subject of substantial research interest providing a path toward improved computational efficiency in the post-Dennard era. Analog matrix vector multiplication (MVM) accelerators provide a popular approach given the ubiquity of MVM operations in numerous applications. However, historically analog computing systems can struggle with applications requiring high precision due to the inherent susceptibility of these systems to analog non-idealities. Therefore, prior work on analog systems has focused either on applications known to be tolerant of limited precision (e.g., neural network inference), or using expensive techniques to emulate high-precision using many analog MVM operations. In this work, we propose an alternative approach. Motivated by recent advances in inexact nonlinear solvers and optimizers, we explore the potential of co-designing optimization algorithms which can take full advantage of the fundamentally inexact analog MVM operations. To enable these co-designed algorithms we also develop a general mathematical theory of the precision and energy efficiency of analog operations, and a new system architecture for tightly-coupled analog and digital computation. Finally, we examine the applicability of analog computing to a wider class of symmetric positive definite systems and find potential in using analog operations as a sparse approximate inverse preconditioner. With these core innovations, this project provides a path toward effectively implementing optimization algorithms on power-constrained autonomous and semi-autonomous systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2023

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.

Yin, Junqi↗
DOE OSTI2023

The effectiveness of D 2 pellet injection in reducing intra-ELM and inter-ELM tungsten divertor erosion rates in DIII-D during the Metal Rings Campaign

Abstract Edge localized modes (ELMs) in H-mode plasmas erode plasma-facing components (PFCs) and lead to impurities in the core, reducing confinement. This study analyzes D 2 pellet injection on the DIII-D fusion experiment used as an ELM mitigation technique applied during the 2016 tungsten Metal Rings Campaign to reduce W erosion during ELMs. The 400.9 nm photon wavelength line emission intensity of tungsten atoms (WI) filterscope channels and Langmuir probes were used to infer the gross erosion rate of tungsten-coated tiles installed in the divertor of DIII-D. D 2 mass injection rates ranging from 34 to 41 arbitrary units (A.U.) and no D 2 injection resulted in a similar total W erosion rate during ELMs (intra-ELM). On average, results show a 29% increase in the total gross W erosion rate with intermediate mass injection rates (∼13–23 A.U.) compared to the no pellets and the highest injection rate cases. On average, the fast D 2 mass injection rate cases had 15% less erosion in the inter-ELM phase than the case with no pellets. Generally, higher D 2 mass injection rates increased the ELM frequency, and the highest injection rates reduced the average erosion per ELM and fractional carbon impurities at the top of the pedestal by nearly 40% when compared to the no-pellet case. As expected, a higher D 2 pellet injection rate led to a higher plasma density and lower plasma temperature in the divertor. Additionally, an increasing divertor inter-ELM plasma electron density directly correlated to more frequent pellet injection and a decrease in both the average gross intra-ELM W erosion and the total gross intra-ELM W erosion rate. Simulations of intra-ELM erosion using the ‘free-streaming plus recycling model’ (FSRM) underestimate W erosion during pellet injection by about 30% on average. The discrepancies between the experimental measurements and the FSRM intra-ELM W erosion predictions are postulated to be due to C/W material mixing. A simple analytic mixed-material model is presented and results in better agreement with the experimental data. These results highlight the importance of incorporating the effects of a mixed-material layer in the analysis of PFC erosion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Particle control via cryopumping and its impact on the edge plasma profiles of Alcator C-Mod

At the high n e proposed for high-field fusion reactors, it is uncertain whether ionization, as opposed to plasma transport, will be most influential in determining n e at the pedestal and separatrix. A database of Alcator C-Mod discharges is analyzed to evaluate the impact of source modification via cryopumping. The database contains similarly-shaped H-modes at fixed I P = 0.8 MA and B t = 5.4 T, spanning a large range in P net and ionization. Measurements from an edge Thomson scattering system are combined with those from a midplane-viewing Ly α camera to evaluate changes to n e and T e in response to changes in ionization rates, S ion ∙ $n^{sep}_e$ and $T^{ped}_e$ are found to be most sensitive to changes to $S^{sep}_{ion}$, as opposed to $n^{ped}_e$ and $T^{sep}_e$. Dimensionless quantities, namely α MHD and v*, are found to regulate attainable pedestal values. Select discharges at different values of P net and in different pumping configurations are analyzed further using SOLPS-ITER. It is determined that changes to plasma transport coefficients are required to self-consistently model both plasma and neutral edge dynamics. Pumping is found to modify the poloidal distribution of atomic neutral density, n 0 , along the separatrix, increasing n 0 at the active X-point. Opaqueness to neutrals from high n e in the divertor is found to play a role in mediating neutral penetration lengths and hence, the poloidal distribution of neutrals along the separatrix. Pumped discharges thus require a larger particle diffusion coefficient than that inferred purely from 1D experimental profiles at the outer midplane.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

An improved theory of the response of DIII-D H-mode discharges to static resonant magnetic perturbations and its implications for the suppression of edge localized modes

The plasma response to an externally generated, static, n = 2, resonant magnetic perturbation (RMP) in the pedestal region of DIII-D H-mode discharge #158115 is investigated using a toroidal generalization of the asymptotic matching model presented by Fitzpatrick [Phys. Plasmas 27, 042506 (2020)]. Just as in a recent paper [Q. M. Hu et al., Phys. Plasmas 26, 120702 (2019)], it is hypothesized that the density pump-out phenomenon is due to locked magnetic island chains induced at the bottom of the pedestal, whereas the suppression of edge localized modes is associated with a locked magnetic island chain induced at the top of the pedestal. Neutral penetration inside the last closed magnetic flux-surface is found to have a significant influence on locked magnetic island chain formation at the bottom of the pedestal. It is found that locked island formation at the top of the pedestal is only possible when q95 lies in certain narrow windows. Finally, it is inferred that, in order to successfully induce a locked island chain at the top of the pedestal, an external RMP field-coil system must generate a magnetic field that is simultaneously strongly amplified by the plasma (via the ideal kink response) and has a large resonant component at a rational surface that lies close to the top of the pedestal.

Fitzpatrick, R. (ORCID:0000000162379309)↗
DOE OSTI2026

Towards self-consistent integrated modeling of the tokamak pedestal, scrape-off layer, and divertor using SOLPS-ITER and EPED

A predictive, self-consistent integrated model of the tokamak edge plasma and neutral system has been developed and validated against experimental data from DIII-D, spanning from the divertor target and scrape-off layer to the top of the pedestal. The model uses the SOLPS-ITER fluid boundary plasma and neutral code to constrain the EPED prediction of the pedestal height and width by taking the separatrix temperature and density calculated from SOLPS as inputs to EPED. An empirical proxy function based on data from a subset of these discharges is then used to infer the ratio of density at the pedestal top to that at the separatrix based on the electron temperature at the divertor target. This allows the density at the top of the pedestal to be provided to EPED as well, so that knowledge of this quantity is no longer required a priori, while SOLPS radial transport coefficients are needed instead. The gas puff rate provided to SOLPS is then a primary model input for this forward model coupling (along with radial transport coefficients, which are kept fixed through each scan in this study). Comparisons with measurements from DIII-D density scans through a range of target conditions find significant pedestal pressure degradation as the density is increased towards detachment in three different divertor configurations, consistent with the experimental data in these ballooning-limited pedestal regimes.

core-edge integration↗
DOE OSTI2021

Predicted Heat Flux Performance of Actively Cooled Tungsten-Armored Graphitic Foam Monoblocks

Tungsten (W)–armored graphitic foam monoblocks were developed for applications requiring high-Z plasma-facing material in long-pulse fusion experiments and ultimately deuterium-tritium fusion reactors. The monoblocks are an integrated material system combining the advantages of a chemical vapor deposited (CVD) W coating with a high-conductivity graphitic foam. The W is a high-melting-point, high-Z material with low tritium retention. The graphitic foam coupled to a swirl tube serves as a high-thermal-conductivity heat sink that cannot melt, although it can sublime at much higher temperatures than copper melts. Together, they comprise a robust plasma-facing component (PFC) weighing roughly 5% of an all-W component or 17% of a traditional W-coated copper heat sink.A single-channel mock-up consisting of four graphitic foam monoblocks equipped with a water-cooled swirl tube was fabricated for eventual testing in the 60-kW, EB-60, rastered electron beam at the Applied Research Laboratory of The Pennsylvania State University. Two monoblocks have a thin 50-μm-thick coating of pure W chemically vapor deposited over NbC and pure Nb interlayers. Two others have a 2-mm-thick pure W coating CVD on graphitic monoblocks using the same interlayers. The mock-up will be cooled with available 10 m/s, 0.7 MPa water with a 22°C inlet temperature and subjected to varying uniform heat loads up to 20 MW/m 2 . It is equipped with type-K thermocouples at various depths, and calibrated infrared thermography and spot pyrometry will be used to characterize the heated surface. Real-time water calorimetry will be used to ascertain the absorbed steady-state power and infer the heat flux during testing. Since testing cannot be done under prototypic divertor flow conditions, it is necessary to predict the thermal response of this novel PFC system and investigate the power sharing between radiation and convection at divertor heat flux levels and its inherent ability to avoid critical heat flux. Results are reported for predictions obtained from computational fluid dynamics models up to 30 MW/m 2 of steady-state uniform heat flux. Leading-edge heat loads of 30 MW/m 2 on a 2-mm-wide side strip were also investigated to ascertain if coating delamination is likely.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

High-Pressure Investigation of 2,4,6-Trinitro-3-bromoanisole (TNBA): Structural Determination and Piezochromism

Understanding phase transitions in energetic materials is crucial for developing predictive models of detonation. 2,4,6-Trinitro-3-bromoanisole (TNBA), an energetic material, was studied in its single-crystal form up to pressures of 45 GPa in a diamond anvil cell. The material was characterized by using X-ray, Raman, and optical transmission measurements. From single-crystal X-ray diffraction, the ambient structure of TNBA was determined which crystallizes in the P2 1 /c space group having four molecular units per unit cell. The X-ray data up to 9.2 GPa were fitted to a third-order Birch–Murnaghan equation of state by using the parameters K o = 13.2(2.4) GPa and K p = 5.1(1.4). Between 6.8 and 7.3 GPa, a phase transition was inferred in TNBA from concurrent fading of X-ray diffraction, disappearance of Raman peaks, increase in sample fluorescence, and discontinuous color change. The new phase was consistent with an amorphous state of at least partially intact molecules judging from the presence of higher-order Raman modes and irreversibility of the Raman spectra upon release. Piezochromism was observed with the translucent yellow TNBA gradually darkening and becoming opaque black at ~25 GPa. This correlated to the absorption edge gradually shifting to the red in the visible spectrum. Signs of two possible additional structural transitions were detected in the 32.4–41.0 GPa range as suggested by a jump in the absorption edge, the irreversible changes in the absorption spectrum upon release to ambient pressure, and by the lack of Raman modes in recovered samples. Here, the crystal and electronic structures of TNBA were also investigated up to 10 GPa by using DFT calculations and crystal structure prediction (CSP) simulations. In agreement with the experimentally observed transition at 7 GPa, the simulations at 10 GPa found a bevy of polymorphs lower in enthalpy and higher in density than P2 1 /c. The lowest calculated enthalpy structure was determined to be P2 1 2 1 2 1 , being in a different space group than the ambient experimental result.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The phase-space structure of dark matter haloes

ABSTRACT The phase space structure of dark matter haloes can be used to measure the mass of the halo, infer mass accretion rates, and probe the effects of modified gravity. Previous studies showed that the splashback radius can be measured in position space using a sharp drop in the density profile. Using N-body simulations, we model the distribution of the kinematically distinct infalling and orbiting populations of subhaloes and haloes. We show that the two are mixed spatially all the way to redge, which extends past the splashback radius defined by the drop in the spherically averaged density profile. This edge radius can be interpreted as a radius that contains a fixed fraction of the apocentres of dark matter particles. Our results highlight the possibility of measuring the outer boundary of a dark matter halo using its phase space structure and provide a firm theoretical foundation to the satellite galaxy model adopted in the companion paper, where we analysed the phase space distribution of Sloan Digital Sky Survey redMaPPer clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The crystal structure of Fe2S at 90 GPa based on single-crystal X-ray diffraction techniques

The Fe-S system was explored in a laser-heated diamond-anvil cell at 89(2) GPa and 2380(120) K to better understand the phase stability of Fe 2 S. Upon temperature quenching, crystallites of Fe 2 S were identified, and their structure was investigated using single-crystal X-ray diffraction techniques. At these conditions, Fe 2 S adopts the C23 structure (anti-PbCl 2 , Co 2 P) with space group Pnma (Z = 4). This structure consists of columns of corner-sharing, FeS 4 tetrahedra, and columns of edge-sharing FeS 5 square pyramids linked along edges in the b direction. Sulfur is in ninefold coordination with Fe. This work marks the first high-pressure structural solution and refinement of Fe 2 S synthesized in a multigrain Fe+FeS sample at 90 GPa and 2400 K and establishes the stability of C23 Fe 2 S at these conditions. A previous powder diffraction study reports an orthorhombic Fe 2 S phase with a C37, Co 2 Si-like unit cell above 190 GPa. A C23–C37 structural transition is inferred to explain the previously observed unit-cell parameters at higher pressures and temperatures. These results highlight the utility of applying single-crystal X-ray diffraction techniques to high P-T multigrain samples to explore the structural properties of iron-rich phases in Earth and planetary cores.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Representing Complex Systems as Graphs for Debugging and Predictive Maintenance-Preliminary Thoughts

Representing complex systems as graphs enables use of mathematical tools to identify faults or predict failures. Graph nodes correspond to individual modules or subsystems, and edges link coupled system parts. ‘Probes’ measure the node outputs, monitoring the system health for unexpected behavior. Assuming one cannot probe every point, within a system, the fault correlates to a region—not necessarily the specific location. Bayesian networks trained to understand fault patterns can accurately identify the source. The diagnostic tool described aides debugging by pinpointing system failure causes. For predictive maintenance, probe data develop probability distribution functions describing subsystem mean time to failure. Unit lifetime can be estimated through these probability distributions. Two approaches include using Bayesian classifiers to infer the system failure source and developing maintenance schedules by treating systems as collections of random variables. When failure behavior does not follow a closed form function, use of similarity models is proposed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Metal-Catalyzed Thermo-Catalytic Decomposition and Continuous Catalyst Generation

In this study, metal dusting is utilized to initiate a two-stage thermo-catalytic decomposition (TCD) process. Stage 1 starts with metal-catalyzed TCD, and in stage 2 the metal-catalyzed carbon catalyzes additional TCD. TEM is presented of the early- versus late-stage TCD to qualitatively illustrate the second-stage TCD by the metal-catalyzed carbons. Corresponding SEM illustrates differences in growth type and surface density between early versus late reaction times, with backscattered imaging differentiating the first- versus second-stage TCD. TGA supports the microscopic inference of a second carbon phase by the presence of an early (low-temperature) reaction peak, characteristic of low-structure or disordered carbon as the second-stage TCD carbon. Raman analysis confirms that the second-stage carbon deposit is more disordered and unstructured, especially at 1000 °C, supported by the ID/IG and La value changes from 0.068 to 0.936 and 65 nm to 4.7 nm, respectively. To further confirm second-stage TCD occurrence upon pre-catalyzed carbons, two carbon blacks are tested. Exposing a combination of edge and basal or exclusively basal sites for the graphitized form, they afford a direct comparison of TCD carbon nanostructure dependence upon the initial carbon catalyst nanostructure. Pre-oxidation of the stainless-steel wool (SSW) prior to TCD is advantageous, accelerating TCD rates and increasing carbon yield relative to the nascent SSW for an equivalent reaction duration.

Chemistry↗