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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Limited potential of irrigation to prevent potato yield losses in Germany under climate change

Climate models project higher temperatures and a substantial net water deficit during the summer months over Germany until the end of the 21st century. The agricultural sector is particularly vulnerable to changing weather and climate conditions. In this study, we examine the role of irrigation for potato yields as a climate change adaptation measure in Northeast Lower Saxony (Germany). The region represents the largest irrigated area in Germany and is one of the main growing areas of potatoes. We develop a statistical multivariate regression model to investigate whether regional climate and irrigation data at the monthly and county level are suitable to describe potato yield variability from 1978 to 2018. Irrigation is estimated based on the climatic water balance and crop water demand. These estimates are calibrated against irrigation data from local agencies to account for realistic monthly water withdrawals. We analyze future yield changes based on climate model projections for the 21st century and different irrigation scenarios. We find that the potato yield will decrease significantly by 18% on average until 2050 with climate projection uncertainty of ±6.4% under the high emission scenario RCP 8.5 and without irrigation. Irrigation at current levels could compensate for about 54% of these yield losses. Doubling the amount of irrigation minimizes yield losses by as much as 80%. Under the low (middle) emission scenario RCP 2.6 (RCP 4.5), potato yield are projected to decrease moderately by 4.5% ± 3.4% (5.4% ± 2.8%) with irrigation at current levels. We show that the yield losses mainly originate from higher temperatures during the summer. Our analysis indicates that the capacity of irrigation to minimize yield losses is limited. Strengthening climate change mitigation efforts may be more effective in preventing yield losses. However, the effect of elevated CO 2 might buffer the high yield losses, especially in RCP 8.5. The studies supports farmers and decision makers in assessing future risks in potato yields. It can guide policymakers in allocating economic resources to cope with climate change.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electrocoagulation Combined with Ultrafiltration Membranes as Pretreatment for RO Desalination of Synthetic Cooling Tower Blowdown Water

Electrocoagulation (EC), an electrochemical water treatment process, is commonly used to remove particulate and colloidal matter from water. Here, in this study, we demonstrate that EC, when coupled with membrane filtration, is also capable of removing dissolved species, such as Ca + , Mg 2+ , and SiO x . The removal of such species is important for downstream membrane-based desalination treatment of the water that can suffer from reduced performance due to membrane scaling. Here, we describe how EC can be combined with a low-pressure membrane (LPM) system to offer efficient (and potentially universal) pretreatment for downstream membrane desalination. Synthetic water, simulating cooling tower blowdown (CTBD) with elevated concentrations of hardness and silicates (Ca: 418 ppm, Mg: 63 ppm, SiO 2 : 50 ppm) is treated using EC coupled to ultrafiltration (UF) to remove Mg up to 30 ± 1%, Ca up to 29 ± 1%, and silica up to 99 ± 1%. We evaluated the effectiveness of the EC-UF pretreatment system in reducing downstream RO scaling using thermodynamic modeling to predict the saturation index (SI) at the RO membrane/water interface. An SI value below zero (SI < 0) indicates under-saturated conditions (with respect to a particular mineral) where mineral scaling does not take place, which correlates with improved water recovery. Our findings suggest that the EC-UF pretreatment system was able to increase water recovery by up to 30%, compared to 0% recovery without pretreatment, under optimal conditions (feed solution pH of 7 and an EC charge loading of 1800 C/L). Finally, we conducted an economic analysis showing that implementing an EC-UF system for CTBD water could yield a cost benefit of up to $\$$14.13 per m 3 compared to direct brine disposal.

cooling tower blowdown↗
DOE OSTI2024

Practical and Optimal Sequential Bayesian Experimental Design for Complex Systems Incorporating Human Experimenter Preferences (Final Scientific/Technical Report)

Experiments are indispensable for developing models of complex systems. Carefully designed experiments can provide substantial savings for these expensive data-acquisition opportunities. However, designs based on heuristics are often suboptimal for systems with multiphysics, nonlinear dynamics, and uncertain and noisy environments. Optimal experimental design, while leveraging predictive models, seeks to systematically quantify and maximize the value of experiments. In this project, we focused on the design of multiple experiments, where current approaches are largely suboptimal: batch-design does not adapt to new data acquired during the experiment campaign (no feedback), and greedy/myopic design ignores future dynamics and consequences (no lookahead). We developed the mathematical framework and computational methods for sequential optimal experimental design (sOED) for complex systems. We enabled tractable model-based sOED in a rigorous manner through novel algorithms based on reinforcement learning, and investigated the effects of human experimenters on the design process. Our methods are fully Bayesian, able to quantify and update uncertainty in a principled manner. The traits aimed by our approach—mathematical rigor and optimality, human effects and uncertainty quantification, computational practicality—are crucial for elevating the standards of artificial intelligence (AI) to support decision-making in scientific domains, and contribute toward trust and realistic adoption of AI in experimental design practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Informing climate adaptation strategies using ecological simulation models and spatial decision support tools

Introduction: Forest landscapes offer resources and ecosystem services that are vital to the social, economic, and cultural well-being of human communities, but managing for these provisions can require socially and ecologically relevant trade-offs. We designed a spatial decision support model to reveal trade-offs and synergies between ecosystem services in a large eastern Cascade Mountain landscape in Washington State, USA. Methods: We used process-based forest landscape (LANDIS-II) and hydrology (DHSVM) models to compare outcomes associated with 100 years of simulated forest and wildfire dynamics for two management scenarios, Wildfire only and Wildfire + Treatments. We then examined the strength and spatial distribution of potential treatment effects and trends in a set of resources and ecosystem services over the simulation period. Results: We found that wildfire area burned increased over time, but some impacts could be mitigated by adaptation treatments. Treatment benefits were not limited to treated areas. Interestingly, we observed neighborhood benefits where fire spread and severity were reduced not only in treated patches but in adjacent patches and landscapes as well, creating potential synergies among some resource benefits and services. Ordinations provided further evidence for two main kinds of outcomes. Positive ecological effects of treatments were greatest in upper elevation moist and cold forests, while positive benefits to human communities were aligned with drier, low- and mid-elevation forests closer to the wildland urban interface. Discussion: Our results contribute to improved understanding of synergies and tradeoffs linked to adaptation and restoration efforts in fire-prone forests and can be used to inform management aimed at rebuilding resilient, climate-adapted landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Greening Future Elevates Flash Drought Risk in Northern Mid‐to‐High Latitudes

Flash droughts have become a growing concern, as they can emerge rapidly and increase the risk of crop failure. Although past studies have investigated the meteorological drivers and future changes of flash drought, why flash drought is more frequent over humid and vegetated regions remains underexplored. This study delves further into the mechanism by which vegetation regulates flash drought and its future change using observations from multiple data sets and large ensemble simulations from three Earth system models. On an interannual timescale, both observations and simulations show robust increases in flash drought frequency and a higher flash-to-sub-seasonal drought ratio during spring or antecedent conditions with dense vegetation, supporting the important role of vegetation in flash drought occurrence, especially in the northern mid-to-high latitudes. In the latter regions, the large ensemble simulations show robust increases in flash drought (e.g., 67% and 46% increases in Eastern U.S. and North Asia in 2050–2100 relative to 1950–2000 under the high emission scenario), where the growing season is lengthening. Although greening might suggest reduced drought stress, it drives precipitation-soil moisture-evapotranspiration decoupling by increasing evapotranspiration partitioning to transpiration. As transpiration can access deep soil water through the plant root system, its increased portion can weaken the constraints of concurrent precipitation on evapotranspiration, thus accelerating soil moisture depletion under high evaporative demand, driving a slow-to-rapid drought transition. How vegetation regulates flash drought by regulating surface moisture budget is supported by observations and simulations. Although warming supports early planting, agriculture may increasingly be threatened by surging flash drought risk.

Drought↗
DOE OSTI2026

Computer simulation of reaction and transport of core-scale serpentinization of Fe-bearing olivine and evolution of geological hydrogen

We present reactive flow and transport simulations of core-scale serpentinization of forsteritic, Fe-bearing olivine under hydrothermal conditions. The model captures fluid flow, evolving porosity–permeability, and redox-controlled H 2 production in a cylindrical core over 55 days at 245 °C and 37 bar. Key processes include olivine dissolution, precipitation of serpentine and magnetite, and oxidation of Fe2+ to Fe 3+ . Here, results show strong coupling among flow velocity, alteration front propagation, and spatially heterogeneous H 2 generation. Elevated H2 concentrations align with inlet-localized magnetite precipitation, consistent with experiments. Secondary mineral formation reduces porosity and permeability, altering transport pathways. Reactive flow at the fluid–solid interface governs H 2 generation rates and distribution. Dissolved SiO2 promotes serpentine and talc formation by suppressing brucite, while bicarbonate extends reaction duration and moderates surface complexation effects.

Geologic hydrogen↗
DOE OSTI2021

FOXM1-activated SIRT4 inhibits NF-κB signaling and NLRP3 inflammasome to alleviate kidney injury and podocyte pyroptosis in diabetic nephropathy

Highlights: • FOXM1 is downregulated in kidney tissues of patients with DN. • Overexpression of FOXM1 alleviates kidney injury in DN modeled mice. • Overexpression of FOXM1 reduces pyroptosis of HG-treated podocytes. • FOXM1 activates SIRT4 to inhibit the NFκB pathway and the NLRP3 inflammasome. • Downregulation of SIRT4 blocks the protective roles of FOXM1 in podocytes and mice. Forkhead box M1 (FOXM1) has been reported to play a protective role against acute kidney injury by driving tubular regeneration. This study aims to probe the function of FOXM1 in diabetic nephropathy (DN) and the molecules involved. FOXM1 was poorly expressed in DN-diseased kidney tissues. A murine model of DN was established, and podocytes cells (MPC5) were treated with high-glucose (HG) for in vitro studies. FOXM1 overexpression improved kidney function and reduced pathological changes in mice, and it increased the expression of the podocyte marker Nephrin in kidney tissues. In vitro, FOXM1 increased viability and reduced pyroptosis of the HG-treated MPC5 cells, and it elevated the expression of the podocyte marker Nephrin whereas reduced the expression of pyroptosis-related NLRP3 inflammasome and cleaved caspase 1. FOXM1 bound to the promoter of sirtuin 4 (SIRT4) to induce transcriptional activation. Downregulation of SIRT4 blocked the protective roles of FOXM1 both in vivo and in vitro. Phosphorylation of nuclear factor-kappa B (NF-κB) in HG-treated cells was suppressed by FOXM1 but restored after SIRT4 inhibition. In conclusion, this study suggested that FOXM1 transcriptionally activates SIRT4 and inhibits NF-κB signaling and the NLRP3 inflammasome to alleviate kidney injury and podocyte pyroptosis in DN.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

Here, this work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

Machine learning↗
DOE OSTI2021

Radon Kinetics in a Basement Space Measured with Different Devices

Although indoor monitoring of radon and benchmarking of radon measurement devices remain important research topics, few intercomparisons of active radon measurement devices have been performed under realistic conditions, let alone dynamic ones enabling comparison of their transient behavior. Five different radon monitors were therefore placed in a poorly ventilated basement space under three different conditions: 24 h under a steady, elevated radon level, 24 h with fans turned on to produce a radon washout transient, and 9 d with fans turned off for a radon buildup transient. Resulting radon concentrations varied between ~200 and ~2,000 Bq m -3 . Accuracy of the devices were evaluated using root-mean-square error, and ventilation data were fit to first order linear compartmental models. To more accurately model behaviors such as cyclic diurnal variations, the source term corresponding to entry of radon from soil into the basement was considered to be non-constant, as it is likely to vary drastically with both the indoor-outdoor pressure differential and soil concentration variations. Here, the improved radon washout model fit very well with the measurements. Despite a wide variety in list prices, all devices performed similarly during transients and at different radon concentrations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Cyclic Injection Leads to Larger and More Frequent Induced Earthquakes under Volume-Controlled Conditions

As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut-in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. Here, in this work, we use 3D physics-based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.

58 GEOSCIENCES↗
DOE OSTI2021

SECARB-USA: Needs Assessment Framework for Storage Complexes (Task 2.1.b)

A team of SECARB storage experts examined 63 formations at 39 sites that were selected to represent the range of diversity of newly assessed, as well as well-advanced, storage prospects in the SECARB region. We inventoried the data needs triggered by the requirements to obtain a Class VI UIC storage permit, the data needs that results from the requirement to create geocellular fluid flow models to support that permit, and by pragmatic and best practice inputs such as public acceptance, regulatory readiness, and pore space leasing. We anchored both the needs inventory and the processes for and cost of meeting the needs with data from 9 sites in in the SECARB area that have advanced far in characterization. Results show that the total cost of characterization prior to obtaining a permit is convergent, because the permit and modeling requirements drive projects to obtain the same types of data for all cases. The high cost data that control cost are 1) drilling, coring, core-testing, logging, sampling and testing a characterization well and 2) collection of a 3-D seismic survey to map reservoir and confining system properties over the area of the plume or the area of elevated pressure. In 5 of our case study sites we determined that one or both of these costs could be avoided because the needs are met by available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Determination of Mixed Gas Compressibility in Nuclear Fuel Rods

Gas behavior of mixed gas species becomes non-ideal at pressures above atmospheric. Theoretical calculations of the behavior can be calculation intensive and may diverge from actual values depending on the model used and its applicability to the range of interest. Experimental data is lacking in the literature for varied pressure compressibility factors (Z) for most pure gas systems and even more rare for mixed gas species. This work covers the experimental determination of compressibility factors for pure Ar and a 1:1 Xe:He gas mixture. The deviation from ideal at elevated pressures can lead to incorrect results when not accounted for during expansion experiments such as those used when characterizing the pressure in nuclear fuel rods.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗
DOE OSTI2025

Computational Modeling of Water Infiltration From Ditches Into Roadway Embankments

Ditches are built along roadways to convey the stormwater runoff. They collect the runoff from the pavement and adjacent drainage areas. Water can move freely in a ditch away from the road surface, thus making it safer for the vehicular traffic. South Carolina DOT expressed interest in studying infiltration of water from ditches into roadway embankments. As part of the current design criteria, it is recommended that the road subgrades be 1.0 foot above the design event water surface elevation in the ditch. The distance between the water surface and bottom of the subbase is referred to as “freeboard”. The purpose of this requirement is to lower the risk of pavement damage by moving the flow away from the pavement, containing the captured flow, and conveying flow received from adjacent properties to outfalls. With changes in roadway design standards, larger shoulders and flatter cross-slopes have moved ditches further away from the pavement and the subbase. These changes could allow for a lower freeboard standard without reducing safety margins due to the increased lateral distance from the subbase to the ditch. The objective of this study is to evaluate whether the current SCDOT criterion requiring the roadway subgrade to be 1 ft above the design-event ditch water surface can be relaxed under selected geometric and soil conditions, particularly where larger shoulder widths and flatter side slopes increase horizontal separation between ditch water and pavement structure.

42 ENGINEERING↗
DOE OSTI2021

Bimodal particle distributions with increased thermal conductivity for solid particles as heat transfer media and storage materials

Solid particles are being considered in several high temperature thermal energy storage systems and as heat transfer media in a variety of advanced power generation systems, particularly in concentrated solar power plants. The downside of such an approach is the low overall heat transfer coefficients caused by the inherently low thermal conductivity values of the low-cost solid media when coupled to heat exchanger for the power cycle working fluid. Choosing the right particle size distribution, emittance, and material of the solid media can all make a substantial difference in packed bed thermal conductivity. Current research though exclusively focuses on continuous unimodal distributions of particles. Here, in this work, we propose the use of a binary particle system with a bimodal size distribution to significantly increase packed bed thermal conductivity by reducing packed bed porosity. This is the first study related to ceramic solid particle heat transfer that has considered the thermal conductivity of non-unimodal size distributions at room and elevated temperatures. The following study found that for the binary particle system using Carbo particles CP 16/30 – CP 70/140 where the large particle volume fraction was 50% there was an 17–47% increase in packed bed thermal conductivity when compared with a nearly unimodal particle size distribution of CP 16/30 between 50 and 300 °C. Two different porosity and effective thermal conductivity models were studied, with one providing better prediction of porosity but both effective thermal conductivity models providing less predictive capacity. Importantly this approach can have a substantial impact of thermal performance, with little to no impact on the particle cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Elevated temperature nanoscratch of Inconel 617 Superalloy

Inconel 617 superalloy is a main candidate to be used for mechanical and tribo-components in high temperature helium-cooled reactors. Recent findings show that it grows a unique surface oxide, especially under high temperature helium with distinct wear, friction, and contact properties. Here, this study reports the elevated temperature nanoscratch behavior of Inconel 617 and further utilizes it to understand the effect of temperature on contact friction constituent contributors, adhesion and plowing at small scales. Inconel 617 is aged in high temperature helium, and consequently, the total kinetic friction coefficient of the alloy surface oxide is obtained in temperatures ranging from 25 °C to 400 °C. A finite element model is developed and validated based on the experimental results. The model is then utilized along with previously established techniques to determine the adhesion and plowing components of the friction coefficient. At small scale, the experimental results show that with increasing temperature the friction coefficient increases. It was inferred that this increase is mainly due to the increased contribution of plowing friction at high levels of deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantifying the temperature of heated microdevices using scanning thermal probes

Quantifying the temperature of microdevices is critical for probing nanoscale energy transport. Such quantification is often accomplished by integrating resistance thermometers into microdevices. However, such thermometers frequently become structurally unstable and fail due to thermal stresses at elevated temperatures. Here, we show that custom-fabricated scanning thermal probes (STPs) with a sharp tip and an integrated heater/thermometer can accurately measure the temperature of microdevices held at elevated temperatures. This measurement is accomplished by introducing a modulated heat input to the STP after contacting the microdevice with the STP's tip and characterizing the DC and AC components of the STP's temperature. From these measured temperature components, the tip-to-sample thermal resistance and the microdevice surface temperature are deduced simultaneously via a simple lumped-capacitance model. The advances presented here can greatly facilitate temperature measurements of a variety of heated microdevices.

42 ENGINEERING↗
DOE OSTI2020

Surface Cloud Grid Version 2 (SFCCLDGRID2) Value-Added Product: Description of Update"

This document describes the algorithm used for the Surface Cloud Grid Value-Added Product (VAP). This VAP uses as input the 15-min. output from the Shortwave (SW) Flux Analysis VAP (see Long 2001; Long and Ackerman 2000; Long et al. 1999) from the Atmospheric Radiation Measurement (ARM) Climate Reseach Facility (ACRF) Southern Great Plains (SGP) Central Facility and extended facilities. This network of 21 sites is unevenly spaced over northern Oklahoma into southern Kansas, covering an area from 95.5° to 99.5° west longitude and 34.5° to 38.5° north latitude. For research applications such as single-column modeling, an estimate of the cloud and cloud effects distribution over this entire domain is desirable. The Surface Cloud Grid VAP applies a multi-pass weighted sum analytic approximation technique (Caracena 1987), which uses Gaussian weighting and an imposed scale length, to interpolate to a 0.25° by 0.25° lat/long grid over the SGP domain. The output, like the input, includes solar elevation angles of 10° or greater.

97 MATHEMATICS AND COMPUTING↗