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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

AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA---Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. Finally, to improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Haze Mitigation in High-Resolution Satellite Imagery using Enhanced Style-Transfer Neural Network and Normalization Across Multiple GPUs

Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.

Park, Byung↗
DOE OSTI2026

Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based X-ray microscope

With increasing interest in studying biological systems across spatial scales—from centimeters down to nanometers—histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here, we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterization of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual Hematoxylin and Eosin (H&E) staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.

3D virtual histology↗
DOE OSTI2024

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2025

Performance Evaluation of Low-Cost Condensers Immersed in the Water Tank of a Heat Pump Water Heater

This paper reports the effect of low-cost immersed condensers on the performance of a heat pump water heater (HPWH) that does not require a water-circulating pump. The proposed design is based on the immersion of an L-style condenser coil through an opening in the top of the water tank. The novel design aims to eliminate the need for the water-circulating pumps, thereby substantially improving efficiency and reducing costs and maintenance of the HPWH system. Comprehensive computational fluid dynamics (CFD) simulations and experimental tests were performed, and the results showed that the CFD simulations were close to the experimental data. Further results confirm that the L-style condenser can introduce buoyancy-driven flow and eliminate temperature stratification in the studied HPWH water tank, thereby improving the heat transfer coefficient and coefficient of performance of the HPWH. The testing results further showed that the L-style condenser enabled more than 53% higher coefficient of performance and 400% higher heat transfer coefficient compared with the straight vertical condenser, and achieved much faster water heating. Furthermore, the effects of immersed condensers with different circuits on the HPWH performance were evaluated and compared. All results indicate that the current energy-saving and inexpensive HPWH technology is technically valid, and the benefits of performance improvement and low cost are attractive in future HPWH design.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
DOE OSTI2022

A Simulation-based Method for Correcting Mode Coupling in CMB Angular Power Spectra

Modern cosmic microwave background (CMB) analysis pipelines regularly employ complex time-domain filters, beam models, masking, and other techniques during the production of sky maps and their corresponding angular power spectra. However, these processes can generate couplings between multipoles from the same spectrum and from different spectra, in addition to the typical power attenuation. Within the context of pseudo-C ℓ based, MASTER-style analyses, the net effect of the time-domain filtering is commonly approximated by a multiplicative transfer function, F ℓ , that can fail to capture mode mixing and is dependent on the spectrum of the signal. To address these shortcomings, we have developed a simulation-based spectral correction approach that constructs a two-dimensional transfer matrix, ${J}_{{\ell }{\ell }^{\prime} }$, which contains information about mode mixing in addition to mode attenuation. We demonstrate the application of this approach on data from the first flight of the Spider balloon-borne CMB experiment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Measurement of the Neutron Electromagnetic Form Factors at High Momentum Transfer Using a Polarized 3He Target at Jefferson Lab

Ever since the composite structure of nucleons was uncovered, many experiments have been commissioned with the purpose of extracting parameters that shape our understanding of this structure. Form factors are powerful tools that rest at the forefront of the nucleon structure study. In the non-relativistic limit, they describe the magnetic and charge distributions inside the nucleon. The GEn-II experiment, which ran in Hall A of Jefferson Lab as a part of the SuperBigbite Spectrometer (SBS) program, aimed to extract the ratio of the neutron’s electric to magnetic Sachs form factors (GnE/GnM. This was accomplished using the double polarization technique: scattering polarized electrons with initial energies up to 8.45 GeV off high-density polarized 3He targets. The experiment was a coincidence measurement, with the scattered electrons detected in the BigBite Spectrometer and the recoil nucleons in a hadron calorimeter, a part of the SuperBigbite Spectrometer. The convection-style target cells used in this experiment have a 60cm long target chamber, an increase from the 40 cm cell used in the most recent polarized 3He target experiment. In addition, the cells were optically pumped from two sides of the pumping chamber. These improvements in design and functionality introduced world-record-breaking luminosity. This thesis presents preliminary results of the GEn-II experiment for the following 4-momentum transfer (Q2) values: 2.93, 6.76, and 9.78 GeV2, in addition to target performance and preliminary polarimetry results.

Presley, Hunter [Univ. of Virginia, Charlottesvill↗
DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantum communication with itinerant surface acoustic wave phonons

Abstract Surface acoustic waves are commonly used in classical electronics applications, and their use in quantum systems is beginning to be explored, as evidenced by recent experiments using acoustic Fabry–Pérot resonators. Here we explore their use for quantum communication, where we demonstrate a single-phonon surface acoustic wave transmission line, which links two physically separated qubit nodes. Each node comprises a microwave phonon transducer, an externally controlled superconducting variable coupler, and a superconducting qubit. Using this system, precisely shaped individual itinerant phonons are used to coherently transfer quantum information between the two physically distinct quantum nodes, enabling the high-fidelity node-to-node transfer of quantum states as well as the generation of a two-node Bell state. We further explore the dispersive interactions between an itinerant phonon emitted from one node and interacting with the superconducting qubit in the remote node. The observed interactions between the phonon and the remote qubit promise future quantum-optics-style experiments with itinerant phonons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Thermal Optimization of a Silicon Carbide, Half-Bridge Power Module

This project describes the modeling process to design the packaging and heat exchanger for a half-bridge wide-bandgap (WBG) power semiconductor module. The module uses two silicon carbide, metal-oxide-semiconductor field-effect transistor (MOSFET) devices per switch position that are soldered to an aluminum nitride, direct-bond copper (DBC) substrate. A baseplate cooling configuration (e.g., no thermal grease) is used along with a water-ethylene glycol, jet-impingement-style heat exchanger. The heat exchanger was designed to be fabricated using prototyping equipment from the National Renewable Energy Laboratory, complies with automotive standards (for minimal channel sizes, flow rates, and coolant), and considers reliability aspects (i.e., erosion/corrosion). Device-scale computational fluid dynamics (CFD) is used first to design the slot jet impingement cooling configuration and compute the effective heat transfer coefficient (HTC) of the concept. The computed HTCs are then used as boundary conditions for a finite element study to optimize the package geometry (e.g., device layout and baseplate thickness) to minimize thermal resistance and minimize temperature variation between the module's four devices. Finally, a fluid manifold is designed to generate the slot jets and cool the devices. Module-scale CFD predicts a relatively low junction-to-fluid thermal resistance of 16.7 mm2 K/W, a 1.4 degrees C temperature variation between devices, and a total pressure drop of 5,860 Pa (0.85 psi) for the design. The thermal resistance of the module design is about 67% lower than the 2015 BMW i3 power electronics/modules thermal resistance.

DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Improving Cost and Efficiency of the Scalable Solid Oxide Fuel Cells Power System

The objective of this project was to design and develop a 20kW range small-scale solid oxide fuel cells (SOFC) power system for applications such as data centers and commercial buildings. The original plan included a 5,000 hours demonstration and a Techno-Economic Analysis (TEA) which were dropped as part of project termination. The original project plan was to use a stack with a cross-flow cell design which had previously been tested for 500 hours at a community college in Malta, NY. However, it was decided to move to the advanced R-SOFC co-flow cell developed under Department of Energy Award DE-FE0031971. The advanced cell design has the advantage of a larger active area for the same manufacturing footprint which results in fewer required cells for the same stack power, hence a higher volumetric power density (kW/L) and lower cost per kW than the original cross-flow cell design. A full SOFC system Simulink model was developed and calibrated with testing data from a fuel cell stack and BOP (balance of plant) components. The simulation results from the calibrated model showed an acceptable match with the experimental data. A structural analysis conducted for various load scenarios indicated no high stress areas for all spatial directions. Major electrical system components were acquired, built and successfully tested. System sensors were verified and validated against controls. Safety checks, a diagnostic check, PID tuning, and control software commissioning tasks were also conducted. The power electronics prototype was delivered and trial testing completed. Balance of Plant component testing and simulation work was conducted to characterize Reformer-Heat Exchanger heat transfer and backpressure and reformer catalyst methane conversion and product selectivity. Simulations were conducted to design the Anode and Cathode fluid passages and size the air-air and fuel-fuel heat exchangers. A Burner operation map was created from test data and the Anode Gas Recirculation blower was tested to evaluate its durability. The SOFC system used a horizontal style design where components sit directly on a casting with a direct connection to the skid. This design has efficient packaging and a small footprint with approximate dimensions of 750 mm x 700 mm x 1700 mm. An SOFC system was built and successfully tested at the Malta, NY facility The system for over 500 hours under load of which over 300 hours was at full load of 20 kW.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Preparation of a membrane-sealed cell for studying catalyst nanoparticles in flowing gas with high vacuum x-ray photoelectron spectrometer

Here a sealing-style x-ray photoelectron spectroscopy study of the surface of a 1.0 wt. %Ni/TiO2 nanoparticle catalyst in a flowing mixture of CO and O2 at 1 bar was performed with a graphene membrane-sealed Si3N4 window-based miniature cell. We report the details on how a commercial Si3N4 window is modified before assembling a graphene membrane, how single-layer graphene membranes are transferred from their metal supports to the modified Si3N4 window, how a modified Si3N4 window covered with a double-layer graphene membrane is assembled onto a blank cell cap, how a nanoparticle catalyst is introduced to the cell cap and then the cell cap is installed onto a cell body to form a complete reaction cell, and how a complete cell is interfaced with a high vacuum chamber of an XPS system before an XPS study of 1.0 wt. %Ni/TiO2 catalyst surface in a flowing mixture for 0.2 bar CO and 0.8 bar O2 is performed. How the characterization of a catalyst using this type of graphene membrane-sealed Si3N4 window-based miniature cell is relevant to the finding of the actual surface chemistry of a catalyst during catalysis is discussed.

Instruments & Instrumentation↗
DOE OSTI2023

Advancements in conventional and 3D printed feed spacers in membrane modules

Plate & frame and spiral-wound modules are used for gas separation, pervaporation, reverse osmosis, nanofiltration, ultrafiltration, microfiltration, electro-dialysis, electro-deionization, membrane distillation and forward osmosis membrane processes. Feed channel spacers are an integral part of both module types – providing mechanical support for a cross-flow channel through the module and, in most cases, promoting mixing to enhance mass transfer to reduce concentration polarization and fouling. However, enhanced mass transfer comes at a cost of increased hydraulic pressure losses and stagnant zones wherever a spacer filament touches a membrane surface. Further, these stagnant zones exacerbate membrane fouling and make cleaning more difficult. Efforts to improve feed spacer performance largely focus on adjusting the chemistry or geometry of the spacer to mitigate these challenges. Additive manufacturing (i.e. 3D printing) offers new degrees of freedom in feed spacer design and production, which opens up a new area of research in membrane technology. This review critically assesses the peer-reviewed literature on conventional net- or mesh-style feed spacers in addition to various novel spacer geometries and chemistries produced via 3D printing. We further review and evaluate conventional spacer manufacturing methods and discuss advantages and disadvantages of 3D printed spacers.

3D printing↗
DOE OSTI2021

Alternative All-Metal Scroll Pump Design

In tritium process systems, vacuum pumps are typically used to evacuate volumes and piping, as well as transfer gas to other parts of the process. This was done using the combination of an all-metal scroll pump with a metal bellows backing pump. The all-metal scroll pump, manufactured by Normetex, has been unavailable since 2012, and efforts continue to find a suitable replacement. The main obstacle is finding a pump that has no oils or polymer components, which degrade when exposed to tritium and introduce corrosive and/or hazardous impurities into the process. There have been development efforts to design an all-metal scroll pump design alternative to the Normetex style. The first generation was fabricated out of machined stainless steel, but the thermal management and increased weight were not ideal. To mitigate the weight, an aluminum version was designed. The tritium holdup on untreated aluminum surfaces can be greater than stainless steel, so the wetted aluminum surfaces were electroless plated in nickel and then electroplated in gold. The development effort outlined in this report details the design of an improved version of the stainless steel version.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Long‐term growth and persistence of granitic inselbergs in a semi‐arid cratonic landscape

Granitic inselbergs are enduring landforms that punctuate planation surfaces in tectonically stable settings, yet the mechanisms and timescales of their persistence remain debated. Here, in this study, we present a multinuclide cosmogenic dataset ( 10 Be, 26 Al and 21 Ne) from bedrock, fluvial sediments and planation surfaces in the semi-arid Central Ceará Domain, north-eastern Brazil, to quantify denudation rates and reconstruct the long-term dynamics of residual relief formation. This triple-nuclide approach enables independent validation of denudation contrasts and long-term exposure histories, providing quantitative constraints on inselberg evolution that a single-nuclide approach could not resolve. Bedrock denudation rates (1–4 m Myr −1 ) are systematically lower than basin-wide rates (8–14 m Myr −1 ), revealing a regional pattern of vertical differential denudation. When combined with summit elevations and surface exposure ages of up to 5 Myr, these contrasts suggest that cumulative relief growth has been operating over timescales of tens of millions of years (1–57 Myr). Morphological and structural observations further indicate localized mass wasting and heterogeneous erosion styles, modulated by rock fabric and joint density. Cosmogenic nuclide inventories in detrital sediments reveal partial reworking from older sediment stocks, including surface pebbles with exposure ages up to 5 Myr, which record multistage of burial and re-exposure histories and reflect episodic remobilization in low-connectivity, semi-arid catchments. Rather than static relicts of ancient surfaces, inselbergs in this cratonic setting emerge as dynamic landforms actively sustained by slow but persistent differential denudation. These results provide new constraints on long-term cratonic landscape evolution and underscore the interplay between lithological resistance, structural inheritance and sediment transfer processes in sustaining topographic relief. They also call for a critical reassessment of classical inselberg models, suggesting that hybrid mechanisms, rather than single-process paradigms, more accurately capture the complexity of residual landform development in cratonic interiors.

Cosmogenic nuclides↗