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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Interplay Between Climate and Urban Expansion on Building Energy Demand in Morocco

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. In conclusion, the study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

Morocco↗
DOE OSTI2023

Maximizing Wave Energy Converter Power Extraction by Utilizing a Variable Negative Stiffness Magnetic Spring

Complex conjugate impedance matching is a key concept for wave energy converter design. Matching the impedance of the power take-off (PTO) system to the complex conjugate of the wave energy converter's (WEC) impedance ensures efficient transfer of energy from the WEC body motion to electrical power. In low frequency waves, impedance matching often requires a negative PTO stiffness. In this paper, an adjustable stiffness magnetic torsion spring will be presented and modeled to understand its potential to improve WEC performance. The spring has the ability to provide a negative stiffness, allowing the PTO impedance to more closely match the complex conjugate of the WEC impedance at low frequencies. The spring also supports an adjustable stiffness value, meaning it can be tuned according to the incoming wave conditions. The spring's tunability may put less stress on the rest of the PTO system in wave conditions outside its normal operation zone without sacrificing electrical power output. The adjustable magnetic spring's effects are modeled and explored in this paper by examining the resultant average annual electrical power and capacity factor. The study suggests that the tunable magnetic spring has the potential to significantly improve capacity factor while maintaining a high average electrical power.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Multiwavelength Observations of the Blazar PKS 0735+178 in Spatial and Temporal Coincidence with an Astrophysical Neutrino Candidate IceCube-211208A

We report on multiwavelength target-of-opportunity observations of the blazar PKS 0735+178, located 2.°2 away from the best-fit position of the IceCube neutrino event IceCube-211208A detected on 2021 December 8. The source was in a high-flux state in the optical, ultraviolet, X-ray, and GeV γ -ray bands around the time of the neutrino event, exhibiting daily variability in the soft X-ray flux. The X-ray data from Swift-XRT and NuSTAR characterize the transition between the low-energy and high-energy components of the broadband spectral energy distribution (SED), and the γ -ray data from Fermi-LAT, VERITAS, and H.E.S.S. require a spectral cutoff near 100 GeV. Both the X-ray and γ -ray measurements provide strong constraints on the leptonic and hadronic models. We analytically explore a synchrotron self-Compton model, an external Compton model, and a lepto-hadronic model. Models that are entirely based on internal photon fields face serious difficulties in matching the observed SED. The existence of an external photon field in the source would instead explain the observed γ -ray spectral cutoff in both the leptonic and lepto-hadronic models and allow a proton jet power that marginally agrees with the Eddington limit in the lepto-hadronic model. We show a numerical lepto-hadronic model with external target photons that reproduces the observed SED and is reasonably consistent with the neutrino event despite requiring a high jet power.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Design and evaluation of a low-cost sensor node for near-background methane measurement

Abstract. We developed a low-cost methane sensing node incorporating two metal oxide (MOx) sensors (Figaro Engineering TGS2611-E00 and TGS2600), humidity and temperature sensing, data storage, and telemetry. We deployed the prototype sensor alongside a reference methane analyzer at two sites: one outdoors and one indoors. We collected data at each site for several months across a range of environmental conditions (particularly temperature and humidity) and methane levels. We explored calibration models to investigate the performance of our system and its suitability for methane background monitoring and enhancement detection, first selecting a linear regression to fit a sensor baseline response and then fitting methane response by the sensor deviation from baseline. We achieved moderate accuracy in a 2 to 10 ppm methane range compared to data from the reference analyzer (RMSE < 0.6 ppm), but we found that the sensor response varied over time, possibly as a result of changes in non-targeted gas concentrations. We suggest that this cross sensitivity may be responsible for mixed results in previous studies. We discuss the implications of our results for the use of these and similar inexpensive MOx sensors for methane monitoring in the 2 to 10 ppm range.

Furuta, Daniel↗
DOE OSTI2023

Improved Particle Heat Transfer by way of Bimodal Particle Distributions for High Temperature Solar Thermal Energy

High temperature solar thermal facilities are looking to increase operating temperatures through novel heat transfer media, one such being solid particles. These particles operating at high temperatures will require transferring their thermal energy into another working fluid like supercritical carbon dioxide which can be used in advanced power cycles. Achieving high heat transfer between the particles and supercritical carbon dioxide is essential to high efficiency and low-cost operation. Therefore, optimizing the thermal conductivity of these particles is one potential way to ensure high performance. Traditionally, unimodal particle distributions have been employed in high temperature particle solar power plants. However, ambient temperature testing of bimodal particle distributions has revealed a superior thermal conductivity when compared to its unimodal counterpart at the same temperature. This data was obtained by certified, off-the-shelf instruments that can effectively simulate the conditions a particle would be exposed to in a high temperature solar thermal system. Data obtained in this way suggests that the increased thermal conductively imputed by a bimodal particle distribution is significant at working temperatures in solar facilities. Furthermore, the thermal conductivity of these bimodal particle distributions peaks when the best combination of large and small particles is applied. At high temperatures, binary particle distributions are compared to monodispersed distributions of larger particles where heat transfer is more prolific due to the increased surface radiation. Various thermal conductivity, porosity and heat exchanger models are explored in conjunction with data acquired up to 700 C.

Stout, Dallin (ORCID:0009000294586091)↗
DOE OSTI2020

From Atom to Engine: Understanding Fundamental Effects of Structure on Combustion Using Tandem Experiment and Computation

The ability to tie structural features to a fuel candidate’s ignition properties provides a path for rational design of advantaged fuels that facilitate higher-efficiency, lower-emitting combustion in engines. The time evolution of a fuel’s radical population directly correlates with global combustion parameters, and because the generated radicals depend on the initial structure and reaction pathways of the fuel molecule, it follows that the molecular structure of a fuel has a direct impact on this cascade, and therefore salient ignition-tied fuel properties. This work aims to understand the impact of structural features in the context of global combustion properties for direct application to emerging engine technologies. Specifically, we highlight the ability to identify potential fuel blendstocks for specific engine strategies by examining and elucidating the radical cascade tied to ignition. A representative subset of alcohols possessing a variety of structural features (e.g. branching, chain length) was selected for study using a robust combination of experimental and computational methods. The approach begins with identifying short-lived radicals and likely abstraction sites upon pyrolysis in a microreactor coupled with photoionization mass spectrometry for direct detection of product species. In tandem, electronic structure calculations were performed to calculate the relevant potential energy surfaces for fuel pyrolysis as well as the subsequent O2 addition-isomerization reactions, which govern ignition behavior. Furthermore, the theoretical energetic and ro-vibrational data are used to calculate rate constants for direct ignition modeling to explore how structural effects impact NTC behavior. The microreactor data combined with electronic structure theory calculations provide a well-informed picture of the reactions relevant to ignition for each fuel in this study, as well as the most likely radicals generated under engine conditions. This thorough fundamental picture of fuel decomposition and ignition chemistry is used to correlate the observed ignition behavior to molecular structural effects.

advanced biofuels↗
DOE OSTI2022

Making Grid-interactive Efficient Buildings a “Win” for Both Customers and Utilities

Buildings account for more than 70% of U.S. electricity use and at least one-third of U.S. economy-wide CO2 emissions. Changing the timing and overall amount of electricity consumption in buildings would significantly reduce energy costs to consumers and facilitate the transition to a decarbonized economy. Grid-interactive efficient buildings (GEBs) incorporate energy efficiency, smart technologies, and active use of distributed energy resources (DERs) to provide these benefits. As the link between the customer and the power system, utilities are central to widespread deployment of GEBs. Yet, despite the significant cost savings and operational benefits of GEBs for utilities, deployment has remained limited. This paper explores emerging models for enabling utilities to facilitate GEB adoption. Building off foundational research conducted to develop the U.S. DOE’s A National Roadmap for Grid-Interactive Efficient Buildings, we describe the most novel recent examples of ways in which utilities and regulators have made demand-side innovation a win for both the customer and the utility. Emerging concepts include: (1) performance incentive mechanisms to align GEB deployment with utility financial motivations, (2) the use of subscription pricing (i.e., fixed monthly bills) to promote load flexibility and energy efficiency, (3) rate-basing utility-controlled behind-the-meter assets, and (4) coupling energy efficiency and load flexibility with electrification proposals to ensure that customer benefits are maximized. The paper concludes with a discussion about prospects for widespread GEB deployment and utilization by utilities, and how to scale efforts.

Satchwell, Andrew↗
DOE OSTI2023

Long-Term Scenarios of Transportation Decarbonization

Achieving a net-zero emissions economy by 2050 requires aggressive curbing of transportation emissions, currently the largest source of U.S. greenhouse gas (GHG) emissions and the fastest growing source of emissions in many countries. Transportation, a heterogeneous sector with many different passenger and freight travel modes and applications, will require a portfolio of solutions to decarbonize. To inform how to achieve significant emissions reductions in U.S. passenger and freight mobility, researchers used the Transportation Energy and Mobility Pathway Options (TEMPO) model to explore many transformation pathways under expert-informed bounding ranges of assumptions on future travel behavior, technology advancements, and policies. Researchers performed more than 2,000 simulations to explore possible transformation pathways and found that a combination of technological, behavioral, and policy strategies enables a staggering 89% reduction in transportation GHG emissions by 2050. Key is the rapid adoption of zero-emission electric vehicles (EVs) for all on-road passenger and freight applications, alongside a simultaneous decarbonization of electricity (supported by managed charging and proper planning). Managing travel demand growth can ease the transition by reducing the requisite amount of clean electricity and sustainable fuels supply.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

Grimsel Test Site - A Successful International Underground Research Laboratory for Many Decades - 20429

For more than 35 years, Nagra and its partners from around the world have been conducting underground research projects at the Grimsel Test Site (GTS, www.grimsel.com) to contribute to the development and confirmation of safe geological disposal concepts and for the characterization of suitable host rock formations. Over the years, the results of this internationally recognized research program have been, and continue to be, incorporated directly into exploration programs, modelling, safety, and engineering feasibility studies on options for deep geological repositories. Each project of the GTS program involves field-testing, laboratory studies, design and modelling tasks, and integrates all scientific and technical aspects. Each project phase is planned with a duration of three to five years to facilitate practical and administrative aspects and allow flexibility for updating the overall project plans with the latest findings. Scientific and engineering interaction among the different projects is ensured via an international steering committee meeting. Hosting an IAEA level C radiation- controlled zone, which allows use of radionuclides, including actinides such as thorium, uranium, neptunium, plutonium and americium, in in-situ experiments is one of the reasons why GTS also developed as a center of excellence for work with radioactive tracers under realistic in-situ boundary conditions. Last year, a new five-year program (2019 to 2023) started which includes projects with a planning horizon of decades. The new five-year program includes a new phase of in-situ experiments using radionuclides such as migration experiments in the Colloid Formation and Migration project (CFM), the Long-Term Diffusion experiment (LTD) and the newly established C-14 and I-129 Migration in cement project (CIM). The 'High Temperature effects on Bentonite' (HotBENT) project is starting in the current phase and is studying the effects of elevated temperatures (>175 deg. C) on bentonite materials. As a generic underground research laboratory (URL) it is expected that the GTS will provide in the coming years a platform for international collaboration, knowledge development and knowledge transfer for the next generation of scientists and engineers in the area of radioactive waste disposal and geosciences. A key role regarding knowledge transfer and training is provided by the well-established Grimsel Training Center (GTC), which (beside many URL related issues) also covers many general aspects of radioactive waste management. In this paper we provide an overview of the current program at the GTS, focusing on the experiments that study the migration of radionuclides through engineered barrier materials and the geosphere. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Understanding the Role of Extrinsic Impurities in Bulk Nb SRF Cavities

High gradient and high quality factor superconducting radiofrequency (SRF) cavities are essential for efficient particle acceleration in next-generation accelerators. This work demonstrates that the superconducting performance of bulk niobium can be enhanced through a controlled introduction of interstitial oxygen and nitrogen. Thermal and chemical surface treatments modify the concentration and distribution of these impurities, enabling systematic tuning of SRF cavity performance. By combining cavity RF measurements with quantitative materials characterizations, we isolate the impact of impurity concentration on BCS surface resistance and identify the mechanisms by which interstitial impurities alter the superconducting properties of niobium. We find that nitrogen is an order of magnitude more effective than oxygen in reducing BCS resistance at 16 MV/m and that the reduction arises from a combination of mean free path optimization and superconducting gap enhancement. Using these insights, we develop a predictive model that estimates cavity performance directly from room temperature sample material studies, reducing the reliance on full-scale cryogenic tests for surface treatment development. We then apply this model to explore co-doping strategies, demonstrating that combining interstitial oxygen and nitrogen achieves a synergistic reduction in BCS resistance. These results establish co-doping as a viable path for the development of surface-engineered niobium for future high quality factor SRF applications.

Hu, Hannah [Chicago U.]↗
DOE OSTI2022

Modeling Dust Production, Growth, and Destruction in Reionization-era Galaxies with the CROC Simulations: Methods and Parameter Exploration

We introduce a model for the explicit evolution of interstellar dust in a cosmological galaxy formation simulation. We post-process a simulation from the Cosmic Reionization on Computers project, integrating an ordinary differential equation for the evolution of the dust-to-gas ratio along pathlines in the simulation sampled with a tracer particle technique. This model incorporates the effects of dust grain production in asymptotic giant branch star winds and supernovae, grain growth due to the accretion of heavy elements from the gas phase of the interstellar medium, and grain destruction due to thermal sputtering in the high-temperature gas of supernova remnants due to thermal sputtering. A main conclusion of our analysis is the importance of a carefully chosen dust destruction model, for which different reasonable parameterizations can predict very different values at the ∼100 pc resolution of the interstellar medium (ISM) in our simulations. We run this dust model on the single most massive galaxy in a 10 h$^{−1}$ comoving megaparsec box, which attains a stellar mass of ∼2 × 10$^{9}$ M $_{⊙}$ by z = 5. We find that the model is capable of reproducing dust masses and dust-sensitive observable quantities broadly consistent with existing data from high-redshift galaxies. The total dust mass in the simulated galaxy is somewhat sensitive to parameter choices for the dust model, especially the timescale for grain growth due to accretion in the ISM. Consequently, observable quantity observations that can constrain galaxy dust masses at these epochs are potentially useful for placing constraints on dust physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2022

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

The impact of agricultural trade approaches on global economic modeling

Future socioeconomic and climate scenarios have been explored using integrated assessment models (IAMs) to understand interactions between human development and global environmental change in the long run. However, differences in trade modeling approaches are an important source of uncertainty in the assessments, particularly for regional projections. Here, we explore the critical role of trade modeling in assessing the potential future of global agroeconomics and terrestrial carbon emissions with a well-established IAM, the Global Change Assessment Model (GCAM). We update the crop trade modeling framework in GCAM from a Heckscher-Ohlin-Vanek (HOV) structure with integrated world markets (IWM) to a newly developed logit-based Armington approach with segmented regional markets (SRM). The updates make it possible to study the sensitivity of model projections of future agroeconomics and terrestrial carbon emissions to assumptions of the state and magnitude of global market integration. Our results demonstrate that assuming full global market integration, represented by homogeneous product modeling, neglecting economic geography, and excluding margins and tariffs, could lead to lower cropland use (i.e., by 115 million hectares globally) and terrestrial carbon fluxes (i.e., by 25%) by the end of the century. However, the results are highly heterogeneous across regions with more pronounced regional trade responses driven by global market integration. Our study highlights the critical role of trade modeling around product differentiation, economic geography, and regional trade parameterization in global economic or integrated assessment modeling. The results also imply that further reconciliations in trade model approaches could improve the convergence of regional results among models in model intercomparison studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Impacts of Spatial Resolution in a High-Fidelity Capacity Expansion Model: An ERCOT Case Study

Capacity expansion models are important tools in examining the evolution of the electric power sector. Embedded in these tools are many modeling choices with consequential impacts on computational burden and associated analysis. In this study, we adjust the spatial resolution of the Regional Energy Deployment System (ReEDS) to understand the implications of higher-fidelity modeling on energy system projections and model solve times. The native ReEDS regions capture the contiguous United States in 134 balancing areas whereas the regions in the higher-resolution version are defined by over 3,000 U.S. counties. Using both resolutions, we conduct a case study of the Texas Interconnection (The Electric Reliability Council of Texas [ERCOT]) to explore differences in model projections and to inform appropriate applications of high spatial resolution in a large-scale, applied capacity expansion model.

county↗
DOE OSTI2022

Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm

Retreat and advance of ice sheets perturb the gravitational field, solid surface and rotation of the Earth, leading to spatially variable sea-level changes over a range of timescales $\textit{O}$ (10 0–6 years), which in turn feed back onto ice-sheet dynamics. Coupled ice-sheet–sea-level models have been developed to capture the interactive processes between ice sheets, sea level and the solid Earth, but it is computationally challenging to capture short-term interactions $\textit{O}$ (10 0–2 years) precisely within longer $\textit{O}$ (10 3–6 years) simulations. The standard forward sea-level modelling algorithm assigns a uniform temporal resolution in the sea-level model, causing a quadratic increase in total CPU time with the total number of input ice history steps, which increases with either the length or temporal resolution of the simulation. In this study, we introduce a new “time window” algorithm for 1D pseudo-spectral sea-level models based on the normal mode method that enables users to define the temporal resolution at which the ice loading history is captured during different time intervals before the current simulation time. Utilizing the time window, we assign a fine temporal resolution $\textit{O}$ (10 0–2 years) for the period of ongoing and recent history of surface ice and ocean loading changes and a coarser temporal resolution $\textit{O}$ (10 3–6 years) for earlier periods in the simulation. This reduces the total CPU time and memory required per model time step while maintaining the precision of the model results. We explore the sensitivity of sea-level model results to the model temporal resolution and show how this sensitivity feeds back onto ice-sheet dynamics in coupled modelling. We apply the new algorithm to simulate sea-level changes in response to global ice-sheet evolution over two glacial cycles and the rapid collapse of marine sectors of the West Antarctic Ice Sheet in the coming centuries and provide appropriate time window profiles for each application. The time window algorithm reduces the total CPU time by ~ 50 % in each of these examples and changes the trend of the total CPU time increase from quadratic to linear. This improvement would increase with longer simulations than those considered here. Our algorithm also allows for coupling time intervals of annual temporal scale for coupled ice-sheet–sea-level modelling of regions such as West Antarctica that are characterized by rapid solid Earth response to ice changes due to the thin lithosphere and low mantle viscosities.

59 BASIC BIOLOGICAL SCIENCES↗