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

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Capabilities for Water Sector Infrastructure Resilience - Prioritizing RD&D in a Target Rich, Resource Poor Sector

WSTB & Water Sector Security Program Expansion Objective: Incubate and shepherd a public-private consortium of joint seal US government sponsors and industry stakeholders to build out industrial control system (ICS) and operational technology (OT) architecture of the Idaho National Laboratory (INL) Water Security Test Bed (WSTB) asset to enable research, testing, and cyber workforce training related to evolving cyber-physical and physical vulnerabilities and threats in the water sector.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Grid Forming and Following Background, Future, and Strategies

The landscape of the U.S. power structure with respect to microgrid configuration strategies that utilize grid forming and grid following converters is becoming relevant. Inverter based control and coordination strategies are every changing due to critical failures and limitations. Understanding current state of grid forming and following converter technology and their normal operational guidelines will lead into the next stage of future research. Grid forming and following converter control strategies and ratio of assets review two case studies to develop an understanding of the current landscape.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2026

A Real2Sim Digital Twin Pipeline for Photorealistic Robot Simulation: Evaluating VLA Policy Deployment on a Bimanual Mobile Robot

Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗
DOE OSTI2023

GOOML (Geothermal Operational Optimization with Machine Learning) [SWR-23-01]

The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.

Buster, Grant↗
DOE OSTI2022

Synergistic Electrocatalytic Syngas Production from Carbon Dioxide by Bi‐Metallic Atomically Dispersed Catalysts

Abstract The production of syngas by traditional processes such as steam methane reforming is energetically intensive and produces a large amount of CO 2 emissions. In contrast, the electrochemical CO 2 reduction reaction (CO 2 RR) enables the carbon neutral production of syngas at ambient conditions. Among non‐precious metal catalysts, metal‐nitrogen‐carbon electrocatalysts are inexpensive and highly selective towards syngas production. This study examined the selectivity of mono‐ and bi‐metallic (M−N−C, M=Fe, Mo or FeMo) electrocatalysts towards syngas production. The ratio of the CO : H 2 in the syngas was tuned by modifying the ratio of the metallic precursors in the bi‐metallic FeMo−N−C catalysts, tailoring the catalysts’ selectivity towards the CO 2 RR or the hydrogen evolution reaction (HER). The catalyst synthesis temperature(s) were considered as they influence the catalyst morphology and activity. Further, the dependence of the ratio of CO : H 2 in the syngas as a function of the potential was explored for the different bi‐metallic catalysts. This work showed that by tailoring both the ratio of Fe : Mo in the bi‐metallic catalyst and optimizing the reductive potential, a CO : H 2 ratio between 0.25 to 5 was achievable. This study demonstrated a novel approach in which the ratio of the product syngas composition could be tailored in a single reaction, without the need for further downstream processing to reach a desired composition.

Delafontaine, Laurent↗
DOE OSTI2023

Multi-atom Pt and PtRu catalysts for high performance AEMFCs with ultra-low PGM content

To reduce the platinum group metal (PGM) loading in anion exchange membrane fuel cells (AEMFCs), it is important to transition to catalysts with very low PGM content, and eventually to catalysts that are completely PGM-free. Here, in this work, four supported low-PGM Pt and PtRu catalysts were prepared using a new, simple, scalable technique: Controlled Surface Tension (CST) method. CST allows for a high density of very small multi-atom clusters. Catalysts were physically characterized using a wide array of techniques and tested for their ORR and HOR activity both ex-situ and integrated into operating AEMFCs. The PGM loading was reduced by a factor of 14 while achieving comparable performance to commercial catalysts. AEMFCs were also assembled with ultralow PGM loading (0.05 mgPGM cm -2 ), where PtRu anodes were paired with Fe–N–C cathodes to achieve a specific power of 25 W/mg PGM (40 W/mg Pt ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ni(OH) 2 -free NiCu as a hydrogen evolution and oxidation electrocatalyst

In this work, we investigate the role of the nickel surface oxidation and the use of additional reducing agent in the synthesis process in the electrochemical activity of NiCu electrocatalysts for hydrogen oxidation reaction and hydrogen evolution reaction. In addition, the role of the carbon support morphology is also investigated: two different supports, DENKA black and Ketjenblack with widely different properties (specific surface, proportion of low strain carbons, etc.) are studied, and no difference are observed in their electrochemical activity. However, the use 4-aminoantipyrine as a reducing agent resulted in a dramatic increase in non-oxidized Ni at the electrocatalyst’ surface, concomitant to a large increase in its activity for both the hydrogen evolution and oxidation.

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

Life cycle analysis of a hydrogen production system based on solid oxide electrolysis cells integrated with different energy and wastewater sources

Hydrogen production via water electrolysis is a promising and evolving technology. The solid oxide electrolysis cell (SOEC) is one of the several technologies for this purpose. They are alternatives to the traditional hydrogen production from fossil fuels to enhance global energy decarbonization. However, in the bid for sustainability and a green economy, these technologies are not free from causing some environmental burdens. Using life cycle assessment (LCA), the greenhouse gas emission and degree of environmental impacts by these technologies can be identified and measured throughout their lifespan. This study addresses how to quantify these CO 2 emission impacts for an electrolytic system with different energy sources. The LCA of a novel integrated hydrogen production SOEC system with energy from solar photovoltaic and bituminous powerplant is performed using simaPro, the leading global LCA solution software. Measuring the environmental impact of the electrolytic operation in terms of weight, the bituminous powerplant poses damage which is, on average 700% more than solar PV. The thermal-to-hydrogen efficiency of the SOEC system is 56%, and a probable 28% reduction in carbon footprint with heat integration. The steam generation and the manufacturing of the SOEC have a very high environmental impact potential on the system. Here, we recommend LCA and net energy analysis is carried out for every new hydrogen technology and adequate comparison to enhance sustainable and green economy.

08 HYDROGEN↗
DOE OSTI2020

Facile All-Optical Method for In Situ Detection of Low Amounts of Ammonia

As a key precursor for nitrogenous compounds and fertilizer, ammonia affects our lives in numerous ways. Rapid and sensitive detection of ammonia is essential, both in environmental monitoring and in process control for industrial production. Here we report a novel and nonperturbative method that allows rapid detection of ammonia at low concentrations, based on the all-optical detection of surface-enhanced Raman signals. We show that this simple and affordable approach enables ammonia probing at selected regions of interest with high spatial resolution, making in situ and operando observations possible.

09 BIOMASS FUELS↗
DOE OSTI2021

Catalysts by pyrolysis: Direct observation of chemical and morphological transformations leading to transition metal-nitrogen-carbon materials

Transition metal-nitrogen-carbon materials (M-N-C catalysts) are promising electrocatalysts in polymer electrolyte fuel cells (PEFCs) and electrolyzer applications. High temperature treatment in inert atmosphere (pyrolysis) is the essential, most common method for the synthesis of M-N-C catalysts and critical to achieve high electrocatalytic activity and electronic conductivity. To this day, despite many uses and successful implementations in materials manufacturing, pyrolysis has been an entirely empirical technology, with process control and optimization relying exclusively on “Edisonian” approach. The knowledge gap in the mechanism about how the precursor is being transformed into catalysts hinders further development of the M-N-C catalysts regardless of the precursor class and processing protocols. In this work, we probed the morphological evolution and chemical transformation of a nitrogen-containing charge transfer organic salt, mixed with transition metal (iron) salt and amorphous silica powder (precursor) during the pyrolysis process via a combination of in situ synchrotron and laboratory-based diagnostic techniques. The pyrolysis process is found to be divided into three stages. During a controlled temperature ramp, the selected organic N-C precursor (nicarbazin) began melting and decomposing just below 400 °C, forming a certain number of micrometer-scale pores and pathways. With increase in temperature from 400 °C to 900 °C, amorphous carbon domains started forming, and reduced (metallic) iron nanoclusters appeared, being dispersed uniformly throughout the carbonaceous matrix. When temperature advanced above 900 °C, graphitization of carbon commenced, associated with appearance and evolution of atomically dispersed metal-nitrogen moieties in the carbonaceous matrix. As the graphitization advanced further, a secondary process of agglomeration of metal nanoparticles occurred. Multi-analytical technique observations conducted here provide a base for rational design and optimization of M-N-C electrocatalysts via pyrolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Catalysts by pyrolysis: Direct observation of transformations during re-pyrolysis of transition metal-nitrogen-carbon materials leading to state-of-the-art platinum group metal-free electrocatalyst

Transition metal-nitrogen-carbon (M-N-C) materials have been the focus of scientists’ efforts to address the rising need for earth-abundant materials solutions for energy technology and decarbonization of the economy. They are viewed as one of the most promising candidates to replace platinum group metal (PGM) catalysts in the fuel cell and energy conversion fields, including the application of oxygen reduction reaction, carbon dioxide reduction reaction, and nitrogen reduction reaction. In the effort to improve M-N-C materials properties and achieve atomic dispersity of the transition metal in the carbonaceous matrix, a re-pyrolysis process has been proposed. This secondary heat treatment process of already obtained primary pyrolysis-derived M-N-C materials has been widely reported to substantially improve the electrochemical performance and operational stability of the catalysts. Here, we report a systematic investigation of this process used on samples of templated M-N-C catalysts to obtain state-of-the-art catalysts via in situ heating X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy (STEM), energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), X-ray diffraction (XRD), and X-ray computed tomography (CT) characterization methods. It is found that the re-pyrolysis of M-N-C materials could result in the partial amorphization of the carbonaceous substrate. It causes the rearrangement and transformation of multitudinous N moieties, leading to optimization of their morphological display and association with atomically dispersed transition metal dopants. Ultimately, the re-pyrolysis results in an increase in uniformity of the active Fe-Nx sites distribution without the formation of nano-crystalline phases (metallic or carbide) and with overall preservation of the morphology of the carbonaceous framework achieved during the first formative pyrolysis step of the templated synthesis. Finally, these observations provide confirmation that empirically established re-pyrolysis is recommended to be used on all M-N-C materials despite the different synthesis routes to obtain a practical advanced catalytic material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗