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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The first intron and promoter of Arabidopsis DIACYLGLYCEROL ACYLTRANSFERASE 1 exert synergistic effects on pollen and embryo lipid accumulation

Summary Accumulation of triacylglycerols (TAGs) is crucial during various stages of plant development. In Arabidopsis , two enzymes share overlapping functions to produce TAGs, namely acyl‐CoA:diacylglycerol acyltransferase 1 (DGAT1) and phospholipid:diacylglycerol acyltransferase 1 (PDAT1). Loss of function of both genes in a dgat1‐1/pdat1‐2 double mutant is gametophyte lethal. However, the key regulatory elements controlling tissue‐specific expression of either gene has not yet been identified. We transformed a dgat1‐1/dgat1‐1//PDAT1/pdat1‐2 parent with transgenic constructs containing the Arabidopsis DGAT1 promoter fused to the AtDGAT1 open reading frame either with or without the first intron. Triple homozygous plants were obtained, however, in the absence of the DGAT1 first intron anthers fail to fill with pollen, seed yield is c . 10% of wild‐type, seed oil content remains reduced (similar to dgat1‐1/dgat1‐1 ), and non‐Mendelian segregation of the PDAT1/pdat1‐2 locus occurs. Whereas plants expressing the AtDGAT1pro:AtDGAT1 transgene containing the first intron mostly recover phenotypes to wild‐type. This study establishes that a combination of the promoter and first intron of AtDGAT1 provides the proper context for temporal and tissue‐specific expression of AtDGAT1 in pollen. Furthermore, we discuss possible mechanisms of intron mediated regulation and how regulatory elements can be used as genetic tools to functionally replace TAG biosynthetic enzymes in Arabidopsis .

McGuire, Sean T.↗
DOE OSTI2024

A Nonintrusive Optical Approach to Characterize Heliostats in Utility-Scale Power Tower Plants: Camera Position Sensitivity Analysis

Optics plays a major role in the effectiveness of concentrating solar power (CSP) technologies. The nonintrusive optical (NIO) approach developed by the National Renewable Energy Laboratory uses uncrewed aircraft system (UAS)-based imaging to survey heliostats in a commercial-scale power tower CSP plant and characterize their optical errors. The image processing algorithm uses photogrammetry to calculate the camera position for each image frame, and the accuracy of the estimated optical errors is highly sensitive to the calculated camera position accuracy. In this study, we simulate a series of case studies in python to examine the impact of different parameters of the sensitivity of the camera calculation, including the number of facet corners used as control points for the photogrammetric calculation, precision error in the detected pixel locations of the facet corners in the image, and precision error of the canting and mounting positions of the facets of the heliostat. The case studies consider heliostat geometry based on three commercial designs to serve as representative examples of different possible sizes of heliostats that the NIO method could be applied to. The results show that increasing the number of control points can improve accuracy for heliostats with many facets, pixel precision has a significantly larger impact on camera calculation accuracy than facet canting and mounting errors, and the camera distance and focal length must be chosen to ensure adequate pixel accuracy on the heliostat surface depending on the size of heliostat. In conclusion, based on the results, recommendations for the allowable values of each parameter are provided to achieve the required NIO optical error estimation accuracy depending on the size of heliostat.

14 SOLAR ENERGY↗
DOE OSTI2025

A Combined Computational and Mathematical Analysis of Interconnect Fatigue Potential in Photovoltaic Modules

A finite element model of a 60-cell monocrystalline silicon glass-polymer photovoltaic module was simulated with ±1.0 kPa and ±2.4 kPa loads applied to the glass to calculate the deformation under load. Cell-to-cell displacements were used to approximate interconnect strain and stress. A mathematical fatigue cycle life relation was fitted to data for the interconnect material (copper), to generate a life prediction at each interconnect location based on the local stress means, reversal extents, and amplitudes. Interconnect stress was found to be significantly asymmetric about zero despite symmetric positive and negative module loads due to laminate thickness offsets about the neutral plane and the effects of module framing. Cycle life results indicated that interconnect fatigue failure was unlikely to occur over a 30-year lifetime of conservative wind and snow load cycles since the typical cell design feature of leaving some unconstrained length between the cell edge and first solder pad increases the effective gauge length and decreases the stress levels below the material endurance limit. Follow-up analyses found that 3.6 mm and 6.4 mm were the minimum unconstrained lengths required to survive the assumed lifetime of wind and snow cycles, respectively, confirming that typical industrial module constructions with 8–15 mm unconstrained lengths should survive conservatively. Notably, large magnitude, low-cycle snow loading was consistently the limiting factor requiring a longer unconstrained interconnect length. Finally, insights and workflows from this study inform module interconnection design limits for survival against mechanical fatigue in deployment environments.

14 SOLAR ENERGY↗
DOE OSTI2025

A Novel Approach for Computing Rigid Body Motion Using Linear Accelerations

Here, a novel approach is presented for computing general rigid body motion based on a few known linear accelerations. This method utilizes linear acceleration data obtained from three distinct points on the body, all within a body-fixed reference frame. The only requirement is that the three chosen points must not be collinear. A system of differential-algebraic equations is derived, combining principles of rigid body kinematics with theory of the rotation group SO(3). These equations provide a framework for numerically computing various motion parameters, including angular velocity, angular acceleration, body orientation, velocity field, acceleration field, and displacement field. By numerically solving this system of equations, we can fully characterize rigid body motion in three-dimensional space. A numerical example is provided to demonstrate the practical implementation and efficacy of the proposed technique, illustrating its potential for accurate motion computation in various applications.

42 ENGINEERING↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2024

Optical Flow Diagnostics of Counter Fluidization of Gravity-Driven Moving Packed Bed for a CSP Receiver Section Featuring Staggered Array of Cylindrical Pins

Particle fluidized beds have the potential to improve the efficiency of heat transfer in concentrated solar receiver furnace for use in next generation concentrated solar power (CSP) plants. This study presents an experimental investigation on the flow characterization of vertically downward moving packed bed with counter fluidization through an array of jets. To control the bubble size and its distribution in the bubbling fluidized bed, an array of cylindrical pin fins was arranged uniformly across the test article. The flow visualization was performed on the surface of transparent glass coated with electrically conductive materials for electrostatic dissipation purposes. The image acquisition was carried out via high-speed camera at a frequency of ~ 1kHz. The acquired images were analyzed in pairs with the help of a modern optical flow algorithm capable of calculating the movement of dense particle flow in the fluidized bed by tracking the light intensity of each predefined window of the frames. A comparison of fluidized beds with plane and pin-finned channels revealed distinct bubble behavior. Pin-finned channels were found to produce a larger number of small-sized bubbles, while plane channels generated fewer but larger bubbles at any given instant. The presence of pin fins was observed to reduce bubble size by preventing bubble merging and splitting larger bubbles when they encountered a pin.

concentrating solar power↗
DOE OSTI2025

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗
DOE OSTI2025

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Small-scale production of bespoke accelerated aging plutonium alloy

Here, this study demonstrates the 100 g scale manufacture of a plutonium alloy that ages at an accelerated rate. The resulting alloy ages six times faster than typical weapons-grade plutonium due to the addition of 238 Pu. As a major innovation, the process involved using a partial direct oxide reduction technique. This method was achieved by developing a new, complex geometry stirrer using additive manufacturing to reduce the 238Pu oxide and efficiently incorporate it into weapons-grade plutonium metal. The material was then purified by molten salt extraction and electrorefining before being alloyed with gallium. The alloy was then cold-rolled and annealed in a homogenization heat treatment. The resulting disk was characterized by metallography and differential scanning calorimetry, and the impurity content was determined using analytical chemistry techniques. The results show that a homogeneous delta phase plutonium alloy was achieved with expected microstructure and minimal impurities. This study was also successful in changing the plutonium isotopic composition by incorporating additional 238 Pu to accelerate the effects of radiation damage. This enables researchers to study long-term aging phenomena in a reduced time frame, thus avoiding the need for large-scale material production and circumventing the limitations of using naturally aged, archived plutonium.

Aging theory↗
DOE OSTI2025

Automated nuclear cloud feature extraction from film

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2026

Uncertainty quantification of fireball features extracted from nuclear test films using computer vision

Films from the US’s historic nuclear testing era comprise the only extensive collection of imagery depicting high-yield detonations. These films offer unique insights into the characteristics of flows occurring on scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models used to describe nuclear detonations. In recent work, we implemented modern computer vision and machine learning techniques to extract features of the fireball following nuclear detonation. With a training dataset of fireball films, we fine-tuned a You Only Look Once 11 (YOLO11) model to detect and track the fireball. Applied to a video, the outer bounding box produced in each frame by YOLO11 is used as an input prompt to Meta’s Segment Anything Model 2 (SAM2), which is shown to accurately predict the boundary of the fireball over time with high resolution. These state-of-the-art computer vision foundation models exhibit impressive visual accuracy in their results but lack an output of values that robustly quantify uncertainty in scientific applications. In this paper, we develop procedures for uncertainty quantification of extracted fireball features. We outline the application of a parallel attention mechanism to calculate uncertainty ranges that complement and better pose model validation data. This higher quality fireball validation data may serve to improve prognostic models describing nuclear detonations in support of nuclear forensic and emergency response activities.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2026

YOLO11 to SAM2 pipeline for feature extraction from nuclear test films

The response to the effects of nuclear detonations is supported by models that describe the evolution of the nuclear fireball and cloud and the associated transport of active debris. Validation of those descriptions relies on data from the nuclear test operations. Video records of those events offer a rich source of information that was exploited to a limited extent in historic analyses. Computer vision and machine learning techniques are powerful tools that can be used to increase the number of measurements that can be obtained from those films. In this work, we apply computer vision techniques to automatically track the temporal evolution of the nuclear fireball. In particular, we apply You Only Look Once 11 (YOLO11) and Segment Anything Model 2 (SAM2) in combination with minimal human intervention to digitized versions of the original nuclear test films. As part of the proposed workflow, the YOLO11 model is applied to films to determine bounding boxes for the fireball within each frame. These are then used as inputs to SAM2, which uses image segmentation to determine the fireball boundaries and their temporal evolution. We assess the accuracy of our approach by using it to determine the energy released during the Trinity nuclear test and comparing the results with previous analyses based on manual measurements.

Van Exel, Kimberly [ORNL] (ORCID:0009000877463894)↗
DOE OSTI2025

Serial-femtosecond crystallography reveals how a phytochrome variant couples chromophore and protein structural changes

The photoreaction and commensurate structural changes of a chromophore within biological photoreceptors elicit conformational transitions of the protein promoting the switch between deactivated and activated states. We investigated how this coupling is achieved in a bacterial phytochrome variant, Agp2-PAiRFP2. Contrary to classical protein crystallography, which only allows probing (cryo-trapped) stable states, we have used time-resolved serial femtosecond x-ray crystallography (tr-SFX) and pump-probe techniques with various illumination and delay times with respect to photoexcitation of the parent Pfr state. Thus, structural data for seven time frames were sorted into groups of molecular events along the reaction coordinate. They range from chromophore isomerization to the formation of Meta-F, the intermediate that precedes the functional relevant secondary structure transition of the tongue. Structural data for the early events were used to calculate the photoisomerization pathway to complement the experimental data. Late events allow identifying the molecular switch that is linked to the intramolecular proton transfer as a prerequisite for the following structural transitions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Scalable fabrication of Chip-integrated 3D-nanostructured electronic devices via DNA-programmable assembly

DNA-based self-assembly methods have demonstrated powerful and unique capabilities to encode nanomaterial structures through the prescribed placement of inorganic and biological nanocomponents. However, the challenge of selectively growing DNA superlattices on specific locations of surfaces and their integration with conventional nanofabrication has hindered the fabrication of three-dimensional (3D) DNA-assembled functional devices. Here, we present a scalable nanofabrication technique that combines bottom-up and top-down approaches for selective growth of 3D DNA superlattices on gold microarrays. This approach allows for the fabrication of self-assembled 3D-nanostructured electronic devices. DNA strands are bound onto the gold arrays, which anchor DNA origami frames and promote ordered framework growth on the specific areas of the surface, enabling control of the lateral placement and orientation of superlattices. DNA frameworks selectively grown on the pads are subsequently templated to nanoscale silica and tin oxide (SnO x ) that follow the architecture, as confirmed by structural and chemical characterizations. The fabricated SnO x superlattices are integrated into devices that demonstrate photocurrent response.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Beyond-classical computation in quantum simulation

Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here, we show that superconducting quantum annealing processors can rapidly generate samples in close agreement with solutions of the Schrödinger equation. We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three-, and infinite-dimensional spin glasses, supporting the observed stretched-exponential scaling of effort for matrix-product-state approaches. We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer within a reasonable time frame. Thus, quantum annealers can answer questions of practical importance that may remain out of reach for classical computation.

King, Andrew D. [D-Wave Quantum Inc., Burnaby, BC ↗
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

Deletion of Re -citrate synthase allows for analysis of contributions of tricarboxylic acid cycle directionality to the growth of Heliomicrobium modesticaldum

ABSTRACT Heliomicrobium modesticaldum,a phototrophic member of the phylum Firmicutes and family Clostridiales, possesses most of the enzymes specific to the reductive tricarboxylic acid (rTCA) cycle, except for the key enzyme, ATP-citrate lyase. It is thought to utilize a split TCA cycle when growing on pyruvate as a carbon source, in which the oxidative TCA (oTCA) direction generates most of the 2-ketoglutarate, but some can be produced in the reductive direction. Although a typicalSi-citrate synthase gene is not found in the genome, it was suggested that gene HM1_2993, annotated as homocitrate synthase, actually encodesRe-citrate synthase, which would function as the initial enzyme of the oTCA cycle. We deleted this gene to test this hypothesis and, if true, see what effect severing access to the oTCA cycle would have on this organism. The endogenous CRISPR-Cas system was used to replace the open reading frame with a selectable marker. The deletion mutants could grow on pyruvate but were unable to grow phototrophically on acetate + CO 2 as carbon source. Growth on acetate could be rescued by the addition of different electron sources (formate or ascorbate), suggesting that the oTCA cycle is used to oxidize acetate to generate electrons required to drive the carboxylation of acetyl-CoA. The deletion mutants were capable of growing in acetate minimal media without additional organic supplements beyond formate, demonstrating that the rTCA cycle can be employed to support sufficient 2-ketoglutarate production in this organism, unlike citrate synthase mutants in several chemoheterotrophic organisms utilizing the oTCA cycle. IMPORTANCE Heliobacteria are a unique group of phototrophic bacteria that are obligate anaerobes and possess a rudimentary system to use light as a source of energy. They do not make oxygen or fix carbon dioxide. Here, we explore their fundamental carbon metabolism to understand the role and operation of the central TCA cycle. This work shows both the role and operation of this cycle under different growth modes and explains how these organisms can obtain electrons to drive their biosynthetic metabolism. This foundational knowledge will be crucial in the future when attempts are made to use this organism as a platform for oxygen-sensitive synthesis of compounds in an anaerobe that can use light as its energy source.

Biotechnology & Applied Microbiology↗
DOE OSTI2024

Complete genome sequence of Rhodococcus qingshengii phage Perlina

ABSTRACT Rhodococcus phage Perlina is a novel phage isolated on Rhodococcus qingshengii S10. Perlina encodes 112 open reading frames with typical phage structural genes identified and 3 tRNAs (tRNA-Ile, tRNA-Met, and tRNA-Asn). Few close relatives can be identified at the nucleotide level, suggesting a new phage species.

59 BASIC BIOLOGICAL SCIENCES↗