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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗
DOE OSTI2021

Time Dependence of Methane Generation Rates from Saltstone

This report documents the tests performed to address the requirements listed in the Technical Request, which resulted in a Task Technical & Quality Assurance Plan (TTQAP) and a Run Plan to obtain the required information. The research expands previous scoping work to better understand methane generation in the Saltstone Disposal Facility (SDF). That scoping work demonstrated that methane is produced when sulfide-containing material is added to methylmercury (MeHg), which is found in the SDF Tank 50 feed waste. When Tank 50 waste is combined with grout premix solids dry feed ingredients of cement, slag cement, and fly ash, sulfides are released from the slag cement component of the premix. The prior testing results showed that while the concentration of total mercury was not affected in the pH 14 waste, the MeHg likely reacted with the sulfide contained in the slag. The scoping tests were performed in sealed reactors and thus, methane generation was not measured continuously; rather, it was measured after one hour and after 24 hours. After 24 hours, a large percentage of the MeHg was converted to methane, but after 1 hour only a small percentage of methane was released. It is noteworthy that the methane generation rate (MGR) was greater at the one-hour measurement. The data compiled from the scoping work needed to be expanded to better understand the generation of methane released from grout mixed with Tank 50 waste. This expanded study developed a more extensive knowledge of how and when methane is generated to inform the Documented Safety Analysis (DSA) of the SDF. As such, employing 8 sealed reactors, the methane generation time periods were extended to 2, 4, 6, 18, 30, 48, 72, and 96 hours (4 days). Results are documented herein to determine the capacity for MeHg, contained within Tank 50 salt solution (T50SS) simulant, to produce methane when mixed with saltstone dry feed ingredients, i.e., individual components (cement, slag, and fly ash) or a combination of those components.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Wind Turbine Generator Reliability Analysis to Reduce Operations and Maintenance (O&M) Costs

Wind turbine major systems (blades, pitch, main bearing, gearbox, and generator) are integrated into a composite system. Specifications for these systems and components are developed to achieve symmetry of operation, avoiding negative interaction. For instance, the main bearing, gearbox, and generator (drivetrain) components are interdependent, functioning in unison for efficient energy production. Hence, wind resource and grid interactions affecting the drivetrain impact the performance and reliability of the turbine generator. This paper discusses generator reliability covering the technology evolution over the last 20 years. EPRI's Wind Network for Enhanced Reliability (WinNER) web-based tool and Shermco Industries databases are presented, and conclusions are drawn regarding failures specific to generator design, manufacturing, and operating conditions. Additionally, this paper compares the life expectancy of stator-fed configurations and doubly fed generator systems.

17 WIND ENERGY↗
DOE OSTI2024

Generating Electricity with Hydraulically Amplified Self-Healing Electrostatic (HASEL) Transducers

This study identifies hydraulically amplified self-healing electrostatic (HASEL) transducers as electricity generators, contrary to their conventional role as actuators. HASELs are soft, variable-capacitance transducers inspired by biological muscles which were developed to mimic the flexibility and functionality of natural muscle tissues. This research characterizes HASELs as generators by reversing their energy conversion mechanism—generating electricity through mechanical deformation. The study assesses the practical laboratory performance of HASELs by analytic modeling and experimental evaluation. Outcomes of the study include the following: (i) up to 2.5 mJ per cycle per 50 mm wide HASEL pouch of positive net energy generation in experimental testing—corresponding to an energy density of 2.0 mJ cm−3; (ii) a maximum theoretical energy density of 4.2 mJ cm−3; (iii) the electromechanical characteristics governing efficient conversion; and (iv) design considerations to enhance HASEL generator performance in future applications. This study broadens HASEL’s applicability and utility as a multi-functional transducer for renewable energy and general adaptive electricity generation.

13 HYDRO ENERGY↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
NASA NTRS2021

Intergenerational Inequities in Exposure to Climate Extremes: Young Generations Are Severely Threatened By Climate Change

Under continued global warming, extreme events such as heat waves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades. Younger generations are therefore expected to face more such events across their lifetimes compared with older generations. This raises important issues of solidarity and fairness across generations that have fueled a surge of climate protests led by young people in recent years and that underpin issues of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming, a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared with another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations (see the first figure), we estimate that children born in 2020 will experience a two- to sevenfold increase in extreme events, particularly heat waves, compared with people born in 1960, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future. Meteorological extremes, hazards, or climate change impacts are mostly studied as they evolve over time under varying emission scenarios and socioeconomic pathways. For example, applying a heat wave indicator (see table S1) to four bias-adjusted global climate models indicates that the land area annually affected by such heat waves will increase from ~15% around 2020 to ~22% by 2100 under a scenario compatible with limiting global warming to 1.5°C, and to ~46% under a scenario in line with current emission reduction pledges (see the first figure). Recent studies extended this approach, studying aspects of climate change as a function of global mean temperature (GMT) increments, highlighting the scenario-independence of several extreme event indicators but remaining, in essence, a comparison of time windows. By contrast, we performed a birth cohort analysis by combining a collection of multimodel extreme event projections with country-scale life expectancy information, gridded population data, and future global temperature trajectories from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C (see supplementary materials). By integrating the exposure of an average person in a country or region to extreme events across their lifetime, we encapsulate spatiotemporal changes in climate hazards, population density, cohort size, and life expectancy (see the first figure).

global warming↗
DOE OSTI2021

Providing Ancillary Services with Photovoltaic Generation in Multi-Timescale Grid Operation: Preprint

With photovoltaic (PV) generation substantially increases, electric power systems need more flexible resources that can provide ancillary services to mitigate the variability and uncertainty of the PV generation. On the one hand, the increase of PV generation necessitates the needs of more flexible resources. On the other hand, PV generation, because of its low operation cost, has been replacing the conventional generation in the system which is the main flexible resources in the current system. Consequently, there is a trend to require the renewable generation including PV to provide flexible ancillary services to further accommodate more PV integration. This paper proposes a multi-timescale grid operation model considering the various control strategies of PV providing different ancillary services. Numerical case studies demonstrate that with PV providing both regulation reserve and primary frequency reserve, the system operating costs and PV curtailment will be reduced significantly. It shows that not only the system reliability but also PV's profitability can be improved with PV providing more ancillary services.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

System Resilience Benefits of Dual-Fuel Capable Generators

The growing dependency on natural gas (NG) for power generation raises challenges for ensuring the resilience of power systems during extreme cold weather. Dual-fuel capable generators that can switch from burning NG to distillate fuel oil during an NG shortage over one way to alleviate these challenges. In this study, the impacts of gas unavailability on the IEEE 73-bus reliability test system (RTS) with 2019 updated generation mixture are explored. An extension to the standard production cost model formulation of unit commitment and economic dispatch is proposed to allow the representation of dual-fuel capable generators that can switch fuels between NG and distillate oil with a specified oil tank capacity and tank refueling capability. The operation of the system under gas unavailability with 0%, 25%, 50%, 75%, and 100% of combined cycle and combustion turbine generators as dual-fuel capable with a one-day supply of fuel oil are simulated using PLEXOS, a production cost model. The dual-fuel generator performance, unserved energy, and system costs are fully assessed.

dual-fuel capable generator↗
DOE OSTI2024

Distributed Automatic Generation Control Considering DPV Using T&D Dynamic Co-Simulation

The increasing adoption of distributed energy resources (DERs) over the last decade warrants a reconsideration of control of generation resources. This paper proposes a distributed Automatic Generation Control (AGC) using transmission-and-distribution (T&D) dynamic co-simulation framework for the efficient DPV frequency regulation services. The co-simulation framework allows AGC units to exchange the information for distributed AGC, based on their adopted communication network topology. As a result, a cost-effective automatic generation control is achieved with DPV and conventional generators. The proposed distributed AGC is based on the gossip algorithm in which the neighboring AGC units share the relevant local information with each other and updates their share of AGC regulation signal. Distributed photovoltaics (DPV) unit contribute to AGC response based on their headroom capacity via DER aggregators. The algorithm is tested on IEEE-14 bus transmission system under conditions of generation failure and random load variation to observe effective frequency regulations service offered by DPVs and other AGC units. The study shows that DPV can effectively participate in AGC with the proposed distributed control framework.

automatic generation control↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗
DOE OSTI2024

Can Wholesale Electricity Markets Achieve Resource Adequacy and High Clean Energy Generation Targets in the Presence of Self-Interested Actors?

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗
DOE OSTI2024

Power generation-cooling water Nexus: Impacts of cooling water shortage on power system operation - a simulation case study in Illinois, U.S

Cooling water shortage, frequently attributed to drought and heat waves, poses a significant threat to the operations of thermoelectric power plants and further poses a challenge for the entire power system and environmental stakeholders. Recognizing the critical nexus between power generation and cooling water availability and the potential ability of power generations to adjust generation schedules during cooling water shortages, this paper introduces a security-constrained unit commitment and economic dispatch model considering water-energy nexus. In specific, the model is augmented with a unit-level cooling water requirement (CWR) model and multi-level cooling water availability (CWA) constraints. The unit-level CWR model quantifies the cooling water withdrawal per MWh of power generation, taking into account factors such as thermoelectric generation technologies, cooling system technologies, and environmental parameters. The multi-level CWA constraints incorporate pump-level, plant-level, watershed-level, and forced minimum power constraints, utilizing data derived from actual-based cooling water shortage scenarios. Using a simulation case study in Illinois, United States, this research examines the reliability, economic, and environmental implications of cooling water shortages on power system operations. The results show that Illinois may experience 10-15% daily load curtailment and severe congestion between certain regions from the east to central during cooling water shortages, while once-through and wet-tower units experience a 52% and 17% reduction in power generation. In conclusion, overall cooling water withdrawal decreases by 24-38% as severity intensifies.

Cooling water shortage↗
DOE OSTI2026

Evaluating alternative steam generation pathways in the food industry

Steam generation in the food sector requires substantial energy and cost expenditures, requiring nearly half of its energy intake. Here, we used life cycle assessment and life cycle cost assessment to investigate the cost and energy impacts of steam generating alternatives: NG, biomass and hydrogen boilers and grid-supplied and self-generated electric steam generation systems (electric boilers, renewable thermal energy storage and industrial heat pumps). The analysis starts with a set of average U.S. conditions, where biomass boilers are the most cost-competitive alternative to NG. In a series of scenarios beyond average conditions, the analysis shows energy procurement costs dominate the total life cycle cost for all technologies and, unfortunately, are highly variable geographically and temporally, significantly affecting the viability of the alternatives. Results show that site-energy consumption ranges from 0.3 MMBtu/klb for industrial heat pumps to 1.6 MMBtu/klb for biomass boilers, with heat pumps achieving up to 78% lower energy use compared to natural gas systems. For the steam costs, the results show a range between $\$$8 and $\$$50/klb for NG, with biomass following closely ($\$$11 – $\$$44/klb) and grey hydrogen and IHP next ($\$$13 – $\$$33/klb and $\$$6 - $\$$78/klb), with cost reductions if IHP's cooling is utilized. Factors like operating hours, the need for cooling, and the ability to negotiate utility rates complicate the decision, making site-specific analyses critical. Therefore, we present a decision-making matrix to help manufacturers identify which steam generating technology is the best business decision for their situation. Overall, these results highlight the importance of steam generation for the facility's organizational goals, as well as the criticality of conducting individual site analyses.

Biomass boilers↗
DOE OSTI2020

Production of mechanically-generated 316L stainless steel feedstock and its performance in directed energy deposition processing as compared to gas-atomized powder

The objective of this work is to study the feasibility of mechanically-generated feedstock for use in directed energy deposition (DED) processing. Mechanically-generated powder was created by machining 316L stainless steel bar stock followed by comminution of the resulting chips through oscillation ball milling. This methodology's production yield and processing time for the specifications of a commercially available DED system are presented along with resulting powder morphology. Performance of the mechanically-generated feedstock was compared to gas-atomized powder and evaluated based on the following figures of merit: flowability, printed part height, printed part density, and chemical compositional stability throughout processing. Mechanically-generated feedstock was created to meet deposition system requirements. Compared with gas-atomized powder, mechanically-generated powder did not flow as well through the powder-delivery system. Parts printed from mechanically-generated feedstock were generally taller than their counterparts from gas-atomized feedstock, but their densities were less predictable. As a result, chemical composition of prints using both feedstocks was within standard nominal compositions for 316L stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stochastic generation of electrolyzer anode catalyst layers

Here, we introduce a stochastic methodology to reproduce the complex pore structure observed in commercial iridium catalyst layers. This method preserves the α pore (pores smaller than 250 nm) and β pore (pores greater than or equal to 250 nm) regions of the catalyst layer. The morphology of the generated materials was validated by comparing the pore size distributions of generated materials against those obtained from commercial materials imaged using x-ray nano computed tomography. We further demonstrate that the pore size distributions of the generated materials are statistically indistinguishable from the imaged catalyst layers, indicating that the stochastic methodology is capable of accurately reproducing catalyst layer morphology. Pore network modelling was conducted on the generated catalyst materials to simulate single-phase permeability, electrical conductivity, and ionic conductivity, and these properties were found to be within experimentally measured ranges for electrolyzer catalyst layers. Additionally, simulations were performed on the generated materials with varying ionomer and iridium catalyst loadings. As the ionomer loading is added, proton conductivity increases exponentially, which demonstrates the importance of optimizing ionomer loading, considering that these effects will be exacerbated in the hydration and temperature conditions of operating electrolyzers. The stochastic material generation method presented in this work is a powerful tool for the development of novel low loading catalyst layers, where the effect of various structural parameters on electrolyzer performance characteristics can be explored.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cavity-Assisted Coherent Phonon Generation and Control in a WSe 2 /Au Structure

Coherent phonons in the Terahertz (THz) regime have gained attention as potential candidates for next-generation high-speed, low-energy information carriers in atomically thin phononic or phonon-integrated on-chip devices. Nevertheless, achieving efficient control over THz coherent phonons continues to pose a considerable challenge. In this work, we explore THz coherent phonon generation in exfoliated van der Waals (vdW) flakes of WSe 2 on Au (WSe 2 /Au) and Si (WSe 2 /Si) using time-resolved pump-probe spectroscopy. The generation of THz coherent phonons was studied as a function of WSe 2 layer thickness and laser wavelength. Notably, a significant enhancement in THz coherent phonon generation was observed in the WSe 2 /Au structure, but only within specific ranges of WSe 2 thickness and laser wavelength. Further, the results from numerical simulations, which consider a self-hybridized optical cavity depending on WSe 2 thickness, along with optical reflectance and Raman spectroscopy measurements, aligned well with the time-domain observations of THz coherent phonon generation. We propose that the observed enhancement in THz coherent phonon generation is strongly influenced by light-matter interaction in the WSe 2 cavity, a mechanism that may be applicable to a broader range of vdW materials. These findings offer promising insights for the development of THz phononic or phonon-integrated devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗