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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Energy storage in combined gas-electric energy transitions models: The case of California

California’s vision for a net-zero future by 2045 relies heavily on variable renewable energy systems. Thus, energy storage - particularly long-duration storage - could play a fundamental role in reliably supplying low-carbon electricity. We study energy storage using the BRIDGES model, a combined gas-electric capacity expansion model for California across multiple investment periods (2025-2045), modeled with progressively decreasing carbon emission targets to a zero emissions by 2045. This least-cost optimization model includes renewable gas production via power-to-gas, long-term storage of energy in gaseous form, electric energy storage such as through batteries and hydrogen storage, and renewable energy generation, all with capacity tracking and investment. Multiple scenarios are evaluated to examine the sensitivity of the optimal storage portfolio to system-level and sector-level parameters. The scenario results show that all electric energy storage systems - which vary in storage duration - are deployed and required in a net-zero California in 2045, amounting to around 75 GW of storage capacity. Lithium ion systems make up approximately 80% of this power capacity and supply most short-run storage needs. Hydrogen storage - in the form of a power-to-gas-to-power system - emerges as a replacement to conventional natural gas storage, comprising most of the total energy storage capacity (~ 4 TWh). This capacity is less than 5% of the current natural gas storage capacity (94 TWh), indicating sufficient room for repurposing part of the gas infrastructure. A demand-side sensitivity analysis proves that higher electricity demand correlates with more builds of Li-ion batteries, while higher industrial heat demand leads to more builds of long-duration storage systems in a net-zero economy. Furthermore, power-to-gas systems satisfy part of the industrial heat demand by locally supplying renewable gas, which overtakes the traditional centralized gas storage and transfers through pipelines, casting significant doubts on the future of the large-scale gas infrastructure.

03 NATURAL GAS↗
DOE OSTI2024

Simulation of gas mixture dynamics in a pipeline network using explicit staggered-grid discretization

Here we develop an explicit staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge boundary values that are more complex than for a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Hybrid data-driven cement-stabilized soil design: An integration of machine learning, multi-objective optimization, and life cycle assessment

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial cost function, served as the objective function in a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with final mix selection guided by the entropy-weighted TOPSIS method. Validation through a case study produced mix designs offering superior strength-cost trade-offs, with the optimal mix achieving 2243.2 kPa unconfined compressive strength and a 16.07 % reduction in carbon emissions compared to the highest-cost design. In conclusion, this study offers a sustainable, scalable approach to soil stabilization and supports informed decision-making in construction.

Life cycle assessment↗
DOE OSTI2022

Long term performance of pilot methanogenic digester filled with seashell wastes treating slaughterhouse wastes: Biogas production and environmental impact

High ammonia and organic acids contents of decomposed slaughterhouse waste (SHW) often result in the severe inhibition or even failure of the anaerobic digestion (AD). The two-stage AD system can alleviate the ammonia inhibition; however, addition of alkali is often needed to maintain the neutral condition. In this study, seashell wastes containing high percentage of calcium carbonate (CaCO 3 ) were for the first time added in a pilot scale methanogenic digester treating chicken blood waste to retain the microorganisms and provide pH buffers for the substrate. A long-duration performance of this digester was conducted for 400 days under different organic loading rates (OLRs). The results show that methane yields of the digester were 244.8 and 224.9 mL g –1 COD added at OLR of 0.4 and 0.6 g COD L –1 d –1 , respectively, higher than the results of previous studies treating the similar feedstock. An AD system treating blood wastes of a typical poultry slaughterhouse was also proposed for which energy balance and carbon footprint analyses were conducted. The energy production of 0.35 MJ kg –1 blood and carbon emissions reduction of 0.05 kg CO 2 -eq kg –1 blood can be achieved from treating blood waste of the broiler chicken. Finally, this study reveals that using seashell wastes in an AD treating SHW can facilitate the reuse of aquaculture wastes and improve the sustainability of meat industry.

09 BIOMASS FUELS↗
DOE OSTI2023

Coal-based graphene oxide-like materials: A comprehensive review

Coal is the most abundant energy and hydrocarbon resource in the United States which has been primarily used for power generation. With the increasing retirement of coal power plants and transition to a low-carbon emission economy, the future use of coal will likely shift to a feedstock source for production of valuable materials for different applications. In this review, the latest research reported on production of graphene-like materials in a powder form from coal is compiled and discussed. Here, this review includes: 1) brief background information on graphene and related materials (GRM); 2) relevant material, resource, economic, and socioeconomic information regarding suitability of coal as a potential feedstock for production of GRM; 3) a summary of common approaches for coal upgrading for preparation of more suitable precursors for production of GRM; 4) a compilation and analysis of the reported routes and methods for preparation of GRM from coal and coal derivatives; and 5) reported potential applications of coal-based GRM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sr 2 MnO 4 as a reactive CO 2 sorbent for sorption-enhanced steam reforming of biogas to green hydrogen

Sorption-enhanced steam biogas reforming is an attractive approach for hydrogen production from renewable resources, with the performance of the CO 2 sorbents being a critical factor. In this study, Sr 2 MnO 4 was investigated as a redox-activated CO 2 sorbent for sustainable hydrogen production from biogas. The Sr 2 MnO 4 sorbents exhibited a CO 2 sorption capacity of over 26 g per 100 g of sorbents, along with excellent cyclic stability in thermogravimetric analysis. Complete regeneration of the sorbent was achieved with a relatively small temperature swing (100 °C). Fixed-bed reactor experiments further demonstrated the application of Sr 2 MnO 4 sorbents in sorption-enhanced steam biogas reforming. Biogas simulants with varying CO 2 contents were converted to ~94 vol% H 2 before CO 2 breakthrough. Stable CO 2 capacity and hydrogen production were maintained over 20 cycles. In addition, optimization of the regeneration duration enabled the generation of highly pure CO 2 and more efficient use of O 2 . These results support the feasibility of biogas-to‑hydrogen conversion with net-negative carbon emissions through integration with CO 2 capture and sequestration.

09 BIOMASS FUELS↗
DOE OSTI2024

Decarbonizing nitrogen fertilizer production via the electrochemical nitrogen oxidation reaction

Nitric acid is an important commodity chemical with extensive applications in both agricultural and industrial sectors. However, current production methods involve a combination of the Haber–Bosch and Ostwald processes, which are both energy and carbon emission intensive. The electrochemical nitrogen oxidation reaction (NOR) to produce nitric acid or nitrates shows great potential as an environmentally friendly method for producing fertilizers under mild conditions. The key to progress in this field lies in understanding the fundamental mechanistic insights and establishing robust experimental methods, which is essential for the efficient design and synthesis of electrocatalysts for NOR. Additionally, poor gas mass transport in conventional electrochemical reactors at present lead to lower NOR activity, thereby limiting the progress in this field. In this work, we present a synergistic computational and experimental approach to map out the potential chemical and electrochemical steps and determine the energetics on PtO 2 catalyst to gain mechanistic insights into NOR. Here, this study marks the first attempt to perform NOR in a vapor-fed reactor designed using advanced (additive) manufacturing. The vapor-fed reactor significantly improved the N 2 mass transport to the catalyst, allowing us to report the highest rate for nitrate production to date at 3.3 μmol cm -2 h -1 at 2.01 V vs RHE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Low- and intermediate-temperature oxidation of dimethyl ether up to 100 atm in a supercritical pressure jet-stirred reactor

Understanding the low- and intermediate-temperature oxidation chemistry of oxygenated fuels like dimethyl ether (DME) at high pressure is paramount to the development of advanced engines with low carbon emissions. The supercritical pressure jet-stirred reactor (SP-JSR) recently developed at Princeton provides a new platform for conducting kinetic studies at low and intermediate temperatures at extremely high pressures with a uniform temperature distribution and a short flow residence time. This paper uses the SP-JSR to investigate DME oxidation at equivalence ratios of 0.175, 1.0, and 1.72, for pressures of 10 and 100 atm, and temperatures ranging from 400 to 900 K. The results demonstrate weakened NTC behavior at 100 atm relative to 10 atm due to increased flux through QOOH + O 2 = O 2 QOOH relative to QOOH = 2 CH 2 O + OH at 100 atm. Furthermore, the intermediate-temperature oxidation window is shifted to lower temperatures at 100 atm. The experimental data are compared with several chemical kinetic models from the literature. The existing models are seen to agree quite well with the experimental data at 10 atm. However, the models fail to properly capture the NTC behavior at 100 atm. Reaction pathway analyses indicate that both the low- and intermediate-temperature chemistries are controlled by RO 2 consumption pathways. The reaction rates for several of the important reactions, such as DME + OH = CH 3 OCH 2 + H 2 O, H 2 O 2 (+M) = 2 OH (+M), and 2 HO 2 = 2 OH + O 2 are updated in this work. Furthermore, the updated model improves the predictability for all key species compared to the original model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions

Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.

08 HYDROGEN↗
DOE OSTI2025

Dual-Functional Thermocapacitive Heat Pump with Electrochemical Supercapacitors for Building Thermal Management and Energy Storage

Efficient heating and cooling technologies can help reduce the energy consumption and carbon emissions of buildings. This work explores the use of supercapacitive cells in a multifunctional, liquid-regenerated thermocapacitive heat pump that can provide electrical energy storage in addition to heating and cooling. A proof-of-concept prototype based on eight commercial supercapacitors and using deionized water as a liquid regenerator demonstrated cooling and energy storage capabilities. A peak cooling coefficient of performance (COPc) of 0.27 was achieved at a temperature drop of 0.24 K. The highest measured electrical energy storage density of the cells was 5.93 J cm-3, and the highest cooling power delivered relative to the volume of the cells was 0.58 mW cm-3. This work demonstrates the use of electrochemical energy storage devices in multifunctional equipment for thermal management in buildings.

25 ENERGY STORAGE↗
DOE OSTI2022

Real time side-by-side experimental validation of energy and comfort performance of a zero net energy retrofit package for small commercial buildings

Making buildings zero-net energy (ZNE) is one of the major strategies for achieving carbon emission reduction goals. For this strategy to be successful, it entails a very significant reduction in energy use – 50% or more. In small commercial buildings, however, owners and building management teams usually have limited resources for identifying, analyzing, and procuring appropriate retrofit measures for reducing such use. An approach to overcome this limitation is the development of bundles of energy efficiency measures that can be presented to building owners/operators as a comprehensive package. The research presented in this paper focuses on an experimental evaluation of the impacts of a retrofit package developed for small office buildings in California. Performing this evaluation in a full-scale whole-building integrated systems test facility allowed a side-by-side evaluation in real time, against a reference case, of the impact of the retrofit package not only on energy use but also on visual and thermal comfort. Here, the retrofit package evaluated is comprised of a combination of HVAC, lighting (including daylighting), and plug load measures. The evaluation occurred at different times of the year in order to account for seasonal variations in environmental conditions, including solar angles and weather. The experimental facility allowed testing for two different façade orientations: south and west. Results show that the proposed ZNE retrofit package can save significant amounts of energy for small commercial buildings. During cooling-prevalent periods, total energy savings were 65% for south orientation and 68% for west orientation; during heating-prevalent periods total energy savings were 22% for south orientation and 25% for west orientation. Measurements indicate that the ZNE retrofit package resulted in small but not very significant changes in comfort levels for building occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2022

An optimization framework for the network design of advanced district thermal energy systems

In this work, a topology optimization framework for district thermal energy systems is presented. The framework seeks to address the questions, for a given district, "What is the best subset of buildings to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?" A particle swarm optimization approach is validated to address the selection of the subset of buildings, and a graph theory-based heuristic is validated for selection of the network topology for any candidate subset of buildings. The framework is applied to a prototypical urban district for illustrative purposes. Additionally, modeling of prototypical districts revealed reductions in source energy use intensity for heating and cooling of 21-25% through the use of advanced district energy systems relative to code-compliant, building level systems. The framework identifies solutions with life cycle cost values 14% to 72% lower than that of base case scenarios based on conventional design approaches, depending on the base case scenario selected. Analysis of the search space indicates that topology optimization facilitates reductions in life cycle cost, source energy use intensity, and carbon emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Comparative assessment of new oxygen carrier materials for gas switching reforming of natural gas: Techno-economics assessment, life cycle analysis, and experimental insights

The increasing demand for hydrogen and the CO 2 intensity of natural gas (NG) reforming motivate the development of low-carbon-emission hydrogen production technologies. Gas Switching Reforming (GSR) with integrated CO 2 capture, a technology based on Chemical Looping Reforming (CLR), has been experimentally proven and shows potential for scale-up. In this study, select oxygen carriers (OC) (NiO/Al 2 O 3 , Fe 2 O 3 -CeO 2 /Al 2 O 3 , and magnetite) were tested in methane steam reforming in a fixed bed reactor to determine their relative reactivities under relevant conditions for GSR (800 °C, 7 bar total pressure). Process models were then developed to perform techno-economic analysis (TEA) of GSR for hydrogen production (GSR-H 2 ) and a combined cycle (GSR-CC) in which high-purity H 2 is fired in a gas turbine to produce electricity. Operating at 10 bar and 1100 °C and with the additional recovery steps implemented increased H 2 production by ∼ 30% and improved efficiency relative to prior studies. For GSR-H 2 , the levelized cost of hydrogen (LCOH) is 1.61–1.64 $/kg-H 2 , competitive with a reference SMR case, though operating and maintenance costs are higher due to increased electricity demand. GSR-CC has a significantly higher levelized cost of electricity (LCOE) than its reference NGCC (natural gas combined cycle) plant, suggesting it is less competitive; however, increasing production scale could make it more attractive. Life-cycle results for GSR-H 2 indicate NG consumption drives ∼ 75% of total global warming impacts (∼2.3 kg CO 2 eq/kg H 2 ). An environmental, health, and safety screening suggests iron-based carriers are comparatively safer, whereas NiO may pose greater risks. Overall, GSR-H 2 is a scalable, competitive option for hydrogen production using nickel and non-nickel OC.

03 NATURAL GAS↗
DOE OSTI2025

Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process

Urban form (e.g., building morphology such as height or footprint) can be used to predict environmental footprints, such as energy/water consumption and carbon emissions. Although progress has been made in predicting building characteristics to fill gaps in observation or derive 3-D representations, the relationships between morphology and other variables such as land use and population are poorly understood. Understanding these relationships may enable projections for how cities will evolve with landscapes in the future. A suite of random forest models, the Building Morphology Distribution Land Models (BMDLM), was developed to determine how well building morphology for two distinct statistical measures (central tendency and frequency) can be predicted using land use (e.g., zoning) and population at different resolutions. Clark County, Nevada and Los Angeles County, California are explored as case studies. Generally, 1-km models outperformed 30-m models. Frequency distribution models had the best performance, especially in LA County. Frequency models significantly outperformed spatial autocorrelative models using inverse distance weighting (IDW). BMDLM offers a new take on modeling urban form in which generalized landscape patterns are characterized to understand the influence of population and zoning on urban development, as described by urban scaling theory.

Sturtevant, Jillian [Baylor Univ., Waco, TX (Unite↗
DOE OSTI2023

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

58 GEOSCIENCES↗