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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stimuli-responsive petroleum cement composite with giant expansion and enhanced mechanical properties

Shrinkage, as an inherent property of cement during the curing process, has been a concern in the oil & gas industry for centuries. Therefore, expansive cement has been studied for many decades, primarily through incorporation of expansive additives. Nevertheless, traditional expansive additives are not able to achieve the required expansive ratio or maintain a good mechanical property for the cement composite at extreme underground condition. Accordingly, a new generation of expansive additive, which not only has the required expansion, but also work at the downhole high temperature and high pressure environment, and maintain or even enhance the mechanical property, is highly desired. In this work, a high enthalpy storage shape memory polymer (SMP) as a new class of expansive additive is investigated. It is found that the cement composite achieves 1.4% circumferential expansion by only 6% by weight of SMP additives. The compressive strength and flexural strength are also enhanced at the same time, which is hardly achievable by other expansive additives with the same concertation. Good pumpability is also proved by rheological study. The mechanism controlling the enhanced properties is reveled through morphological study and element mapping analysis at the SMP/cement interface.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Discontinuous Galerkin sparse grids methods for time domain Maxwell’s equations

In this paper, we present a time-explicit sparse grid discontinuous Galerkin method for solving the three-dimensional time-domain Maxwell equations. The conservation properties and convergence rates are established for different choices of numerical fluxes. The convergence rates are proved theoretically and then verified by several numerical examples. Even though our scheme does not preserve the divergence, but by implying the higher order polynomial, one can observe the same convergence rate as numerical solution. Several numerical tests are presented to validate these conclusions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A pseudospectral implicit particle-in-cell method with exact energy and charge conservation

The standard particle-in-cell (PIC) method employs explicit finite-difference (FD) methods (e.g. the leap-frog scheme) for both spatial and temporal integrations. Here, we employ a pseudospectral method for solving the Poisson equation and a fully implicit time integration to achieve exact energy conservation. The advantage of a pseudospectral field solver is its spectral accuracy in solving field solutions. Earlier studies of implicit time integration of PIC FD equations can enforce exact energy exchange between field and particles, resulting in exact energy-conserving schemes. Here, we prove that the exact energy conservation property can be carried over to the pseudospectral scheme. Simultaneously, we provide a solution to ensure a pseudospectral charge continuity equation. We demonstrate the new scheme in a 2D electrostatic PIC code. In conclusion, theoretical results are confirmed via numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A provably stable numerical method for the anisotropic diffusion equation in confined magnetic fields

We present a novel numerical method for solving the anisotropic diffusion equation in magnetic fields confined to a periodic box which is accurate and provably stable. We derive energy estimates of the solution of the continuous initial boundary value problem. A discrete formulation is presented using operator splitting in time with the summation by parts finite difference approximation of spatial derivatives for the perpendicular diffusion operator. Weak penalty procedures are derived for implementing both boundary conditions and parallel diffusion operator obtained by field line tracing. We prove that the fully-discrete approximation is unconditionally stable. Discrete energy estimates are shown to match the continuous energy estimate given the correct choice of penalty parameters. A nonlinear penalty parameter is shown to provide an effective method for tuning the parallel diffusion penalty and significantly minimises rounding errors. Several numerical experiments, using manufactured solutions, the “NIMROD benchmark” problem and a single island problem, are presented to verify numerical accuracy, convergence, and asymptotic preserving properties of the method. Finally, we present a magnetic field with chaotic regions and islands and show the contours of the anisotropic diffusion equation reproduce key features in the field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Removal of Na + and Ca2 + with Prussian blue analogue electrodes for brackish water desalination

Desalination of brackish water sources is critical to addressing the growing global freshwater demand. One promising approach is electrically driven desalination using intercalation electrodes. While intercalation electrodes have been widely researched for energy storage applications, only a small subset of those materials is suitable for desalination. In this work, we report the synthesis, characterization, and in-device testing of three Prussian blue analogue intercalation compounds: copper, manganese, and zinc hexacyanoferrate with formulas K x M[Fe(CN) 6 ] z ·nH 2 O (M = Cu, Mn, Zn). The desalination performance for each of these materials against carbon electrodes is reported for Na + intercalation and for Ca 2+ intercalation using 1000 ppm NaCl and 1000 ppm CaCl 2 feed solutions, respectively. While the copper and manganese analogs showed promising performance for Na + and Ca 2+ intercalation, the zinc compound was unstable and underwent rapid dissolution. Manganese hexacyanoferrate showed the best desalination performance in terms of salt removal capacities and salt removal rates with NaCl while copper hexacyanoferrate performed the best with CaCl 2 . The manganese analog proved to be the most stable intercalation material, retaining 83% and 72% of its salt removal capacity after 280 cycles in NaCl and CaCl 2 feed solutions respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Capture of novel sp 3 hybridized Z-BN by compressing boron nitride nanotubes with small diameter

Experimental synthesis of new sp 3 hybridized carbon/boron nitride structures remains challenging despite that numerous sp 3 structures have been proposed in theory. Here, we showed that compressed multi-walled boron nitride nanotubes (MWBNNTs) and boron nitride peapods (C 60 @BNNTs) with small diameters could transform into a new sp 3 hybridized boron nitride allotrope (Z-BN). This strategy is considered from the topological transition point of view in boron nitride nanotubes upon compression. Due to the increased curvature in compressed small-diameter MWBNNTs, the uncommon 4- and 8-membered rings in Z-BN could be more favorably formed. And the irreversible tube collapse is proved to be a critical factor for the capture of the formed Z-BN, because of the competition between the resilience of tube before collapse and the stress limitation for the lattice stabilization of Z-BN upon decompression. In this case, Z-BN starts to form above 19.0 GPa, which is fully reversible below 45 GPa and finally becomes quenchable at 93.5 GPa. Consequently, this collapse-induced capture of the high-pressure phase could also be extended to other tubular materials for quenching novel sp 3 structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The connection between the chromatic numbers of a hypergraph and its 1-intersection graph

A well known problem from an excellent book of Lovász states that any hypergraph with the property that no pair of hyperedges intersect in exactly one vertex can be properly 2-colored. Motivated by this as well as recent works of Keszegh and of Gyárfás et al. we study the 1-intersection graph of a hypergraph. The 1-intersection graph encodes those pairs of hyperedges in a hypergraph that intersect in exactly one vertex. We prove for k ϵ {2, 4} that all hypergraphs whose 1-intersection graph is k-partite can be properly k-colored.

1-intersection graph of hypergraphs↗
DOE OSTI2024

Biofuel production from palm oil deoxygenation using nickel-molybdenum on zirconia catalyst using glycerol as a hydrogen donor

The growing demand for renewable energy has generated interest in biofuels as alternatives to fossil fuels. Second-generation biofuels, derived from deoxygenating fats and oils, have garnered a higher level of interest from industry and academia due to their potential for direct replacement of diesel and jet fuels. Palm oil, mostly cultivated in Thailand and composed of C16 and C18 fatty acids, is a primary feedstock sought for biofuel production. Palm oil deoxygenation contains several pathways that may or may not require hydrogen gas. This study aimed to produce biofuels in different fuel ranges, such as gasoline, jet fuel, and diesel, through palm oil deoxygenation using glycerol as a hydrogen source. Glycerol, a low-value byproduct, was used as a hydrogen donor, whereas nickel-molybdenum-supported catalysts were chosen for their high efficiency in deoxygenation and cost-effectiveness. The study investigated the impact of reaction time, temperature, and catalyst activation method on palm oil deoxygenation. Catalyst characterization methods, including XRD, SEM, TEM, XPS, FTIR, TGA, and nitrogen-sorption, were employed to understand the role of catalysts’ activity during palm oil upgrading. Findings indicated that alkane hydrocarbons are the major components in liquid products. The presence of excess hydrogen in post reaction gaseous phase proves the hydrogen donation capability of glycerol. Increased reaction time and temperature facilitated the removal of oxygen from palm oil. Nickel-molybdenum on zirconia activated by sulfidation demonstrated higher stability than by reduction activation.

09 BIOMASS FUELS↗
DOE OSTI2020

Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation

Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Inverse reinforcement learning control for building energy management

Reinforcement learning (RL) based control is widely considered a promising approach in building automation and control as it has demonstrated the potential to deal with complex objectives in adjacent domains like robotics, autonomous vehicles, gaming applications, and advertisement recommendations. When applied to any environment, model-free RL learns to improve its control performance over time without requiring a control model, by receiving and then analyzing feedback from the building environment after each control action. Operational objectives are becoming increasingly complex through the simultaneous consideration of thermal comfort, carbon emissions, grid services, and indoor air quality. In this context, conventional rule-based control approaches are proving sub-optimal, mostly heuristic, and inadequate. The model-free and self learning nature of RL appears promising and attractive as it may address the scalability issues associated with advanced control approaches. However, it suffers from long training times and unstable control behavior during the early stages of its learning process, which makes it unsuitable to be applied directly to buildings. This paper addresses these issues using an inverse reinforcement learning approach (IRL), a technique utilized to learn the objective of a controller agent which is considered an expert in its respective domain. Here, we consider a rule-based control as the expert demonstrator. IRL is different from a direct imitation (i.e., direct mapping of states to actions) of control actions as it tries to find the underlying intent of an expert's policy, providing the controller with a better-generalized policy for unseen states or environments with slightly different dynamics. This approach propels the RL controller's policy to levels similar to or better than that of a rule-based policy before it starts learning by interacting with the building. This makes RL for building energy management applications more practical as it prevents the erratic and exploratory behavior in the initial training period, simultaneously speeding up the learning process when compared to applying an untrained RL agent directly to a building environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Parameter identification methods for low-order gray box building energy models: A critical review

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

High‑performance cold‑climate heat pump using tandem compressors with and without vapor injection: Laboratory investigation and field demonstration

Cold‑climate air‑source heat pumps must maintain heating capacity and stable operation at low ambient temperatures for residential applications. This study adapts tandem single-speed compressors—proven in commercial systems —for residential CCHPs, integrating VI and validating performance across laboratory and extreme field conditions to achieve cold-climate targets. That is to achieve ≥75% of the rated heating capacity at −25.0 °C relative to 8.3 °C and heating coefficient of performance (COP) greater than 4.0 at 8.3 °C. Laboratory testing confirmed that both configurations met these targets while operating within acceptable discharge‑temperature limits. Relative to the non‑VI configuration, the VI system provided up to 15% higher heating capacity and up to 9% higher heating COP under identical conditions, yielding a 5.6–9.1% increase in heating seasonal performance factor (HSPF). Field tests in Ohio and Alaska, with minimum outdoor temperatures of −25.0 °C and −34.0 °C, respectively, showed stable operation, minimal supplemental heating (<3.2%), low defrost penalties, and consistent output. Tandem‑compressor architectures proves promising for cold-climate residential heating.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2025

Effects of ambient temperature on electric vehicle range considering battery Performance, powertrain Efficiency, and HVAC load

Here, this study investigates the impact of ambient temperature on the range of electric vehicles (EVs) by analyzing its effects on usable battery energy (UBE), heating, ventilation, and air conditioning (HVAC) energy consumption, and powertrain energy losses. Chassis dynamometer tests within a thermal chamber were conducted under various temperature conditions to investigate these impacts. The results indicate that lower temperatures lead to a decrease in UBE for lithium-ion batteries in EVs. At −18 °C, the UBE exhibited reductions of 4---8 % compared to the UBE at 22 °C. Battery thermal management strategies significantly affected the UBE loss, with different strategies resulting in distinct UBE reductions. HVAC energy consumption, especially for interior heating, proved to be the most dominant variable affecting EV driving range. Larger discrepancies between the HVAC target temperature (22 °C) and the ambient temperature increased HVAC energy usage. The type of HVAC system also influenced energy consumption, where EVs equipped with heat pumps demonstrated lower energy consumption for heating compared to those relying solely on resistance heaters. Ambient temperature also influenced motor energy consumption due to increased frictions, powertrain losses and tire rolling resistance at lower temperatures; consequently, regenerative braking energy decreased in cold conditions. Combining these effects influenced the overall energy consumption and driving range of EVs. At −18 °C, the driving range saw a substantial decrease of up to 60 % compared to 22 °C, while a slight decrease was observed at 35 °C.

Ambient Temperature↗
DOE OSTI2022

Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis

Electrical energy storage systems offer a wide range of options to efficiently manage our power supply infrastructure. Energy and environmental security via renewable resources and energy storage devices will also play a critical role in addressing the supply-demand challenges. A novel energy efficient storage system based on near isothermal compressed air energy storage concept, named as Ground-Level Integrated Diverse Energy Storage (GLIDES) is analyzed for integration with residential and commercial buildings. Additionally, the influence of different configurational aspects on key performance and cost attributes is presented in this study. GLIDES modules in the range of 0.5 kWh–15 kWh configurations were considered for residential buildings whereas 25 kW h–500 kW h modules were considered for commercial buildings. Detailed cost and performance analysis of the GLIDES module proved them to be an effective resource in lowering the peak energy demand and corresponding utility bills.

25 ENERGY STORAGE↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2026

On the roles of welding residual stresses in determination of fracture toughness in austenitic stainless steel SUS 304 pipeline girth welds

Welding residual stresses especially the high tensile stresses are proved to have negative impacts on the fatigue and fracture behaviors of welded structures. In this study, a virtual fabrication of test specimens from welding process to specimen preparation was carried out by numerical simulation. An austenitic stainless steel multi-pass pipe welding was simulated by transient thermal–mechanical finite element analysis, the residual stresses were then mapped into the test specimen to evaluate fracture toughness. The findings in this study confirmed that, residual stress can be high in a sub-sized compact tensile specimen, which may accelerate or hinder the crack propagation during actual fatigue and fracture tests as reported in recent years. The influence of the cutting location and orientation of the specimen on fracture performance was investigated systematically to provide a fundamental understanding of welding residual stress and necessary insights into the specimen preparation procedure. Considering the limitation of measuring techniques and the complexity of the stress distribution, the developed numerical model can be a very useful tool to elucidate the stress evolution and quantify the effect of remaining welding stress on fracture toughness.

Fracture behavior↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗