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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 235 records · Page 13

Plasma expansion and relativistic filamentation in intense laser-irradiated cone targets

We report that compound parabolic concentrator (CPC) cone targets have been shown to produce increased MeV photons on the NIF-ARC by 10× over flat targets. Multiple x-ray frames can potentially be generated by firing the NIF-ARC's beamlets into distinct cone targets at few nanosecond relative delays. This requires that the cone targets with delayed beams are not degraded by their proximity to previous targets. One concern is that the spatial wings of a beam fired into one target can fall on neighboring targets, producing a preformed plasma that may interfere with laser light reaching the tip of the cone. In this work, 3D hydra simulations of realistic targets and beam parameters show that hundreds of micrometer scale length preplasmas are produced in cones within 1 mm of the laser spot. 2D particle-in-cell simulations of the intense main pulse in this preplasma indicate a density threshold for the onset of relativistic filamentation in our conditions. Applying our modeling approach to a NIF-ARC shot with an intentional 15 J prepulse yields good agreement with experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Polariton-assisted incoherent to coherent excitation energy transfer between colloidal nanocrystal quantum dots

Here, we explore the dynamics of energy transfer between two nanocrystal quantum dots placed within an optical microcavity. By adjusting the coupling strength between the cavity photon mode and the quantum dots, we have the capacity to fine-tune the effective coupling between the donor and acceptor. Introducing a non-adiabatic parameter, γ, governed by the coupling to the cavity mode, we demonstrate the system’s capability to shift from the overdamped Förster regime (γ $\ll$ 1) to an underdamped coherent regime (γ $\gg$ 1). In the latter regime, characterized by swift energy transfer rates, the dynamics are influenced by decoherence time. To illustrate this, we study the exciton energy transfer dynamics between two closely positioned CdSe/CdS core/shell quantum dots with sizes and separations relevant to experimental conditions. Employing an atomistic approach, we calculate the excitonic level arrangement, exciton–phonon interactions, and transition dipole moments of the quantum dots within the microcavity. These parameters are then utilized to define a model Hamiltonian. Subsequently, we apply a generalized non-Markovian quantum Redfield equation to delineate the dynamics within the polaritonic framework.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow

Background: Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims: The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods: We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key Results: Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions: While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications: This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

60 APPLIED LIFE SCIENCES↗

Optimal function estimation with photonic quantum sensor networks

The problem of optimally measuring an analytic function of unknown local parameters each linearly coupled to a qubit sensor is well understood, with applications ranging from field interpolation to noise characterization. Here we resolve a number of open questions that arise when extending this framework to Mach-Zehnder interferometers and quadrature displacement sensing. In particular, we derive lower bounds on the achievable mean square error in estimating a linear function of either local phase shifts or quadrature displacements. In the case of local phase shifts, these results prove, and somewhat generalize, a conjecture by Proctor []. For quadrature displacements, we extend proofs of lower bounds to the case of arbitrary linear functions. We provide optimal protocols achieving these bounds up to small (multiplicative) constants and describe an algebraic approach to deriving new optimal protocols, possibly subject to additional constraints. Using this approach, we prove necessary conditions for the amount of entanglement needed for any optimal protocol for both local phase and displacement sensing. Published by the American Physical Society 2024

Bringewatt, Jacob↗

Ultrafast Annealing Improves SNR and Long-Term Stability of a Highly Multiplexed Line-by-Line FBG Array Inscribed by Femtosecond Laser in a Coreless Fiber for Extreme-Temperature Applications

This study reports the fabrication of a fourth-order line-by-line fiber Bragg grating (FBG) array using femtosecond laser inscription within a highly multimode coreless optical fiber, with a particular focus on achieving substantial multiplexing capabilities. An ultrafast annealing procedure is employed, resulting in an impressive enhancement of the FBG sensor’s fringe visibility by approximately 13 dB, signifying a notable improvement of approximately ~4 dB. This substantial enhancement contributes to the long-term stability and performance of the multiplexed FBG array in extreme-temperature conditions. The systematic fabrication approach employed for the multiplexed FBG array guarantees a high signal-to-noise ratio (SNR) for each individual FBG within the array. This FBG array is intended for extreme-temperature applications, addressing limitations associated with traditional FBGs based on doped optical fibers, including SNR degradation and temperature-induced fringe drift. Testing at temperatures up to 1120 °C demonstrates the FBG array’s stability without fluctuations in readings. Furthermore, it endures seven heat cycles, spanning from 500 °C to 1120 °C, over 60 h, exhibiting outstanding thermal stability. Furthermore, this highly multiplexed FBG array with an ultrafast annealing approach holds promise for extreme-temperature environments, such as steelmaking, where precise and reliable distributed temperature monitoring is imperative.

47 OTHER INSTRUMENTATION↗

To Fail or not to Fail: An Exploration of Machine Learning Techniques for Predictive Maintenance

Predictive maintenance refers to the ability to predict when machinery or systems need to be maintained. Making an accurate prediction is quite challenging given the costs for both over-estimating (unnecessary maintenance and reduction in availability of assets) and under-estimating (untimely breakdowns and possible loss of equipment or lives). To address these challenges researchers were able to develop new approaches for analyzing oil samples taken extracting samples from oil-wetted machinery that may provide information critical to developing predictive capabilities. We consider the problem from both supervised (though data limited) and unsupervised approaches and provide a first look into a data driven approach for identification of condition indicators. Through this work we identify a collection of candidate features that can form the basis of condition indicators for both a high level discrimination of failure vs. normal operation as well as a set for potential failure mode identification. Finally, we present an anomaly detection framework for detecting failures which can be a viable solution for an onboard analysis tool in deployed systems.

predictive maintenance, anomaly detection, Laserne↗

Subsampling of Parametric Models with Bifidelity Boosting

Least squares regression is a ubiquitous tool for building emulators (a.k.a. surrogate models) of problems across science and engineering for purposes such as design space exploration and uncertainty quantification. When the regression data are generated using an experimental design process (e.g., a quadrature grid) involving computationally expensive models, or when the data size is large, sketching techniques have shown promise at reducing the cost of the construction of the regression model while ensuring accuracy comparable to that of the full data. However, random sketching strategies, such as those based on leverage scores, lead to regression errors that are random and may exhibit large variability. To mitigate this issue, we present a novel boosting approach that leverages cheaper, lower-fidelity data of the problem at hand to identify the best sketch among a set of candidate sketches. This in turn specifies the sketch of the intended high-fidelity model and the associated data. We provide theoretical analyses of this bifidelity boosting (BFB) approach and discuss the conditions the low- and high-fidelity data must satisfy for a successful boosting. In doing so, we derive a bound on the residual norm of the BFB sketched solution relating it to its ideal, but computationally expensive, high-fidelity boosted counterpart. Finally, empirical results on both manufactured and PDE data corroborate the theoretical analyses and illustrate the efficacy of the BFB solution in reducing the regression error, as compared to the nonboosted solution.

97 MATHEMATICS AND COMPUTING↗

Architecture-Based Control of Temperature Gradient-Driven Water Transport in Polymer Electrolyte Fuel Cells

Temperature gradients can move water within the porous media of a polymer electrolyte fuel cell through phase-change-induced (PCI) flow. It is critical to understand PCI flow so that control and distribution of water within a fuel cell can be accomplished. This work investigates the role of architecture, specifically anode land width, on overall cell water content and distribution in the through-plane direction. A specially-designed 4.8 cm 2 fuel cell with precise thermal boundary conditions was imaged with neutrons using two different anode flow field configurations. A new, non-dimensional thermal transport number was developed which quantifies the relative influence of PCI flow on cell water transport. It was found that anode lands larger than the cathode lands cause large thermal gradients that instigate net water flux from the cathode to the anode. An asymmetric configuration with larger anode lands was found to have large changes in water content that were strongly sensitive to cell operating conditions. The thermal transport number developed here enables deduction of the net flux condition based on operating conditions and architecture. This approach enables design of high-performance systems with balanced water management.

25 ENERGY STORAGE↗

Control development and sizing analysis for a 5th generation district heating and cooling network using Modelica

5th generation district heating and cooling system (5GDHC) are a relatively new concept. They use a single district loop near ambient temperature to provide heating and cooling. This paper improves on the modelling and control of a 5GDHC system called the reservoir network. The study updates the sewage heat exchanger plant model to more realistically represent seasonal changes, uses refined pump models with variable efficiency, introduces a ground coupled district pipe model to consider the inertia of the district network and implements a new control strategy for geothermal storage and sewage heat exchanger. The new approach reduced operating costs, mainly due to pumping cost for storage, sewage heat exchanger plant and distribution pump, while increasing the overall robustness of the approach in different sizing conditions. Thanks to the new controller, the pumping consumption was reduced by 21% with respect to the original baseline. Furthermore, the new control makes the system take better advantage of design changes, when reducing borehole field size and increasing the sewage heat exchanger size, the pumping energy savings become 29% with respect to the original baseline. Lastly, borehole field temperature stability was analyzed through 40 years of simulation.

Zanetti, Ettore↗

Collaborative Research: Unravelling the Physics Associated with the Production of Extremely Dense Plasma States of Microscale (Final Report) Nanosecond-pulsed Discharges

The aim of this project is to study and establish the physical mechanisms that contribute to the formation of anomalously dense plasmas in high-pressure nanosecond-pulsed discharges. These discharges have a broad range of applications such as plasma-assisted combustion, plasma flow actuators, biomedical sterilization and exotic materials synthesis. The structure and formation of these discharges, producing high plasma densities of ~10 14 -10 15 cm -3 , are well-studied and understood. Fast-pulsed microscale high-pressure discharges can be driven to even higher densities of > 10 19 cm -3 , approaching warm dense matter conditions. The mechanisms that generate these plasmas have not been understood. Analysis of the warm dense matter state under laboratory conditions is an expensive and non-trivial endeavor. For instance, dense plasmas can be generated by electrical explosions of metal foils and wires. Plasmas generated after the explosion have a short lifetime and often present difficult conditions for diagnostics. Generation of dense plasmas was also achieved during high-voltage nanosecond pulsed discharges when the so-called explosive electron emission is obtained. Unfortunately, this process is very difficult to control for the studies of warm dense matter. In our recent study, we have shown that additional heating of plasma by lasers can further increase the density of plasma and even lead to the fully ionized state. This method, potentially, allows better control of the plasma parameters. In this work, we studied a second stage laser-heated micro-discharge using a self-consistent one-dimensional particle-in-cell Monte Carlo-collision (1D PIC-MCC) model coupled with Maxwell’s equations. We predicted the generation of a fully ionized plasma on the picosecond time scale. However, this model considered the plasma as an ideal gas despite the high pressure and the nearly fully ionized state. The ideal plasma model assumes that the dilute gas approximation is valid, where the inter-particle interactions are negligible. For charged particles this assumption holds as long as the shielded Coulomb potential assumption is valid. For very high plasma densities, this concept breaks down since the Debye sphere surrounding each charged particle no longer contains enough electrons to statistically provide the shielding of the single particle Coulomb interaction potential. At such densities, the plasma can no longer be described as ideal and non-ideal coupling effects need to be considered. In this report, we elucidate our recent work of developing a PIC-MCC model with improvements for non-ideal plasma conditions due to Coulomb coupling at high densities. In particular, we study the interaction of green light radiation and a dense microplasma, and explore the non-ideal plasma effects in this interaction. In this computational model, we implement the two most important non-ideal effects: ionization potential depression (IPD) and enhanced collision cross sections. Our primary goal is to study the physics associated with electromagnetic (EM) wave heating, also called the second-stage wave-heating, and establish the role of plasma non-ideality in this phenomenon. Our secondary goal is to improve the chemistry mechanism of the 1D PIC-MCC model by including a more detailed excited species collision treatment. At high pressures, stepwise ionization from excited species might play an important role in the ionization process. Previously, this ionization mechanism was neglected due to the excitation collision cross section of xenon being smaller than that of ionization. However, a preliminary study showed that the excited species density in the initial microplasma was an order of magnitude higher than the electron density. Therefore, my aim is to determine the significance of this additional ionization pathway to the plasma generation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Pedestrian-Involved Traffic Signal Optimization Using Decentralized Graph-based Multi-Agent Reinforcement Learning

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

K Kumarasamy, Vijayalakshmi↗

COGIHTO: Comprehensive Geothermal Integrated HVAC Retrofits in Disadvantaged Communities

Geothermal heating and cooling (GHC) systems offer one of the most efficient approaches to provide space conditioning and water heating in buildings. Due to the relatively stable temperature of the ground year-round, these systems can provide an efficient solution to decarbonize heating systems. Yet this technology has faced limited market adoption due to its high first cost. This project seeks to address this barrier by designing a community GHC system at a public housing development in New York, taking advantage of load diversity (in terms of the timing and amount of heating / cooling used per person) that a larger community offers to reduce the size and thus first cost of a GHC system. Project success would result in societal benefit from reduced energy and fossil fuel use, reduced local emissions, and improved comfort, which would positively impact the health and well-being this community. This report presents the findings from Phase 1 project supported by the Department of Energy’s Geothermal Technologies Office and includes the technical design of a GHC at a public housing development in New York. To support the success of a potential deployment of a GHC system, the team conducted community engagement and prepared an assessment of the local workforce needs. The project team, led by EPRI, considered a subset of NYCHA housing developments that were previously identified by NYCHA as suitable for GHC retrofits. This report presents the site selection criteria the team developed and the overall score assigned to each of the eight developments considered. One site was selected for focus of the technical design: Stapleton Houses in Staten Island, a mid-size development with roughly 700 apartments across 6 buildings. The site has an existing hot water hydronic loop served by natural gas boilers for space heating and water heating. The technical design partner assessed baseline energy consumption at the site and established thermal models of the apartment buildings to assist in the design. The team considered several GHC system configurations and selected a hybrid geothermal design fed from water-source heat pumps with auxiliary gas boilers in a central plant. This design best leveraged the existing site configuration and minimized costs due to additional electrical infrastructure upgrades. The report also details the feedback received from community engagement efforts and the findings from the workforce needs assessment and workforce transition plan. Members of the site maintenance staff and the resident advisory council were interviewed for their input and feedback about both the site’s existing heating/cooling infrastructure and how a construction project would impact resident life. This feedback was taken into consideration when completing the geothermal system design. Research was conducted into existing workforce development opportunities available to NYCHA residents. A workforce development plan was established using information from NYCHA’s resident economic empowerment and sustainability group, along with training material and curriculum from the International Ground Source Heat Pump Association (IGSHPA).

15 GEOTHERMAL ENERGY↗

On the Existence of Fast Modes in Compressible Magnetohydrodynamic Turbulence

Abstract We study the existence and properties of fast magnetosonic modes in 3D compressible MHD turbulence by carrying out a number of simulations with compressible and incompressible driving conditions. We use two approaches to determine the presence of fast modes: mode decomposition based on spatial variations only and spatio-temporal 4D fast Fourier transform (4D FFT) analysis of all fluctuations. The latter method enables us to quantify fluctuations that satisfy the dispersion relation of fast modes with finite frequency. Overall, we find that the fraction of fast modes identified via the spatio-temporal 4D FFT approach in total fluctuation power is either tiny with nearly incompressible driving or ∼2% with highly compressible driving. We discuss the implications of our results for understanding the compressible fluctuations in space and astrophysical plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗

Cloud water adjustments to aerosol perturbations are buffered by solar heating in non-precipitating marine stratocumuli

Abstract. Marine low-level clouds are key to the Earth's energy budget due to their expansive coverage over global oceans and their high reflectance of incoming solar radiation. Their responses to anthropogenic aerosol perturbations remain the largest source of uncertainty in estimating the anthropogenic radiative forcing of climate. A major challenge is the quantification of the cloud water response to aerosol perturbations. In particular, the presence of feedbacks through microphysical, dynamical, and thermodynamical pathways at various spatial and temporal scales could augment or weaken the response. Central to this problem is the temporal evolution in cloud adjustment, governed by entangled feedback mechanisms. We apply an innovative conditional Monte Carlo subsampling approach to a large ensemble of diurnal large-eddy simulation of non-precipitating marine stratocumulus to study the role of solar heating in governing the evolution in the relationship between droplet number and cloud water. We find a persistent negative trend in this relationship at night, confirming that the role of microphysically enhanced cloud-top entrainment. After sunrise, the evolution in this relationship appears buffered and converges to ∼-0.2 in the late afternoon. This buffering effect is attributed to a strong dependence of cloud-layer shortwave absorption on cloud liquid water path. These diurnal cycle characteristics further demonstrate a tight connection between cloud brightening potential and the relationship between cloud water and droplet number at sunrise, which has implications for the impact of the timing of advertent aerosol perturbations.

Zhang, Jianhao (ORCID:0000000169882935)↗

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

A Coupled Experimental and Simulation Approach to Investigate the Impact of Grain Growth, Amorphization, and Grain Subdivision in Accident Tolerant U 3 Si 2 Light Water Reactor Fuel

Triuranium disilicide (U 3 Si 2 ) is being considered as an alternative accident tolerant fuel (ATF) due to its higher thermal conductivity. However, there is uncertainty in its use due to a lack of irradiation data at light water reactor (LWR) conditions. In this project, we used an integrated experimental and simulation approach to answer two significant questions pertaining to U 3 Si 2 that were not investigated by the Fuel Cycle Research and Development (FCRD) and Nuclear Energy Advanced Modeling and Simulation (NEAMS) programs: (1) Will grain growth in the hotter portions of the fuel significantly impact U 3 Si 2 LWR fuel behavior? (2) Under what conditions do grain subdivision and amorphization occur in U 3 Si 2 and will either occur at LWR conditions? Our approach to answer these questions used in situ ion irradiation and annealing to investigate grain growth, grain subdivision, and amorphization, along with mesoscale simulations using the MARMOT tool. We found that while the grain boundary mobility of U 3 Si 2 is larger than that of UO 2 , grain growth in U 3 Si 2 fuel pellets will be less than in UO 2 and may be neglected due to the lower thermal conductivity and thus lower centerline temperature. We also found that amorphization will not occur in U 3 Si 2 above 600 K, such that it is not likely to occur at typical LWR operating conditions. Grain subdivision will occur at LWR conditions and will likely occur at lower burnups than in UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗