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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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Aquifer Monitoring Using Ambient Seismic Noise Recorded With Distributed Acoustic Sensing (DAS) Deployed on Dark Fiber
Abstract Groundwater is a critical resource for human activities worldwide, and a vital component of many natural ecosystems. However, the state and dynamics of water‐bearing aquifers remain uncertain, mostly due to the paucity of subsurface data at high spatial and temporal resolution. Here, we show that analysis of infrastructure‐generated ambient seismic noise acquired on distributed acoustic sensing (DAS) arrays has potential as a tool to track variations in seismic velocities (d v / v ) caused by groundwater level fluctuations. We analyze 5 months of ambient noise acquired along an unused, 23 km‐long telecommunication fiber‐optic cable in the Sacramento Valley, CA, a so‐called “dark fiber." Three array subsections, ∼6 km apart, are processed and the stretching technique is applied to retrieve daily d v / v beneath each location. Near the Sacramento river, d v / v variations in the order of 2%–3% correlate with precipitation events and fluctuations in river stage of ∼1.5 m. In contrast, regions away (2.5 km) from the river do not experience large d v / v variations. These observations reveal short‐scale spatial variability in aquifer dynamics captured by this approach. Dispersion analysis and surface wave inversion of noise gathers reveal that seismic velocity perturbations occur at depths of 10–30 m. Rock physics modeling confirms that observed d v / v are linked to pore pressure changes at these depths, caused by groundwater table fluctuations. Our results suggest that DAS combined with ambient noise interferometry provides a means of tracking aquifer dynamics at high spatial and temporal resolutions at local to regional scales, relevant for effective groundwater resource management.
Metaproteomics reveals enzymatic strategies deployed by anaerobic microbiomes to maintain lignocellulose deconstruction at high solids
Economically viable production of cellulosic biofuels requires operation at high solids loadings—on the order of 15 wt%. To this end we characterize Nature’s ability to deconstruct and utilize mid-season switchgrass at increasing solid loadings using an anaerobic methanogenic microbiome. This community exhibits undiminished fractional carbohydrate solubilization at loadings ranging from 30 g/L to 150 g/L. Metaproteomic interrogation reveals marked increases in the abundance of specific carbohydrate-active enzyme classes. Significant enrichment of auxiliary activity family 6 enzymes at higher solids suggests a role for Fenton chemistry. Stress-response proteins accompanying these reactions are similarly upregulated at higher solids, as are β-glucosidases, xylosidases, carbohydrate-debranching, and pectin-acting enzymes—all of which indicate that removal of deconstruction inhibitors is important for observed undiminished solubilization. Our work provides insights into the mechanisms by which natural microbiomes effectively deconstruct and utilize lignocellulose at high solids loadings, informing the future development of defined cultures for efficient bioconversion.
Deployment expectations of multi-gigatonne scale carbon removal could have adverse impacts on Asia’s energy-water-land nexus
Abstract Existing studies indicate that future global carbon dioxide (CO 2 ) removal (CDR) efforts could largely be concentrated in Asia. However, there is limited understanding of how individual Asian countries and regions will respond to varying and uncertain scales of future CDR concerning their energy-land-water system. We address this gap by modeling various levels of CDR-reliant pathways under climate change ambitions in Asia. We find that high CDR reliance leads to residual fossil fuel and industry emissions of about 8 Gigatonnes CO 2 yr −1 (GtCO 2 yr −1 ) by 2050, compared to less than 1 GtCO 2 yr −1 under moderate-to-low CDR reliance. Moreover, expectations of multi-gigatonne CDR could delay the achievement of domestic net zero CO 2 emissions for several Asian countries and regions, and lead to higher land allocation and fertilizer demand for bioenergy crop cultivation. Here, we show that Asian countries and regions should prioritize emission reduction strategies while capitalizing on the advantages of carbon removal when it is most viable.
Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements
Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnostics, yet prior implementations still rely on such components. Here we present the first experimental implementation and validation of the GPSR methodology, realized by the use of standard accelerator elements including accelerating cavities and dipole magnets, to achieve complete 6-dimensional phase space reconstruction. Through simulations and experiments at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility, we successfully reconstruct complex, nonlinear beam structures. Furthermore, we validate the methodology by predicting independent downstream measurements excluded from training, revealing the reconstruction closely resembling ground truth. This advancement establishes a pathway for predictive diagnostics across beamline segments while reducing hardware requirements and expanding applicability to various accelerator facilities.
Deployable high-fidelity metagenome binning at scale with QuickBin
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Metal production requirements for rapid photovoltaics deployment
To supply even a small share of electricity in 2030, several thin-film photovoltaic (PV) technologies, such as CdTe shown here, would require metal production growth rates that exceed those observed historically. In contrast, crystalline silicon could supply a majority electricity share without requiring unprecedented metal growth rates.
Irradiance and temperature considerations in the design and deployment of high annual energy yield perovskite/CIGS tandems
The annual energy yields for metal halide perovskite/copper indium gallium diselenide (CIGS) tandem photovoltaics have been calculated for 16 different bandgap combinations, using both 2 terminal and 4 terminal device designs, with fixed-tilt mounting as well as 1- and 2-axis tracking. Measured complex index of refraction data were used for the materials comprising the devices, and hourly irradiance data were extracted from Version 3 of the National Solar Radiation Database. Simulations were performed for Toledo OH, Golden CO, Phoenix AZ, and New Orleans LA, and the effect of local temperature variation was also considered. The combination of irradiance and temperature variations throughout the year cause different devices to be optimal at different times of the year. Interestingly, devices constructed to maximize AM1.5 photoconversion efficiency do not necessarily maximize the annual energy yield. A detailed analysis of the monthly energy yields at the different locations reveals the interplay between the changing light and temperature conditions. Over the course of the year these effects average to some degree so that annual energy yields that are close to the maximum possible value can be achieved by several different tandem device designs. The conclusions are valid for devices made with relatively thin perovskite layers, such as those used in champion efficiency devices. When the perovskite layers are thicker, however, the device is less tolerant to variation. Our results show that close matching of bandgap pairs is not essential for the fabrication of high-performance tandems. These findings should allow manufacturing efforts to proceed without the need for precise compositional control during formation of the absorber layers.
Material and system development needs for widespread deployment of hydroxide exchange membrane fuel cells in light-duty vehicles
We have developed a comprehensive system model for hydroxide exchange membrane fuel cell (HEMFC)-based light-duty vehicles which allows us to determine the material and system developments needed to enable affordable HEMFC-based cars for the mass market.
Addressing adoption barriers and accelerating market deployment of new technologies
Adoption of emerging technologies can be difficult, but can be improved by using a framework that incorporates the relative environmental, social, and economic performance of that new technology, such as for enzymatic recycling of a polymer.
Comparing the Net Cost of CSP-TES to PV Deployed with Battery Storage
Concentrated solar power with thermal energy storage (CSP-TES) is a unique source of renewable energy in that its energy can be shifted over time and it can provide the electricity system with dependable generation capacity. In this study, we provide a framework to determine if the benefits of CSP-TES (shiftable energy and the ability to provide firm capacity) exceed the benefits of PV and firm capacity sources such as long-duration battery storage or conventional natural gas combustion turbines (CTs). The results of this study using current capital cost estimates indicate that a combination of PV and conventional gas CTs provides a lower net cost compared to CSP-TES and PV with batteries. Some configurations of CSP-TES have a lower net cost than PV with batteries for even the lowest battery cost estimate. Using projected capital cost targets, however, some configurations of CSP-TES have a lower net cost than PV with either option for even the lowest battery cost estimate. The net cost of CSP-TES varies with configuration, and lower solar multiples coupled with less storage are more attractive at current cost levels, due to high component costs. However, higher solar multiples show a lower net cost using projected future costs for heliostats and thermal storage materials.
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
Optimal deployment of resources for maximizing impact in spreading processes
Significance Spreading processes play an increasingly important role in marketing, opinion setting, and epidemic modeling. Most existing algorithms for optimal resource allocation in spreading processes are based on topological characteristics of the underlying network and aim to maximize impact at infinite time. Clearly, realistic and efficient real-time allocation policies should consider both network properties and details of the dynamics; additionally, control may be applied only to a restricted set of accessible nodes, and impact should be maximized in a limited time window. We introduce a probabilistic targeting framework that incorporates the dynamics and encompasses previously considered optimization formulations. It is based on a scalable dynamic message-passing approach that allows for the solution of large real-world network instances.
Near-term deployment of carbon capture and sequestration from biorefineries in the United States
Significance Carbon dioxide removal through the permanent sequestration of biogenic CO 2 is a critical technique for climate change mitigation, but most bioenergy with carbon capture and sequestration (CCS) technologies are technically immature or commercially unavailable. In contrast, examples of CCS of biogenic CO 2 resulting from fermentation emissions already exist at scale. Here, we evaluate low-cost, commercially ready sequestration opportunities for existing biorefineries in the United States. We find that existing and proposed financial incentives suggest a substantial near-term opportunity to catalyze the growth of CCS infrastructure, improve the impacts of conventional biofuels, support development of carbon-negative biofuels, and satisfy low-carbon fuel policies.
Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code
Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.
New In-Situ and Operando Facilities for Catalysis Science at NSLS-II: The Deployment of Real-Time, Chemical, and Structure-Sensitive X-ray Probes
The start of operations at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory heralded a new beginning for photon-science-based research capabilities in catalysis. This new facility builds on many years of pioneering work that was conducted at the NSLS synergistically by many scientists from academia, government labs, and industry. Over several decades, numerous discoveries in catalysis were driven through the emergence of an arsenal of tools at the NSLS that exploited the power of emerging X-ray methods encompassing scattering, spectroscopy, and imaging. Thus, in-situ and operando methodologies that coupled reactor environments directly with advanced analytical techniques paved a rapid path towards realizing an improved fundamental understanding at the frontiers of chemical science challenges of the day.
An Onshore Deployment of Advanced Dual-Doppler Radar for Wind Energy Applications
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