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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Revealing the coupled oxygen and hypochlorite chemistry in saltwater batteries through operando pH and oxygen monitoring

Saltwater batteries (SWBs) that utilize Na⁺ ions from seawater have emerged as promising candidates for low-cost and sustainable grid-scale energy storage. To date, the cathode reaction mechanism of SWBs has been predominantly described by oxygen evolution and reduction reactions (OER/ORR). However, this assumption is valid only under idealized ocean-like conditions with constant pH and continuous oxygen replenishment. In practical systems, SWBs operate in finite volumes of saltwater, where saltwater composition dynamically evolves during cycling. Here, in this work, we systematically investigate the cathode reaction mechanisms of SWBs under finite saltwater conditions using galvanostatic cycling combined with electrochemical diagnostics and operando monitoring of dissolved oxygen and pH. Our results reveal that the cathode chemistry during SWB operation is considerably more complex than previously assumed. In addition to OER and ORR, hypochlorite formation and consumption reactions, along with pH-dependent switching of dominant reaction pathways, play critical roles. We further identify the sequence and relative contributions of these reactions throughout charge–discharge cycling. These findings provide a comprehensive and mechanistically grounded understanding of SWB cathode processes under relatively realistic cell design and operation condition. The insights presented here establish a new framework for interpreting SWB electrochemistry and offer directions for future strategies aimed at improving performance, stability, and practical viability.

Hypochlorite redox reaction↗
DOE OSTI2025

Rancor Integrated Procedure System (RIPS): A Computer-Based Procedure Platform for Advanced Reactor Research

The Rancor Microworld Simulator is a simplified, pressurized water, small modular reactor simulator that includes a multi-unit plant model server, an advanced digital human-machine control interface, and the Rancor Integrated Procedure System (RIPS). Rancor provides a research and development tool that can be used for collecting operator performance data and for prototyping concepts of operations (ConOps) for advanced reactor development. RIPS is meant as a research tool and includes many unique features: (1) RIPS has a robust procedure authoring system. (2) RIPS has the capability to run any of the three IEEE-Std-1786 computer-based procedure types. (3) RIPS can be configured to take on the look and feel of different vendors’ computer-based procedure systems for the purpose of developing and evaluating different ConOps for plant upgrades or new builds. (4) RIPS includes the capability for logging operator procedure use, including integrating procedure logs with Rancor simulator logs, thereby allowing automated data collection of operator scenario runs. (5) RIPS integrates with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a dynamic human reliability analysis environment that creates a digital human twin or virtual operator to mimic reactor operator performance. (6) RIPS includes support for automation of plant monitoring and control functions. While RIPS is explicitly built into Rancor, it may also be used with full-scope training simulators. This functionality allows RIPS to be used for existing plants and advanced reactors under development.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Stress evolution and creep deformation in solid-oxide electrolysis cell systems – Dynamic modeling and multi-objective optimization to maximize stack life and efficiency

Here, this study develops a thermal stress model of solid-oxide electrolysis cells (SOECs) including a model for creep strain and failure probability that is integrated with a dynamic plant-wide model of a hydrogen production process. Uncertainties in key material properties of the cell are quantified to assess their impact on stress profile variability. The oxygen electrode is found to have about 10 times higher failure probability compared to the fuel electrode. The study shows that if the stack operation is not optimized, cycling operation would lead to stress build-up eventually leading to catastrophic failure. A dynamic optimization problem is set up for obtaining the optimal operational profile considering a variable hydrogen production rate. Due to the tradeoff between the efficiency and stress build-up, the dynamic optimization problem is multi-objective. It is observed that the optimizer can considerably reduce the stress build-up (i.e., can increase the stack life) albeit at the cost of a lower efficiency thus exhibiting strong tradeoffs between capital and operating costs. For example, if the stack would be replaced in 0.5 yr, specific energy requirement would be 48.5 kWh/kg H 2 while for a stack replacement time of about 6 yr, the specific energy requirement rises by about 4.2 %.

SOEC↗
DOE OSTI2025

Dynamic_Syringe_Pump_Controller

A LabVIEW based programmable user interface for operating ISCO D-series and SyriXus-series syringe pumps that enables advanced user operations and integration with auxiliary data sources.

Frash, Luke↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Dynamic Aperture Studies at the Fermilab Recycler Ring

As part of the Proton Improvement Plan II (PIP-II), Fermilab aims to increase beam intensity delivered to neutrino experiments. In this context, higher intensity injection into the Recycler Ring enhances space charge effects, pushing operations closer to third-order resonances. These resonances reduce the Dynamic Aperture (DA), leading to increased beam loss. This study presents simulations of DA as a function of tune in the Recycler Ring, incorporating chaos indicators such as the Reversibility Error Method (REM). The effectiveness of existing resonance mitigation strategies is evaluated by quantifying their ability to delay DA degradation. Additionally, the study examines how space charge detuning and DA limitations dictate viable operational tune points for the Recycler Ring.

Gonzalez-Ortiz, Cristhian Eduardo [Fermilab]↗
DOE OSTI2026

Dynamic aperture studies at the Fermilab recycler ring

As part of the Proton Improvement Plan II (PIP-II), Fermilab aims to increase beam intensity delivered to neutrino experiments. In this context, higher intensity injection into the Recycler Ring enhances space charge effects, pushing operations closer to third-order resonances. These resonances reduce the Dynamic Aperture (DA), leading to increased beam loss. This study presents simulations of DA as a function of tune in the Recycler Ring, incorporating chaos indicators such as the Reversibility Error Method (REM) and Frequency Map Analysis (FMA). The effectiveness of existing resonance mitigation strategies is evaluated by quantifying their ability to delay DA degradation. Additionally, the study examines how space charge detuning and DA limitations dictate viable operational tune points for the Recycler Ring.

Gonzalez-Ortiz, Cristhian [Fermilab]↗
DOE OSTI2025

LHCspin: a Polarized Gas Target for LHC

The goal of the LHCspin project is to develop innovative solutions for measuring the 3D structure of nucleons in high-energy polarized fixed-target collisions at LHC, exploring new processes and exploiting new probes in a unique, previously unexplored, kinematic regime. A precise multi-dimensional description of the hadron structure has, in fact, the potential to deepen our understanding of the strong interactions and to provide a much more precise framework for measuring both Standard Model and Beyond Standard Model observables. This ambitious task poses its basis on the recent experience with the successful installation and operation of the SMOG2 unpolarized gas target in front of the LHCb spectrometer. Besides allowing for interesting physics studies ranging from astrophysics to heavy-ion physics, SMOG2 provides an ideal benchmark for studying beam-target dynamics at the LHC and demonstrates the feasibility of simultaneous operation with beam-beam collisions. With the installation of the proposed polarized target system, LHCb will become the first experiment to simultaneously collect data from unpolarized beam-beam collisions at $\sqrt{s}$=14 TeV and polarized and unpolarized beam-target collisions at $\sqrt{s_{NN}}\sim$100 GeV. LHCspin has the potential to open new frontiers in physics by exploiting the capabilities of the world's most powerful collider and one of the most advanced spectrometers. This document also highlights the need to perform an R&D campaign and the commissioning of the apparatus at the LHC Interaction Region 4 during the Run 4, before its final installation in LHCb. This opportunity could also allow to undertake preliminary physics measurements with unprecedented conditions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2025

Effects of Particle Mixing and Gravitational Settling on Charge Transport in Carbon Flow-Electrode Cells

Flowable carbon slurries are actively studied and under development for charge transport in various electrochemical systems including flow capacitors, capacitive deionization cells, semi-solid flow batteries, and lithium extraction. However, much less is known about in operando slurry flow dynamics and their corresponding effect on charge transport. We performed an experimental study of mixing and settling dynamics of slurry electrodes within an electrochemical flow cell during continuous operations. The electrochemical cell consisted of two horizontal co-flowing channels, separated by a cation-exchange membrane (CEM). Here we used high-speed optical imaging of planes parallel to gravity and simultaneous electrochemical measurements. At low flow rates, dense yet dynamic particle beds formed on the bottom electrode in each channel, which unexpectedly yielded the highest currents. This approach enables the operation of the flow cell at low system-average particle concentrations while leveraging gravity-driven particle settling to locally enhance carbon concentrations precisely at the current collector sites. Conversely, high flow rates were characterized by thin particle beds and well-mixed particle flows. In the latter case, the electrodes in closest proximity (located on either side of the CEM) achieved a current higher than the other electrode pairs. The observations have implications for slurry control and electrode designs in electrochemical systems.

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

Delineating the Impact of Diluent on High-Concentration Electrolytes for Developing High-Voltage LiNi 0.5 Mn 1.5 O 4 Spinel Cathode

LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-voltage spinel cathode with low nickel content, making it an attractive candidate for next-generation lithium-ion batteries (LIBs). However, its application is limited by interfacial instability with conventional carbonate-based electrolytes at high voltages. In this work, a localized saturated electrolyte (LSE) capable of stably operating up to 4.85 V is investigated. Molecular dynamics simulations and Fourier transform infrared spectroscopy reveal that adding “non-solvating” 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether diluent in the saturated electrolyte, more PF 6 − anions are present in the first solvation shell of Li + , at the expense of solvent molecules. This tailored solvation environment promotes the formation of a robust, LiF-rich cathode-electrolyte interphase that mitigates transition metal dissolution and parasitic side reactions. The optimized LSE enables excellent cycling performance, with 95% capacity retention in Li|LNMO half-cells after 100 cycles and 94% retention in Li 4 Ti 5 O 12 |LNMO full cells after 250 cycles, even at a practically relevant LNMO cathode loading of ≈15 mg cm −2 . In conclusion, these results highlight the benefits of electrolyte engineering and solvation structure control in advancing high-voltage LIB technologies.

LNMO cathode↗
DOE OSTI2024

Shape-persistent ladder molecules exhibit nanogap-independent conductance in single-molecule junctions

Molecular electronic devices require precise control over the flow of current in single molecules. However, the electron transport properties of single molecules critically depend on dynamic molecular conformations in nanoscale junctions. Here, in this work, we report a unique strategy for controlling molecular conductance using shape-persistent molecules. Chemically diverse, charged ladder molecules, synthesized via a one-pot multicomponent ladderization strategy, show a molecular conductance (d[log( G/G 0 )]/d x ≈ -0.1 nm -1 ) that is nearly independent of junction displacement, in stark contrast to the nanogap-dependent conductance (d[log( G/G 0 )]/d x ≈ -7 nm -1 ) observed for non-ladder analogues. Ladder molecules show an unusually narrow distribution of molecular conductance during dynamic junction displacement, which is attributed to the shape-persistent backbone and restricted rotation of terminal anchor groups. These principles are further extended to a butterfly-like molecule, thereby demonstrating the strategy's generality for achieving gap-independent conductance. Overall, our work provides important avenues for controlling molecular conductance using shape-persistent molecules. Achieving robust and controllable conductance in single-molecule junctions is challenging due to the dynamic nature of molecular conformations that fluctuate over operational timescales. A strategy using shape-persistent molecules has now been developed that demonstrates nearly junction-displacement-independent conductance, providing a stable solution for single-molecule electronic properties.

molecular electronics↗
DOE OSTI2026

Out-of-time-order correlators bridge classical transport and quantum dynamics

The out-of-time-order correlator (OTOC) has emerged as a central tool for quantifying decoherence across wide-ranging physical platforms. Here, we demonstrate its direct measurement in a classical ensemble using nuclear magnetic resonance with a modulated gradient spin echo sequence and extend the method into a multidimensional correlation to track exchange phenomena. Position is encoded through magnetic field gradients and momentum through the velocity autocorrelation function, enabling experimental access to OTOCs for proton motion confined within the self-similar lattice of the metal–organic framework MOF-808. Here, water confined to specified geometries within the MOF pores gives rise to spatially distinct diffusive eigenmodes with characteristic relative entropies. We demonstrate that periodic radio frequency driving combined with gradient modulation yields entropy evolution through the selection of distinct diffusion modes. Frequency-resolved diffusion spectra connect these entropy dynamics to classical heat exchange laws, revealing how operational features of quantum systems are mirrored in confined, macroscopic spin ensembles.

Fricke, Sophia N. [University of California, Berke↗
DOE OSTI2025

Particulate organic matter (POM) transport and transformation at the terrestrial-aquatic interface (Final Report)

This project investigates the input, transport, and degradation of particulate organic matter (POM) in near-surface riverbed sediments at the Hanford 300 Area of the Columbia River, a dynamic, regulated river system influenced by upstream dam operations. Riverbed sediments are biogeochemical hot spots where organic carbon inputs stimulate intense microbial activity, affecting nutrient cycling and redox transformations in the hyporheic zone (HZ). While dissolved organic matter (DOM) cycling has been studied extensively, little is known about the infiltration and transformation of POM—particularly under variable flow regimes common in large, regulated rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Heat load measurements for the PIP-II pHB650 cryomodule

This study presents a brief overview of the 1st and 2nd phases and an in-depth analysis of the 3rd phase heat load testing performed on the pHB650 (prototype High Beta 650 MHz) cryomodule at PIP2IT (PIP-II Injector Test Facility), with a focus on both the results and the methodological advancements that have improved testing efficiency and accuracy. A key challenge identified in the testing campaign is the higher-than-expected heat loads observed in the first PIP-II (Proton Improvement Plan II) prototype cryomodules (pSSR1 and pHB650) tested at PIP2IT. Elevated heat loads are concerning given the fixed capacity of the PIP-II cryoplant that is currently being installed at Fermilab. However, understanding the sources of these elevated heat loads offers a critical opportunity to implement effective heat load mitigations on upcoming PIP-II cryomodules to stay within the available capacity of the PIP-II cryoplant. The study includes a summary of test results, descriptions of measurement procedures, and key observations on parameters directly and indirectly related to heat load measurements. Direct observations include measured heat loads and the effectiveness of JT heat exchanger under varying conditions, while indirect observation analyze factors such as the temperature distribution on the two-phase pipe and relief piping under varying conditions. Thermal acoustic oscillations (TAO) were identified during testing, which was mitigated by replacing the original G10 stem with a stainless steel stem equipped with wipers for the cryomodule cooldown valve. A major innovation during pHB650 Phase 3 testing was the development of an automated Python script to streamline data acquisition, analysis, and reporting of heat load results. This script automatically retrieved data from ACNET (Accelerator Control Network), performed heat load calculations, and generated detailed reports featuring plots and tables. This advancement significantly reduced manual labor and enhanced the thoroughness of data analysis compared to earlier campaigns. The heat load test reports were promptly uploaded to the electronic logbook shortly after each test, enabling rapid feedback and collaboration between the SRF and cryogenic teams. The heat load measurements included various components: HTTS (high-temperature thermal shield), LTTS (low-temperature thermal shield), 2K isothermal and non-isothermal heat loads. Results were recorded both within the cryomodule and between the bayonet can supply and return. Measurements were conducted under different operating conditions such as "standard", "linac", and "simulated dynamic". Additionally, HTTS and LTTS heat loads were calculated in real time, allowing for the tracking of thermal stability and identification of changes during testing, both in steady-state and transient conditions. The results of this testing campaign not only provide valuable insights into the performance of the pHB650 cryomodule but also highlight best practices and lessons learned that will inform future cryomodule testing at PIP2IT. These include adopting automated tools for data analysis, refining real-time measurement capabilities, and emphasizing detailed pre-test planning. The framework established in this campaign aims to set an improved standard for cryomodule testing and heat load reporting in future cryomodule test campaigns.

Porwisiak, D. [Fermilab; Wroclaw Tech. U.]↗
DOE OSTI2025

Deep Koopman operators for causal discovery

Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relationships in nonlinear dynamics due to the presence of complex feedback mechanisms, timescale mixing, and nonstationarity. Thus, applying these methods to study causal dynamics in real-world systems, such as the Earth, is a major challenge. Addressing this shortcoming, we leverage deep learning and a Koopman operator-theoretic formalism to present a class of causal discovery algorithms. Kausal uses deep Koopman operator methods to approximate nonlinear dynamics in a linearized vector space in which traditional causal inference methods such as Granger causality can be more easily applied. Our idealized experiments demonstrate Kausal’s superior ability in discovering and characterizing causal signals compared to existing deep learning and non-deep learning state-of-the-art approaches. Finally, the successful identification of major El Niño and La Niña events in observations showcases Kausal’s skill to handle real-world applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data Interfaces for Automated Vehicle Services - A Municipality Perspective

As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗