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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Best Practices for Resilience in Smart Grid-Interactive Efficient Buildings

The Federal Energy Management Program (FEMP) supports federal agencies' energy decisions with information and guidance on design, funding, and operations to ensure federal buildings are efficient and resilient. The modernization of building infrastructure and the evolution of buildings to support decarbonization involves complex implementation of multiple components across several systems. This includes energy-efficient equipment, on-site energy generation and storage systems, and control systems. These systems have operational modes that can operate more efficiently if they are able to behave responsively to the conditions of the electrical grid. These grid-interactive efficient buildings (GEB) allow facilities to manage power demand according to operational constraints and market signals issued by grid operators. With proper design and planning, these same capabilities have the potential to enable building and facility resilience - coordinating with microgrids, maintaining power on critical circuits to sustain essential operations, and monitoring building health and safety status during an outage. By managing the load of buildings, GEBs can also reduce the cost of backup generation and make better use of renewable power sources on site. This document outlines some of the processes and considerations to guide the design and operation of GEBs in ways that promote facility resilience.

building decarbonization↗
DOE OSTI2024

A FeCrAl-Al2O3 Composite Produced via Laser Powder Bed Fusion of a Mixed Powder for Porous Catalyst Scaffolds

This study proposes a novel approach for synthesizing and etching bicontinuous FeCrAl-Al2O3 composites as a means for replacing FeCrAl foams as catalyst scaffolds in bio-driven alcohol reactors for jet-fuel production. Conventional FeCrAl foams suffer from poor availability and consequent high costs. New additive manufacturing techniques provide an opportunity to produce tailored foams in reasonable times and at acceptable costs. This research aimed to generate a porous FeCrAl structure by etching a bicontinuous FeCrAl-Al2O3 composite produced by laser powder bed fusion of amalgamated FeCrAl and Al2O3 powders. The composite powder for laser powder bed fusion is created by ball-milling FeCrAl and Al2O3 powders. This research focuses on achieving a bi-continuous FeCrAl-Al2O3 structure, essential for the selective removal of the ceramic phase. The influence of laser processing parameters on the microstructure was examined across a range of laser powers (60-120 W) and scan speeds (100-400 mm/s), showing that higher powers and speeds produce finer metal struts. A bi-continuous microstructure was consistently obtained, marking a key achievement. The Al2O3 removal process involved a two-step etching method using hydrochloric and phosphoric acids, tested across various etching times. The alumina phase was reduced from 36 vol% to 17 vol% (corresponding to an increase in porosity from 24 vol% to 43 vol%), showing the potential for use as a porous catalyst scaffold. This research demonstrates the potential for using additive manufacturing to produce porous FeCrAl structures capable of replacing hard-to-source FeCrAl foams.

Son, Kwangtae↗
DOE OSTI2025

A machine learning framework for accurate and robust analysis of radiation detector pulses

The microscopic properties of atomic nuclei are used to study various scientific questions. They are essential for understanding the fundamental forces of nature and the chemical evolution of the universe. Detecting decay radiation from radioactive nuclei makes it possible to probe these fundamental nuclear properties. Detector waveform traces may contain additional information about the radiation. Generally, advanced signal processing techniques are needed to extract this additional information, often involving fitting the waveform with model response functions using non-linear least-squares optimization with second-order gradient methods. While this is a powerful technique, it is also computationally expensive, leading to slow processing time, which scales with the volume of data. To address this problem, we have developed a machine learning (ML) approach that infers the characteristics of traces from a model detector response function. In particular, we are interested in classifying whether a single recorded trace consists of one or two pulse constituents and estimating the pulse parameters. Furthermore, our proposed ML method can precisely extract the pulses’ parameters, such as energy and timing information, and accurately classify the pulse multiplicity of a trace. Unlike non-learning-based approaches, our ML approach uses neural networks that are significantly faster at inference, as they do not require any optimization during this stage.

Curve fitting↗
DOE OSTI2024

Models implemented in the methodological approach to design the initial STEP first wall contour

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is managing the loads on the first wall within acceptable limits. Therefore, the conceptual design development of the STEP Prototype Power plant needs to be based on load estimates derived using legitimate plasma physics assumptions through dynamic and flexible tools. The current design foresees the STEP main chamber first wall to withstand steady-state heat loads of up to ~1 MW/m 2 , excluding critical regions expected to receive higher heat loads such as the baffle regions approaching the divertors. These critical areas will require ad hoc assessments and will be designed with the presence of limiters. This article focuses on the models and methodology adopted for designing the 2-D poloidal contour of the STEP first wall, based on the anticipated charged particle and radiation heat loads during normal operation. Firstly, the models adopted for calculating the charged particle and radiation heat loads are introduced. The first model is validated through benchmarking against the particle tracing code SMARDDA, while the second model is verified by comparing it with data from the MAST-U experiment. Secondly, the model used to design the 2-D first wall contour according to the heat loads is explained. We acknowledge that this preliminary design stage assumes certain simplifications, notably an axisymmetric geometry, for computational efficiency and clarity in presentation. It is understood that subsequent design phases will address the complexities of real-world engineering, including non-axisymmetric effects, transient plasma scenarios, and the impact of disruptions on the first wall design. Finally, an automatic procedure based on these models is presented for defining the 2-D poloidal contour of the STEP first wall to minimize heat loads, taking into account the need to radiate most of the alpha-particle and auxiliary heating power. Here, by providing an overview of the models, methodology, and an automatic procedure, this paper contributes to the design process of the STEP first wall, addressing the engineering challenges associated with fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Evaluation of Iron‐Phosphate Glass–Ceramic Waste Form for Electrorefiner Salt Waste Simulant Dechlorinated With Phosphoric Acid

The importance of glass and glass–ceramic nuclear waste forms has been reaffirmed in recent years by the growing interest in nuclear power as a reliable energy source. Determination of processing methods for the disposal of halide-containing wastes will be essential for the advancement of nuclear technologies such as non-aqueous fuel reprocessing. Phosphate-based dechlorination and subsequent vitrification of radioactive salt waste into an iron-phosphate waste form have been identified as a potential processing scheme for electrochemical processing waste. The impact of H 3 PO 4 -based dechlorination of complex salt mixtures on the vitrification process and structure of the final iron-phosphate waste form has not yet been investigated. In this work, iron-phosphate glass–ceramics were made from simulant salt waste (48LiCl–33KCl–19NaCl mol%) dechlorinated with the H3PO4-based method. The glass-forming region was compared to that of traditionally prepared Na 2 O–Fe 2 O 3 –P 2 O 5 systems. For a candidate glass-forming composition, the processing scheme presented here was determined to favor Fe 3+ species. The O/P molar ratio was consistent for the candidate composition when dechlorinated at 400°C and 600°C in air and argon environments, indicating glass network connectivity was maintained despite variations in processing parameters. The results presented here validate processing schemes requiring iron-phosphate waste form synthesis following H 3 PO 4 -based dechlorination.

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

Corroborating VNA and thermal measurements of transmission loss on the DIII-D ECH waveguide system

Electron cyclotron heating (ECH) and current drive (ECCD) will play a large role in tokamak-based fusion reactors. At the DIII-D tokamak, 110 GHz microwaves injected into the plasma can provide core heating and current drive as well as impurity control, neoclassical tearing mode mitigation, and breakdown assistance. Understanding the physics of these processes relies on accurate estimates of injected ECH power. DIII-D’s ECH system consists of six MW-class Microwave Power Products (MPP) gyrotron microwave sources. Operating the gyrotrons far from the tokamak removes them from magnetic field interference, so 31.75 mm inner-diameter corrugated waveguides transmit the microwave power the 80 m from the gyrotrons to steerable launchers in the tokamak chamber. Estimates of injected power rely on knowing the generated power at the source and then subtracting transmission loss. Conventional transmission loss measurements based on calorimetric dummy loads are onerous and only possible during extended maintenance periods. This work examines two tools that provide more flexibility for the transmission loss measurements. Furthermore, a resistive temperature detector (RTD) array installed along a waveguide measures heat lost to the transmission line, and low power time domain reflectometry (TDR) measurements with a vector network analyzer (VNA) allows loss measurements without burdensome hardware modifications.

ECH↗
DOE OSTI2025

Machine learning models for volumetric swelling in uranium nitride

Machine learning methods are applied to predict the volumetric swelling rate of the nuclear fuel uranium nitride (UN) over various temperatures, irradiation conditions, and power densities. Both kernel-based methods and symbolic regression models for UN swelling are developed and compared with multiple experimental datasets. We find that the UN pellet geometry and dimensions must be taken into account to accurately model swelling behavior. Strong agreement is observed between the developed machine learning models and the data. The predictive error generated by the machine learning models improves on empirical models taken from the literature. Sensitivity analysis is performed to determine which properties such as temperature, burnup, and power density, are most important in the swelling process. We find that machine learning can be used to quickly develop accurate swelling models for nuclear materials. In conclusion, the presented results illustrate the potential of machine learning to determine volumetric swelling in UN.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Design of an 8-channel 40 GS/s 20 mW/Ch waveform sampling ASIC in 65 nm CMOS

One picosecond timing resolution is the entry point to signature based searches relying on secondary/tertiary vertices and particle identification. We describe PSEC5, an 8-channel 40 GS/s waveform-sampling ASIC in TSMC 65 nm process targetting one picosecond resolution at 20 mW power per channel. Each channel consists of four fast and one slow switched capacitor arrays (SCA), allowing for picosecond time resolution combined with a long effective buffer. Each fast SCA is 1.6 ns long and has a nominal sampling rate of 40 GS/s. The slow SCA is 204.8 ns long and samples at 5 GS/s. Recording of the analog data for each channel is triggered by a fast discriminator capable of multiple triggering during the window of the slow SCA. To achieve a large dynamic range, low leakage, and high bandwidth, the SCA sampling switches are implemented as 2.5 V nMOSFETs controlled by 1.2 V shift registers. Stored analog data are digitized by an external ADC at 10 bits or better. Specifications on operational parameters include a 4 GHz analog bandwidth and a dead time of 20 microseconds, corresponding to a 50 kHz readout rate, determined by the choice of the external ADC. PSEC5 has been submitted for fabrication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Enhancing Sensitivity in Targeted Single-Cell Proteomics by Coupling a Dual Ion Funnel Interface with Triple Quadrupole Mass Spectrometer

Single-cell proteomics (SCP) has emerged as a powerful approach for understanding cellular heterogeneity and biological processes at unprecedented resolution. However, the extremely limited protein content of individual cells (femtogram to picogram levels) pushes current mass spectrometry instrumentation to its sensitivity limits, creating a critical analytical bottleneck. While selected reaction monitoring (SRM) using triple quadrupole (QqQ) instruments 1 offers advantages in sensitivity and reproducibility for targeted proteomics quantification, SRM still struggles with sensitivity for quantification of moderate- or low-abundance proteins from single-cell sample amounts. Here, we report the development and systematic evaluation of a dual ion funnel interface designed to address the sensitivity limitation by significantly enhancing ion transmission efficiency in commercial QqQ mass spectrometers. The dual ion funnel interface, composed of a curved S-funnel followed by a conventional ion funnel, improves ion transmission efficiency while reducing chemical noise through selective ion focusing. The performance of the dual ion funnel interface was systematically compared to standard interface on a TSQ Vantage platform across samples with different levels of complexity. The dual funnel interface demonstrated to provide up to 25-fold improvement in sensitivity across a wide range of protein concentrations in different biological matrices (low complex mouse macrophage and high complex human cells). Critically, enhanced sensitivity was accompanied by increased analytical reproducibility with lower coefficient of variations. Most importantly, the dual funnel interface enabled reliable quantification of low-abundance proteins that were barely detectable or not detected by the standard interface, extending analysis to single-cell equivalent amounts while maintaining excellent reproducibility. These results demonstrate that the dual funnel interface addresses the critical bottleneck in quantitative targeted proteomics, providing a technological foundation for ultrasensitive targeted SCP that requires both high sensitivity and robust quantitative performance.

Min, Sehong↗
DOE OSTI2025

Density Functional Tight-Binding Enables Tractable Studies of Quantum Plasmonics

Routine investigations of plasmonic phenomena at the quantum level present a formidable computational challenge due to the large system sizes and ultrafast time scales involved. This Feature Article highlights the use of density functional tight-binding (DFTB), particularly its real-time time-dependent formulation (RT-TDDFTB), as a tractable approach to study plasmonic nanostructures from a quantum mechanical purview. We begin by outlining the theoretical framework and limitations of DFTB, emphasizing its efficiency in modeling systems with thousands of atoms over picosecond time scales. Applications of RT-TDDFTB are then explored in the context of optical absorption, nonlinear harmonic generation, and plasmon-mediated photocatalysis. We demonstrate how DFTB can reconcile classical and quantum descriptions of plasmonic behavior, capturing key phenomena such as size-dependent plasmon shifts and plasmon coupling in nanoparticle assemblies. Lastly, we showcase DFTB’s ability to model hot carrier generation and reaction dynamics in plasmon-driven H2 dissociation, underscoring its potential to model photocatalytic processes. Collectively, these studies establish DFTB as a powerful, yet computationally efficient tool to probe the emergent physics of materials at the limits of space and time.

Electrical energy↗
DOE OSTI2024

Probing shocks interacting with radiation waves with the Radishock experiment

Both radiation flows and shocks have been extensively studied in the laboratory in the past few decades due to their critical roles in many astrophysical and high-energy density physics processes. In the Radishock experiment, a halfraum-powered radiation wave is driven into a low-density foam and interacts with an ablatively driven, counter-propagating shock. The interacting waves produce a spike in energy density with a temperature greater than the local temperature of the individual waves. As in the successful predecessor experiment, COAX, the primary diagnostic uses absorption spectroscopy at many locations down the cylindrical target, enabling a spatial temperature inference of the radiation wave and its interactions with the shock. Combined with a radiography diagnostic that is capable of imaging the shock and interaction features, we are able to study and inform model predictions of the interaction spike phenomenon. We describe the underlying physics behind the shock interactions with the radiation front and the implications of this experimental study for a broad range of astrophysical phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design-Point Techno-Economics of Brayton Cycle PTES for Combined Heat and Power

Pumped thermal energy storage (PTES) systems are grid batteries that use heat pumps to create both hot and cold thermal energy stores when there is excess electricity and then use a power cycle to convert the thermal energy into electricity when there is demand for electricity. In normal operation, Joule-Brayton PTES discharges low-grade heat at temperatures useful for thermal energy consumers like district and industrial heating. Furthermore, PTES designs, like conventional combined heat and power (CHP) technology, can be modified to sacrifice some round-trip efficiency RTE to increase the temperature of heat rejection. This paper uses design-point performance and cost models that provide a detailed understanding of the efficiency and cost trade-offs of rejecting heat at various temperatures in ideal-gas Brayton PTES configurations. First, we keep the heat rejection in its nominal location in the PTES system: in the discharge cycle after the low-pressure exit of the recuperator before the cold-storage heat exchanger. Next, we move the heat rejection to the discharge turbine exit. We define design-point metrics that isolate both the cost and performance penalty associated with the hotter heat rejection and attribute it exclusively to the heat economic metrics. Finally, we estimate the performance of electric heater technology to generate heat at equivalent temperatures. We find that the levelized cost of heat, including the cost of thermal energy storage (TES) buffering the PTES and heat off-taker, compares favorable versus electric technologies and is less than the cost of natural gas for low temperature scenarios and competitive with the cost of natural gas in some regions of the contiguous United States in high temperature scenarios.

CHP↗
DOE OSTI2025

Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a "sniff-and-seek" mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus-molybdenum disulfide (bP-MoS2) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

Zhang, Dehui↗
DOE OSTI2025

Efficient, reliable and cost-effective reversible solid oxide cell technology for hydrogen and electricity production

This report summarizes the work performed by University of California San Diego (UCSD) – OxEon Energy LLC (OxEon) for the U. S. Department of Energy/National Energy Technology Laboratory (DOE/NETL) under Cooperative Agreement DE-FE0031940 entitled “Efficient, Reliable and Cost Effective Reversible Solid Oxide Cell Technology for Hydrogen and Electricity Production”. This reversible solid oxide cell (RSOC) technology has two main novel elements: (i) a compact and low-cost stack architecture that consists of multi-cell cell modules in electrical parallel and series connections and (ii) high performance and fuel-flexible reversible cells with electrodes or all components made by sputtering deposition process for efficient operation in both fuel cell (power generation) and electrolysis (hydrogen production) modes.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗