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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Nuclear Structural Component Relevant Properties of Nickel-Based Alloys Produced via Additive Manufacturing

Idaho National Laboratory initiated examination of nickel-based alloys manufactured via three different additive manufacturing methods for potential applications in nuclear, high temperature structural components. The three methods analyzed included laser powder bed fusion, blown powder laser directed energy deposition, and wire-fed gas metal arc directed energy deposition. With the rapid push towards additive manufacturing, codes do not exist that definitively define what is or is not tolerable for each process and application, such as with conventional, wrought products. This report contains the initial work to understand possible manufacturing methods for high temperature alloys, and specifically, void formation, microstructure evolution, corrosion, and mechanical properties. To generate mechanical test data, specimens were tested irrespective of voids and microstructures were analyzed to better understand how to negate/improve these issues. The preliminary results showed major decreases in mechanical performance for material tested. Test specimens will continue to be produced to further improve each additive manufacturing processes, quantify void acceptance, and better understand the most suitable high temperature alloys receptive to additive manufacturing and high temperature nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2026

Intense cavitation-assisted electric discharge as a promising tool for water treatment

This study investigates interrelations between one-electrode Cavitation-Assisted Electric Discharge (CAED), two-electrode CAED, and recently discovered Intense CAED (I-CAED). The one-electrode CAED is a self-triggered nanosecond discharge with pulse energy in the micro-Joule range, which can be generated even by a DC voltage. I-CAED consists of a non-equilibrium part within a low-pressure cavitating region and a micro-spark traversing a liquid film. We hypothesize that CAED propagates from the high-voltage electrode as an ionization wave through bubbles of saturated vapor. Subsequently, the streamer-like discharges in the bubbles may form a continuous plasma channel. Inside the cavitating region, the plasma is strongly non-equilibrium, providing an ideal environment for generating chemically unstable species such as hydrogen peroxide (H 2 O 2 ). Plasma of I-CAED spark is characterized by high electron density and near-thermal equilibrium, emitting a continuous ultraviolet spectrum. The combination of these different discharge parts makes I-CAED in water a highly effective tool for the Advanced Oxidation Process, particularly in water disinfection. Experimentally demonstrated Electric Energy per Order value for disinfection of E. coli-contaminated water is as low as 0.135 ± 0.035 kWh/m 3 /order. Estimates show that the implementation of “dry electrodes” configuration reduces the erosion rate of the electrode material by at least one order of magnitude. Spectral analysis reveals that the continuum emission generated by I-CAED in proximity to metal electrodes deviates from the spectra of discharges spatially decoupled from the electrodes. We assume that this spectral divergence is attributable to blackbody-like emission originating from metallic nanoparticles form during the electrode's erosion process.

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

BLADE: An Automated Framework for Classifying Light Curves from the Center for Near-Earth Object Studies Fireball Database

Fireballs (bolides) are high-energy luminous phenomena produced when meteoroids and small asteroids enter Earth’s atmosphere at hypersonic speeds, often resulting in fragmentation or complete disintegration accompanied by significant energy release. The resulting bolide light curves capture temporal brightness variations as these objects traverse increasingly dense atmospheric layers, providing essential information on meteoroid entry dynamics, fragmentation behavior, and atmospheric energy deposition processes. The Center for Near-Earth Object Studies’ (CNEOS) continuously expanding fireball database offers a globally comprehensive archive of bolide events, including light curves and associated metadata. Events associated with infrasound detections allow direct correlations between acoustic signatures and light curve features, therefore enabling detailed analyses of fragmentation dynamics and energy deposition. Here, we introduce Bolide Light-curve Analysis and Discrimination Explorer (BLADE), a robust and high-fidelity framework specifically designed to analyze bolide light curves for objects detected from space. BLADE incorporates a processing pipeline integrating Savitzky–Golay filtering, prominence-based peak detection, and gradient analysis, enabling systematic identification and classification of fragmentation events and their associated energy release characteristics. Preliminary results demonstrate that BLADE reliably distinguishes distinct bolide behaviors, providing an objective, scalable methodology for characterization and analysis of large bolide light curve data sets. This foundational work establishes a novel pathway for advanced bolide research, with promising applications in planetary defense and global atmospheric monitoring. Future research should adopt an integrative approach combining CNEOS optical data with complementary infrasound measurements, further clarifying relationships between bolide energy deposition and acoustic signatures, thus refining our understanding of meteoroid and asteroid atmospheric entry processes.

Asteroids↗
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence

For leveraging wearable technologies to advance precision medicine, personalized and learning-based analysis of continuously acquired health data is indispensable, for which neuromorphic computing could provide the most efficient implementation of artificial intelligence (AI) data processing. For realizing on-body neuromorphic computing, skin-like stretchability is required, but yet to be combined with the suite of desired neuromorphic metrics, including linear, symmetric weight update, and sufficient state retention, for achieving high computing efficiency. Here, we report an intrinsically stretchable neuromorphic device based on an electrochemical transistor, which provides a large number (>800) of states, linear/symmetric weight update, excellent switching endurance (>100 million), good state retention (>10 4 s), together with high stretchability of 100% strain. Further integration into a prototype array successfully realized the implementation of vector-matrix multiplication even at 100% strain. Finally, we demonstrate the feasibility of implementing AI-based classification of health signals (as exemplified by electrocardiograms) with a high accuracy that is minimally influenced by the stretched state of the neuromorphic hardware. Finally, this work breaks the ground for combining AI data analysis into skin-like wearable electronics for achieving human-integrated/mimetic intelligent systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Radioactive Waste Sludge Washing and Demonstration of the Nitric-Glycolic Acid Flowsheet for Sludge Batch 10 Qualification

For each sludge batch that is processed in the Defense Waste Processing Facility (DWPF), the Savannah River National Laboratory (SRNL) performs qualification testing to demonstrate that the sludge batch (SB) is processible. During processing of SB9, DWPF will be transitioning from the Nitric-Formic Acid (NFA) flowsheet to the Nitric-Glycolic Acid (NGA) flowsheet. Thus, the qualification of SB10 was requested to only be performed using the NGA flowsheet. In order to qualify the batch for the NGA flowsheet, Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) cycles, designated SC-19, were performed using SB10 Tank 51 sample material. SRNL received Tank 51 material in the midst of Tank Farm washing. SRNL continued the washing in the SRNL Shielded Cells. The SRNL process included the addition of Sodium Reactor Experiment (SRE) material from H Canyon, simulating the transfer of SRE from H Canyon to Tank 51 during Tank Farm washing. The washed SB10 Tank 51 material, with SRE, was characterized prior to flowsheet qualification testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A single-component water-lean post-combustion CO 2 capture solvent with exceptionally low operational heat and total costs of capture – comprehensive experimental and theoretical evaluation

A comprehensive evaluation of a recently developed water-lean amine-based solvent, namely N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA), has been performed to analyze its post-combustion CO 2 capture performance. This evaluation comprises (1) fundamental characterization of the solvent–CO 2 interaction using vapor–liquid equilibria, kinetics and viscosity measurements; (2) process characterization of the CO 2 capture performance as measured in a laboratory scale continuous flow system and via Aspen Plus® simulation using a flue gas simulant; as well as (3) a full techno economic analysis of the capture process at industrial scale with corresponding projections of critical metrics. This paper summarizes the many parts of this comprehensive evaluation and shows how the various parts come together to empower validated conclusions about its process performance. Notably, it is projected that this solvent can operate at a regeneration heat rate of 2.0 GJ per tonne CO 2 for post-combustion capture, and at a total cost of capture of $50.6/tonne CO 2 . Overall, with further process optimization significant reductions in the capture cost are predicted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modular, Mobile Systems for the Treatment of Contaminated Water at Three Magnox Sites in the United Kingdom - 20421

Veolia Nuclear Solutions (VNS), and its mitigated entity, Veolia Nuclear Solutions Federal Services (VNSFS), are designing, overseeing the fabrication and installation, and supporting the commissioning of highly mobile and modular skid-based water treatment systems to assist in the decommissioning of three Magnox reactor sites based in the U.K. These Modular Active Effluent Treatment Plants (MAETP) will be deployed at the Chapelcross, Hinkley Point A, and Dungeness A sites to process contaminated water through the use of ion exchange systems and/or filtration units. At the time of writing, two systems have been installed and inactively commissioned, and the third system is expected to be delivered in 2020. Veolia has utilized a multi-national team of engineers, based in the UK and the US, to effectively leverage the best of Veolia expertise in the design and delivery of mobile, modular systems. The VNS team continues to refine the modular, mobile, and scalable approach to water processing system delivery, and has successfully patented the evolving technology for proprietary protection. The intent of this technology investment is to improve the functionality, mobility, and modularity, resulting in a system which can be adapted to a broad range of throughput and isotope removal requirements to achieve the most cost effective, flexible system possible for customers. Designing, fabricating, and factory-testing the modules off site followed by onsite installation, testing, start-up, and commissioning ensures expedited delivery and setup, as well as cost savings for nuclear applications. For the MAETP systems, VNS's previous nuclear site experience and ongoing Research and Development (R and D), combined with the resulting mobile/modular water processing systems developed and successfully deployed by VNS, have proven to be important inputs for the successful end-design of the systems, materials selection, system testing and commissioning. The lessons learned in the development and continued enhancement of the patented systems described in this paper will prove useful in advancing and deploying future systems for other nuclear sites, where rapid customizing and cost-effective implementation are critical factors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles

Here tritium accountancy in the fusion fuel cycle is a significant concern for the operation of commercial devices. It is expected that a limit on the maximum amount of tritium inventory in the system will be implemented, meaning that accountancy of the tritium inventory in the fuel cycle will need to be as accurate as possible. This is difficult since not all locations along the fuel cycle can benefit from the implementation of a tritium accountancy sensor and measurements will inherently contain error. Commercial fusion plants will also operate continuously, challenging current accountancy techniques that rely on static processes in well-controlled environments. In this paper a simple fusion fuel cycle concept is defined and the tritium inventory over time of each component is modelled using the Euler Approximation of a series of differential Equations in Python. This information is then used to simulate sensor measurements at specific points on the fuel cycle and then passed through a Kalman Filter (KF) to improve the accuracy of the true measurement of the sensors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

After the hurricane: Validating a resilience assessment methodology

With increasing utility grid outages in the United States, there is growing interest in assessing risk and developing mitigation strategies to reduce the impact of grid outages. Working with the U.S. Air Force, the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) developed a replicable energy resilience assessment methodology and investment decision tool to: (1) identify and score hazards and vulnerabilities at the site level; (2) analyze risks to energy infrastructure; and (3) identify and prioritize energy resilience investments. This work improves on existing resilience assessment methodologies and tools by combining a bottom-up, all-hazards assessment methodology with top-down geographic information system mapping capabilities to provide an innovative, dynamic tool for identifying and prioritizing actionable solutions. This process combines probabilistic forecasting with an iterative approach for continuously updating and reassessing risks to address temporal dynamism. Relationships among systems are modeled and visualized to estimate the effectiveness of resilience actions across multiple interdependent systems and inform financial priorities through cost-difficulty-impact trade-offs. The approach is validated in a case study at Tyndall Air Force Base (AFB) in Florida, which experienced a Category 5 hurricane in 2018. The risks and mitigation strategies identified pre-hurricane are compared with post-hurricane, realized impacts. The assessment effectively identifies risks and actions to increase site energy resilience, but the methodology can be enhanced though greater consideration of the interdependencies between the energy system and related systems like transportation, communication, and food/water systems, which impact the recovery of the energy system and the base.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evolution from topological Dirac metal to flat-band-induced antiferromagnet in layered K x Ni 4 S 2 (0 ≤ x ≤ 1)

Condensed matter systems with coexisting Dirac cones and flat bands and a switchable control between them within a single system are desirable but remarkably uncommon. Here, we report a layered quantum material system, K x Ni 4 S 2 (0 <= x <= 1), that simultaneously hosts both characteristics without involving typical Kagome/honeycomb lattices. Enabled by a topochemical K-deintercalation process, the Fermi surface can be fine-tuned continuously over a wide range of energies. Consequently, a non-magnetic Dirac-metal state with a topological nontrivial Z 2 index of 1;(000), supported by first-principles calculations and high mobility up to 1,471 cm 2 V -1 s -1 , is observed on the K-rich x = 1 side, whereas a flat-band-induced antiferromagnetic state with T N up to 10.1 K emerges as the K-content approaches 0. The K x Ni 4 S 2 system offers a versatile platform for exploring emerging phenomena and underscores aviable pathway for in situ control of quantum materials dominated by Dirac cones, flat bands, and their interplay.

dirac metal↗
DOE OSTI2022

Compositionally graded aluminum-silicon alloy fabricated via friction extrusion

Herein, a novel solid-phase gradient alloying technique was developed for a high throughput composition-microstructure-mechanical property assessment. Friction extrusion of a two-piece billet setup facilitated the formation of a seamless compositionally graded aluminum (Al)-silicon (Si) extrudate rod, with Si concentration in Al matrix gradually increasing from ~1 at% at one end to ~7 at% at the other end of the rod. We observed a strong dependence of the Al grain size, morphology, and texture on the Si concentration. The Si particles pinned the recrystallized grain boundaries resulting in refined (< 10 µm) and textured grains in the high Si content region, while the Al grains in the low Si region are equiaxed, randomly textured, and with a larger average size (~50 µm). The hardness of Al-Si gradient extrudate increases by ~50% percent with a 6 at% increase in Si content due to Hall Petch and particle strengthening effects. Our approach demonstrates the use of a solid phase processing technique to successfully produce a defect-free continuously compositionally graded bulk components thus overcoming the limitations of porosity and material heterogeneities commonly observed in other techniques such as by additive laser deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

3D printing synthesis of catalysts

The catalyst industry generates approximately $20 billion every year globally and plays a major role in the energy production sector. Traditional industrial catalysts (e.g., pellets) typically have mass and heat transfer limitations, and cause a pressure drop in continuous-flow reactors, lowering the efficiency of the catalytic processes. 3D printing technology has evolved rapidly over the past decade, and the 3D printing of catalysts with desired geometries can address many of the above-stated challenges with traditional catalysts. However, challenges remain to be addressed and opportunities remain to be explored before the full potential in the design and 3D printing of novel catalysts can be realized. Herein this article reviews the recent development in the 3D printing of catalysts. It summarizes the 3D printing design, dimension, property, and performance of 3D printed catalysts. The applications of 3D printed catalysts, such as reforming, wastewater treatment, and CO 2 capture and removal, are discussed, with a techno-economic analysis and life cycle analysis. Future research directions and opportunities for 3D printing of catalysts are also highlighted.

25 ENERGY STORAGE↗
DOE OSTI2025

Mineral Scale Formation during Crossflow Reverse Osmosis at Constant Flux and Constant Transmembrane Pressure Conditions

Mineral scale formation on membrane surfaces is a significant challenge in reverse osmosis water purification. Laboratory fouling experiments are typically run such that the transmembrane pressure (TMP) is fixed, and the permeate flux decreases over time as scales accumulate on the membrane surface. However, this change in flux means that the hydrodynamic conditions at the membrane surface are continuously changing, which could affect crystallization and foulant deposition processes. Operating under constant permeate flux conditions, in contrast, is advantageous because it keeps the hydrodynamic conditions relatively consistent, making it possible to compare how membrane properties (e.g., surface chemistry) affect fouling propensity. Industrial reverse osmosis operations are not run strictly in either constant TMP or constant flux mode; while they may start at a constant TMP, feed pressure may be periodically adjusted to maintain permeate water production within a specified range. Here, the scarcity of constant permeate flux reverse osmosis scaling experiments reported in the literature frustrates efforts to compare membrane fouling processes under constant TMP and constant flux conditions. For the first time, the evolution of the fouling layer resistance was compared as a function of cumulative permeate volume per membrane area during constant TMP and constant flux reverse osmosis filtrations. Scaling experiments were conducted by challenging commercial reverse osmosis membranes with a model feed solution nearly saturated with calcium sulfate dihydrate (gypsum). At low fluxes, the increase in fouling layer resistance was quantitatively similar for the two operational modes. In contrast, at high fluxes, the fouling layer resistance increased more rapidly in constant flux filtration than in constant TMP filtration. The mechanism of scale formation in constant TMP and constant flux operation was self-limiting and self-reinforcing, respectively.

36 MATERIALS SCIENCE↗