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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sea spray aerosol production flux retrieval based on Doppler lidar measurements

Supermicron sea spray aerosols (SSA) play a crucial role in stratocumulus cloud microphysics and drizzle formation. However, our understanding of SSA's spatiotemporal distribution, production mechanisms, and fluxes in the marine boundary layer remains limited. This study introduces a novel approach to determine supermicron SSA production flux and number concentration at various heights above the ocean surface. Our method leverages Doppler lidar data, specifically attenuated backscatter and vertical velocity, collected at a 105 m range gate during the Eastern Pacific Clouds and Aerosols for Precipitation Experiment (EPCAPE) at Scripps Pier, La Jolla, California. To focus on nascent SSA, we analyzed data from periods with wind directions between 225° and 315° and wind speeds exceeding 4 m s −1 . Cloud and precipitation events were excluded using relative humidity, backscatter thresholds, and 2D Video Disdrometer observations. Results show that calculated supermicron SSA number concentrations at 105 m ranged from 0.26 to 2.93 cm −3 for surface wind speeds between 4 and 6 ms −1 . When exceeding the method's Limit of Detection, estimated production fluxes ranged from 0.20 to 1.53 cm −2 s −1 . This methodology has potential for application in various oceanic locations, likely enhancing our understanding of SSA's role in atmospheric processes and climate interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗
DOE OSTI2020

Shock-induced kinetics and cellular structures of liquid nitromethane detonation

Using a combination of high-speed diagnostics, optical pyrometry, velocimetry and video photography, we examine the spatiotemporal reaction kinetics of a prototypical high-energy explosive, liquid nitromethane (NM), as we drive this high explosive into detonation. The detonations were initiated by powerful shock waves whose durations (4 ns) were shorter than the characteristic time associated with the reaction (7 ns). Simple optical spectroscopy alone cannot characterize the kinetics with high time resolution because the reactants and products are flowing at a high velocity of ~6 km/s (6 µm/ns). Additionally, every detection volume behind the shock front contains material initiated over a range of times. High spatial resolution can be obtained by probing interfaces where the shock enters or breaks out of the NM. In addition, we obtain cellular patterns imprinted on the luminous shock front by the two-stage explosion in NM. These spatiotemporal patterns arise naturally as a result of the asymmetry produced by the moving shock front. In this way the shock front serves as a thin moving intrinsic optical gauge that reports and characterizes the two-stage NM explosion behind the front.

42 ENGINEERING↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Influence of strain-rate on the response of elastomeric architected materials

Architected materials have shown substantial promise in impact mitigation and protective applications, and there has accordingly been great interest in better characterizing their response at elevated strain rates due to impact. There remains ambiguity regarding the contribution of inertial and material responses to strain rate sensitivity, and, in particular, when these effects begin to gain dominance in the impact response of an architected material. The response of soft polymer architected materials as a function of strain rate, in particular, has been little investigated. We characterize the experimental impact response of four soft polymer architected lattice geometries across varying strain rates in the intermediate strain rate regime (∼10 3 s −1 ) using split-Hopkinson pressure bar loading and high speed video characterization of the resulting deformation fields. In conclusion, our results highlight the interplay of influence between constituent material, lattice geometry, length scale, and strain rate in determining the onset of significant inertia effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Insights into the failure mode of the Chelyabinsk meteor from high-fidelity simulation

In 2013, an asteroid with a diameter of approximate 20 m broke up in the atmosphere above the Russian city of Chelyabinsk producing what is known as an airburst event. The event was recorded on video from multiple angles and a 500-kg fragment was recovered from Lake Chebarkul shortly thereafter. These aspects, coupled with the asteroid’s large size, distinguish the Chelyabinsk event as unique. In this study, we use the smoothed particle hydrodynamics method to examine the breakup mode of a Chelyabinsk-sized monolithic asteroid and probe whether a monolithic structure is consistent with observations from the Chelyabinsk event. We model the object as a homogeneous sphere with properties consistent with Chelyabinsk meteorites. We use planar-2D simulations to test the sensitivity of results to variations in the material model and to qualitatively inspect the cause of crack nucleation and fragmentation. We then compare results from a 3D simulation to observations from the event. Our 3D simulation predicts a burst height within 3 km of the observed peak luminosity suggesting the responsible asteroid may have been monolithic.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

3D optical diagnostics for explosively driven deformation and fragmentation

High-speed, optical imaging diagnostics are presented for three-dimensional (3D) quantification of explosively driven metal fragmentation. At early times after detonation, Digital Image Correlation (DIC) provides non-contact measures of 3D case velocities, strains, and strain rates, while a proposed stereo imaging configuration quantifies in-flight fragment masses and velocities at later times. Experiments are performed using commercially obtained RP-80 detonators from Teledyne RISI, which are shown to create a reproducible fragment field at the benchtop scale. DIC measurements are compared with 3D simulations, which have been ‘leveled’ to match the spatial resolution of DIC. Overall, results demonstrate improved ability to identify predicted quantities-of-interest that fall outside of measurement uncertainty and shot-to-shot variability. Similarly, video measures of fragment trajectories and masses allow rapid experimental repetition and provide correlated fragment size-velocity measurements. Measured and simulated fragment mass distributions are shown to agree within confidence bounds, while some statistically meaningful differences are observed between the measured and predicted conditionally averaged fragment velocities. Together these techniques demonstrate new opportunities to improve future model validation.

42 ENGINEERING↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Preventative studies should begin now for detecting AI-generated microscopy images

With the rapid progress made in artificial intelligence (AI), text, images, and even videos that are often indistinguishable from human-created content can now be easily generated using new AI models. In this essay, we call attention to the potential misuse of advanced AI models to generate microscopy images, as it could challenge reviewers and fraud hunters in an unprecedented way. This concern is more than hypothetical because relevant open-sourced implementations and datasets are widely available. As a result, we urge that preventative studies on methods to detect microscopy image fabrication should begin now before potential misconduct materializes and becomes widespread.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An implantable biomechanical energy harvester for animal monitoring devices

Insufficient service life and the resulting need for battery replacements have been a great challenge for implantable electronic devices. This is particularly true for animal tracking applications, because recapturing animals is often unlikely once they are released to the wild. To tackle this problem, we developed a biomechanical energy harvester that uses a Macro Fiber Composite™ (MFC) piezoelectric beam to harvest the mechanical energy from animals’ body bending movements as the power source for implantable and wearable devices. Prototypes of an underwater acoustic transmitter using this technology were subdermally implanted into juvenile white sturgeon and their energy harvesting performance was evaluated through the device’s transmissions. Additionally, the fish successfully recovered from the implantation surgery and freely swam inside a tank. The transmitter prototypes in the fish continually transmitted signals for a period up to 5 weeks. A benchtop test setup was also created to emulate the fish’s body bending, estimate the device’s energy harvesting performance in the live fish, and perform accelerated fatigue testing of the energy harvester by applying test parameters learned from a video study of the fish’s body movement and behavior characteristics. The gradual depolarization of the piezoelectric ceramic material in the MFC under cyclic mechanical loading was the main limiting factor for the life span of the energy harvester. Pathways for improvement are proposed to achieve long-term efficacy of powering implantable and wearable electronic devices.

42 ENGINEERING↗
DOE OSTI2026

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗
DOE OSTI2022

First snowflake divertor experiments in MAST-U tokamak

First snowflake (SF) divertor experiments in the MAST-U tokamak demonstrated steady-state snowflake-plus divertor configurations in 450 kA ohmic L-mode plasmas. The SF divertor configuration features a second poloidal field (PF) null in the divertor region close by or overlapping with the main X-point. The resulting low PF region and two additional divertor legs (strike points) may lead to additional power and particle flux sharing via a hypothesized convective cell, and increased plasma-wetted area and radiation. The free-boundary Grad–Shafranov equilibrium code FIESTA was used to design SF configurations with several inter-null distances and orientations. In the experiment, the SF configurations with inter-null distances 0.13–0.20 m and lasting 0.2–0.3 s were obtained. Parallel connection lengths between the midplane and the outer strike point in the SF configurations (evaluated at field lines 1–2 mm from the separatrix in the midplane) were 25–30 m, higher than in the standard divertor (20–25 m) or the Super-X divertor (25 m). Diagnostic measurements highlighted salient SF features. The infra-red video bolometer diagnostic showed that the radiated power peaking in the PF null region was not as pronounces as in the standard divertor. Divertor ion fluxes measured by target Langmuir probes showed increased ion flux in the plate region where a secondary SF strike point landed, concomitantly with the SF formation. These measurements may suggest that some particle and heat redistribution was taking place in the convective SF zone. The first SF experiments provide a basis for future SF studies in MAST-U tokamak with higher input power, improved plasma control and diagnostic measurements, to be compared with the modeling predictions of plasma convective SF mixing and lower density strike point detachment threshold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Organic photodetector with built-in amplification for the detection of visible light with low optical power

We report on an organic-based photodetector that integrates a dual-gate organic thin-film transistor (DG-OTFT) with an organic photodiode (OPD) to produce a device with a high effective responsivity at low optical power and video-rate compatible response. In this device, the OPD operates in photovoltaic mode, instead of the commonly used photoconductive mode, to modulate one of the gate voltages of the DG-OTFT. Effective responsivity values of 10 A W –1 are measured at optical power values lower than 10 nW at 635 nm. Here, modeling of the operation of this new photodetector suggests that effective responsivity values up to 10 5 A W –1 can be achieved at optical powers of 1 nW using current printing technology and state-of-the-art organic semiconductors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Improved signal stability in inductively coupled plasma–atomic emission spectrometry through use of tandem spray chambers and surfactant addition

A search for causes of intermittent mid-term (about two hours) instability in emission signals from an inductively coupled plasma led to adoption of a tandem spray-chamber arrangement and subsequently to use of a surfactant (Triton X-100) to mitigate the remaining and newly found instabilities. Through a series of investigations, abrupt signal excursions in the tandem setup were traced to droplet coagulation and drainage inside the glass tube that connected the two spray chambers. However, the signal shifts were not the result of sample-solution release directly but rather to the influence of the underlying factors on plasma behavior. Experiments tailored to the study included not only examination of temporal signal behavior but also collection of long-term videos and measurement of radiofrequency characteristics of the plasma. The addition of a surfactant, Triton X-100, for signal-stability improvement is applicable not only to systems that employ tandem spray chambers but also to conventional single Scott-type chamber arrangements. Further, use of the surfactant was unsuccessful in overcoming “acid effects”, either of the steady-state or transient nature, and did not alter plasma background or analyte signals significantly.

Inductively coupled plasma–atomic emission spectro↗
DOE OSTI2020

A Digital Imaging Method for Evaluating the Kinetics of Vapochromic Response

This work describes the use of a cell phone camera and the L*a*b method (color space specified by the International Commission on Illumination) to characterize the color change in different vapochromic systems. In this study we have developed a semi-automatic color change analysis software that digitally analyzes images (e.g., video frames) collected while a vapochromic material is absorbing vapor. The advantages of using this method, as compared to reflectance spectroscopy or transmission spectroscopy through a thin film, include low cost, convenience, portability, ease of sample preparation, the absence of need for specialized equipment, and the ease of simultaneously collecting data on different samples under identical conditions. In addition, this method arguably provides direct insight into what a human would observe when monitoring these color changes by eye. Limitations of the method also are discussed in this paper.

cell phone camera, colorimetric sensing, vapor det↗