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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Challenges and Development of Tin-Based Anode with High Volumetric Capacity for Li-Ion Batteries

Abstract The ever-increasing energy density needs for the mass deployment of electric vehicles bring challenges to batteries. Graphitic carbon must be replaced with a higher-capacity material for any significant advancement in the energy storage capability. Sn-based materials are strong candidates as the anode for the next-generation lithium-ion batteries due to their higher volumetric capacity and relatively low working potential. However, the volume change of Sn upon the Li insertion and extraction process results in a rapid deterioration in the capacity on cycling. Substantial effort has been made in the development of Sn-based materials. A SnCo alloy has been used, but is not economically viable. To minimize the use of Co, a series of Sn–Fe–C, Sn y Fe, Sn–C composites with excellent capacity retention and rate capability has been investigated. They show the proof of principle that alloys can achieve Coulombic efficiency of over 99.95% after the first few cycles. However, the initial Coulombic efficiency needs improvement. The development and application of tin-based materials in LIBs also provide useful guidelines for sodium-ion batteries, potassium-ion batteries, magnesium-ion batteries and calcium-ion batteries. Graphic Abstract

25 ENERGY STORAGE↗
DOE OSTI2020

Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management

Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Using on-line and near real-time monitoring with spectroscopy, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF reprocessing streams and LNW since the data can be collected non-destructively and analyzed nearly instantly. It can accurately quantify and qualify nuclear material in near-real time which can help to know the immediate UNF reprocessing stream and LNW conditions and detect the diversion of materials. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. Furthermore, the future development of spectroscopy based on-line monitoring is also discussed in this manuscript.

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

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
DOE OSTI2024

System design and analysis of thermal power dispatch systems for boiling water reactors

Nuclear power plants are crucial to meeting net zero emission goals and achieving energy sustainability. Integrating these plants with clean energy technologies such as high-temperature steam electrolysis (HTSE) may improve the efficiency and economic competitiveness of these plants. Here, the current study investigates the design and operation of a thermal power dispatch (TPD) system for coupling boiling water reactors (BWRs) to HTSE plants. The TPD system extracts a portion of the steam from the reactor’s main steam line and transfers its thermal energy to an HTSE plant through a power transport loop. A TPD system for 5 % steam extraction has been designed and the system performance during steady and transient operations has been analyzed. The TPD system dispatched a total of 197 MW thermal energy to the HTSE plant under nominal design conditions. Saturated steam at 7.17 MPa from the BWR plant was condensed and subcooled to a temperature of 168 °C, while a mass flow rate of 91.1 kg/s of superheated steam was dispatched to the HTSE plant. Furthermore, the system performance during transient operation showed a continuous transition from the initial hot standby mode to the nominal power dispatch level. The transient simulation results emphasized the importance of investigating component level performance for the TPD system design. The current results will guide future works on the development of integrated energy systems for hydrogen production or process heat applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Differentiable hybrid neural network approach for enhancing reactor dynamics simulations

Reactor dynamics simulations provide essential insights into the time-dependent behavior of nuclear reactors under various operating conditions. However, high-fidelity simulations can be computationally intensive, requiring significant computational resources. Here, to address this challenge, this study employs a differentiable hybrid model that utilizes neural networks as a corrector to enhance the performance of a low-fidelity simulation, aligning its predictions with those of a high-fidelity simulation. Low-fidelity and high-fidelity simulations were obtained by adjusting the mesh size in the System Dynamics Analysis Tool. The differentiable hybrid model was trained in two approaches: time-step-wise and sequence-wise. It was then applied to simulate various transients in a molten salt reactor. Its performance was evaluated by comparing its responses to transients against those of the high-fidelity simulation. An additional approach was performed using a data-driven model to correct the low-fidelity simulation. In comparison, the differentiable hybrid model showed significant improvements in transient prediction, effectively addressing the limitations of the low-fidelity simulations. The results highlighted the robustness of the differentiable hybrid model in both training approaches. It delivered simulations that were at least 3.8 times faster than high-fidelity models. In the time-step-wise approach, it achieved at least a 39% improvement in accuracy. In the sequence-wise approach, it showed at least an 81% accuracy improvement over the full transient. This approach offers a promising path for improving computational efficiency without compromising accuracy in nuclear reactor simulations, making it suitable for real-time digital twin applications.

42 - ENGINEERING↗
DOE OSTI2021

Experimental validation of an organic rankine-vapor compression cooling cycle using low GWP refrigerant R1234ze(E)

There is a significant global opportunity to capture and utilize low grade waste heat to reduce fossil fuel consumption, greenhouse gas emissions, and improve energy efficiency across a wide range of industries. In this work, an advanced type of thermally activated cooling system, an organic Rankine-vapor compression cycle (ORVC) with novel heat integration strategies, was designed and tested at a relevant scale for industrial waste heat recovery (300 kW th cooling capacity). The ORVC linked an organic Rankine power cycle and a vapor compression cooling cycle using a turbine and compressor that shared a single shaft. The ORVC test facility absorbed waste heat from a liquid stream at 91 °C to simulate engine coolant in diesel generator sets, rejected heat to a glycol stream at 30 °C, and generated chilled water at 7 °C. A cooling capacity of 264 kW ± 3.5 kW was experimentally validated with a COP of 0.56 ± 0.01 during steady-state operation at the design temperatures. The thermal efficiency, accounting for pump work, of the Rankine cycle was 7.7% ± 0.22% and the COP of the vapor compression cycle was 5.25 ± 0.09. The centrifugal turbo-compressor operated at 31.5 kRPM ± 0.3 kRPM, with a turbine and compressor isentropic efficiencies of 76.7% ± 0.90% and 84.8% ± 0.54%, respectively, with near-perfect power transmission between these components. The pressure drop in the piping and heat exchangers were significantly larger than expected which had a detrimental impact on the performance of the ORVC. In addition, the condenser on the cooling cycle could not deliver the subcooling as specified from the design point modeling. Furthermore, the results from the sensitivity analysis showed that the higher condenser glycol outlet temperature had the largest impact on performance, which is consistent with other analytical models in the literature. When the ORVC simulations were updated with experimental values for isentropic efficiencies of the turbomachinery, the thermal COP was 0.66 which represents an estimate of the predicted performance if test facility limitations are overcome.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Distributed model predictive control for coordinated, grid-interactive buildings

Continued focus on reducing carbon emissions and improving energy efficiency requires buildings to become grid-interactive and not just behave as static consumers. A distributed model predictive control (DMPC) algorithm known as Limited-Communication (LC) DMPC is modified to enable grid-interactive buildings. A grid-aggregator subsystem is added that allows for a bulk grid power reference signal to be followed while the individual building subsystems also achieve their local comfort objectives. The LC-DMPC algorithm is applied for the first time to systems with multiple buildings. Adequate power tracking is shown for different simulation scenarios involving heterogeneous buildings, and next steps are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array

To improve the efficiency of wave farms and achieve maximum power generation, the layout of wave energy converters (WECs) in an array needs to be carefully designed so that the hydrodynamic interactions can be positively exploited. For this, the hydrodynamic characteristics of the WEC array in different layouts need to be calculated. However, such calculations using numerical models usually entail significant computational cost, especially for large arrays of WECs. To address the computational challenge, a physics-constrained Gaussian process (GP) model is proposed to replace the original expensive numerical model and predict the hydrodynamic characteristics of the WECs for any array layout. By exploring the relationship between the WEC array (i.e., the input) and different hydrodynamic characteristics (i.e., the output), here we summarize a set of physical constraints/features, including invariance, symmetry, and additivity. This prior knowledge about the input-output relationship is then directly embedded in the constructed GP model through the design of physics-constrained kernels. In particular, a double-sum invariant kernel is first developed to incorporate the invariance and symmetry features, and then an additive kernel is developed to incorporate the additive feature of the problem. The invariant kernel and the additive kernel are then integrated to construct the physics-constrained GP model. Compared to the standard GP model, the proposed physics-constrained GP models require less training data to achieve the desired accuracy in predicting the hydrodynamic characteristics and are also less vulnerable to the curse of dimensionality (i.e., good scalability for large arrays) due to the use of an additive kernel. The efficiency, accuracy, and scalability of the proposed approach are demonstrated through an application to predict the hydrodynamic characteristics for WEC arrays of different sizes and layouts.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

A novel shallow bore ground heat exchanger for ground source heat pump applications—Model development and validation

A novel ground heat exchanger design, the Underground Thermal Battery (UTB), was proposed as a low-cost alternative to the conventional vertical bore ground heat exchanger (VBGHE) for the application of ground source heat pumps (GSHPs). The UTB is designed to be installed in a shallow borehole (less than 6 m deep) and thus could cost much less than the conventional VBGHE, which usually is installed in vertical bores 60 m deep. By utilizing natural convection of water and phase change materials, the UTB tempers its temperature change in response to thermal loads, which helps improve the efficiency of GSHPs. A one-dimensional (1D) model of the UTB has been developed and validated with the measured performance data of a small-scale UTB, as well as the simulation results of a more detailed three dimensional (3D) numerical model, which accounts for both heat transfer and fluid dynamics in the UTB. Further, this 1D model is computationally much more efficient than the 3D model and thus can simulate the annual performance of the UTB with acceptable computation time. The 1D model has been used to evaluate the performance of the UTB so that it can be compared with the conventional VBGHE.

42 ENGINEERING↗
DOE OSTI2020

Ceramic encapsulated metal phase change material for high temperature thermal energy storage

Thermal energy storage (TES) is a broad-based technology for reducing CO₂ emissions and advancing concentrating solar, fossil, and nuclear power through improvements in efficiency and economics. Phase change materials (PCMs) are of interest as TES media because of their ability to store large amounts of heat in relatively small volumes. The volume expansion during a phase change, typically between a solid and liquid, can cause breakage of protective coatings. This paper reports on the fabrication of a ceramic encapsulated metal (CEM) high temperature TES technology using a rotary calcining furnace and a fluidized bed chemical vapor deposition coating technique. Aluminum beads were chosen as the PCM because Al has a high melting point (660 °C), low cost, high heat of fusion, and an ability to form a thin, strong alumina layer capable of supporting the Al melt for subsequent processing. Quite remarkably, this study shows that 1 mm diameter Al can be fluidized up to at least 1500 °C in an appropriate atmosphere while maintaining a spheroid geometry. This allowed for producing a first of a kind CEM whereby Al particles were encapsulated in pyro-carbon (PyC) and high purity, dense chemical vapor deposited SiC. The CEM with a PyC only coating was exposed to thermal cycling to test the performance with a differential scanning calorimeter; the melting point and latent heat were measured to be 648.4 ± 2.8 °C and 293.3 ± 6.2 J/g respectively. It was demonstrated that the CEM design is possible to produce, laying the foundation for manufacturing of high temperature, tunable, TES media.

25 ENERGY STORAGE↗
DOE OSTI2023

Thermal hydraulic characteristics of liquid sodium and supercritical CO 2 in a diffusion bonded heat exchanger

Compact heat exchangers are being examined for use in advanced, high-temperature nuclear or concentrated solar power plants to improve thermal efficiencies and reduce capital costs. At the time of this work, no experimental investigation of liquid sodium in the popular diffusion-bonded heat exchanger design had been performed. Herein this paper describes the thermal-hydraulic and overall operations performance observed during testing of a 316/L stainless steel “zig-zag” microchannel heat exchanger operating between a (0.5 kg/s) sodium loop and a (0.3 kg/s) recuperated supercritical CO 2 system with outlet conditions up to 550 °C at 16 MPa. Observed pressure drop is presented in the form of an effective friction factor correlation which aligns closely with recent experimental data. The thermal performance of this heat exchanger, measured by its overall conductance under various flow conditions, was decomposed to examine the independent convection behaviors of both the CO 2 and sodium streams. These results are presented in the form of Nusselt correlations fitted to the experimental data. The sodium heat transfer correlation aligns closely with the widely accepted Lubarsky-Kaufman correlation, while the CO 2 heat transfer correlation lies between previous correlations developed by computational and experimental methods. This was the first long-duration use of sodium in a microchannels (~1.8 mm hydraulic diameter) heat exchanger. The use of an oxide control system enabled trouble-free operation of the heat exchanger for over 150 h of high-temperature operation. This first-of-a-kind demonstration using liquid sodium and supercritical carbon dioxide shows great promise for further application of this technology in advanced nuclear or solar power plants.

42 ENGINEERING↗
DOE OSTI2023

Upgrade of the mechanically pumped CO 2 two-phase cooling system for the alpha magnetic spectrometer on the international space station

This study describes the successful upgrade of a mechanically pumped CO 2 two-phase cooling system in space by designing a new pump module for the Alpha Magnetic Spectrometer-02 on the International Space Station. Key factors for mission success are emphasized, including achieving high coolant filling accuracy within 10% of the target and maintaining system stability within ±1 °C. The impact of adding radiators to improve cooling efficiency is examined, and it is found that operating multi-radiators out-of-phase does not significantly affect system reliability. The centrifugal pump design is shown to allow for better lubricant circulation, while the in-house designed controller incorporates protective measures to prevent cavitation, overheating, and over-current. Further, this research advances the understanding of circulation loop systems and their upgrades in space and demonstrates the potential for extending the lifetime of space-borne mechanically pumped two-phase cooling systems.

42 ENGINEERING↗
DOE OSTI2026

Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage

Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Electrochemical Characterization of Photo-Driven Hole-Scavenging by Cadmium Sulfide Quantum Dot-Nitrogenase Biohybrid Complexes

Light-driven biohybrid systems that couple semiconductor nanocrystals with enzymes offer a promising strategy for solar-to-chemical energy-conserving reduction reactions, yet are often limited by inefficient hole scavenging. Hole scavenging is critical for maintaining charge separation in the light-absorbing electron donor molecule and, thus, for sustaining catalytic turnover, making quantification essential to improving system efficiency. Here, we introduce a photoelectrochemical approach for real-time monitoring of hole scavenging in cadmium sulfide (CdS) quantum dot (QD)-nitrogenase MoFe protein biohybrids that catalyze the reduction of H+ and N2. Using hydroquinone (HQ) as the sacrificial electron donor (SED), oxidation of HQ to benzoquinone (BQ) by photogenerated valence-band holes is coupled to the electrochemical reduction of BQ at an electrode, enabling quantification of the hole-scavenging reaction via chronoamperometry. This approach provides a continuous, real-time readout of charge-transfer dynamics under illumination. Systematic variation of light intensity and donor concentration reveals that hole scavenging scales with photon flux and exhibits a non-linear dependence on SED concentration, with an optimum at intermediate HQ concentrations. These results establish chronoamperometry as a powerful tool for probing photoinduced charge transfer, advancing the quantitative understanding of hole-scavenging in biohybrid systems, and offering a generalizable framework for optimizing solar-driven biocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Approaches to investigate the role of chelation in the corrosivity of biomass-derived oils

The need to provide the U.S. market with a renewable liquid fuel energy source from a non-food feedstock stream has gained considerable traction due to benefits such as improved energy efficiency, reduced environmental impacts, and enhanced national security. Practical achievement of these goals via biomass and bio-waste utilization involves production of liquid intermediates containing corrosive, reactive species like carboxylic acids, ketones, aldehydes, and hydroxyaldehydes. Such mixtures challenge materials of containment, processing, and transport. It is widely recognized that the smaller organic acids, such as acetic and formic, are corrosive and can remove protective surface oxides on alloys used in bio-oil processing infrastructure, and ketones can swell sealing polymers. However, literature shows, and findings herein confirm, larger carboxylic acids and bidentate alcohols are present. This highlights the potential for synergistic, detrimental effects of constituents in bio-oil corrosion, including direct reactivity of small acids compounded with the possibility of mobilization of protective metal oxide layers via chelation by larger acids and oxygenates. The question of whether species beyond small acids can significantly contribute to corrosion requires analytical approaches previously not applied to bio-oil corrosion studies and certainly not previously applied corroboratively. As such, this work introduces a combination of optical, mass spectral, and electrochemical impedance spectroscopies with an incubation approach to study metal mobilization, to facilitate elucidating chelation's role in bio-oil corrosive pathways. To enable systematic study of these oxygenates' material compatibility individually and in combination, a model matrix of bio-oil constituents was also developed based on identification of key components of real bio-oils.

09 BIOMASS FUELS↗
DOE OSTI2024

Mapping use cases and dataset needs for benchmarking buildings data

A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Accelerating geostatistical modeling using geostatistics-informed machine Learning

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.

58 GEOSCIENCES↗