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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 OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Infrared intrusion detection system (IRIDS)

A system and method for intrusion detection includes an imager directed towards an object in an interior space. The imager is in data communication with a computer. The computer is arranged to process digital three-dimensional image data received from the imager and programmed to execute a change detection algorithm in response to the processed three-dimensional data to determine movement of the object. The computer generates an alarm output in response to detecting movement of the object above a predetermined threshold. The method includes providing an imager directed towards an object in an interior space; receiving Time of Flight signals by the imager; processing digital three-dimensional image data received from the imager; and executing a change detection algorithm in response to the processed three-dimensional data to determine movement of the object.

Russell, John L.↗
DOE OSTI2022

Third data release of the Hyper Suprime-Cam Subaru Strategic Program

This work presents the third data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8.2 m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 deg 2 in all five broad-band filters (grizy) at the full depth (~26 mag at 5σ depending on filter) in the Wide layer. If we include partially observed areas, the release covers 1470 deg 2 . The Deep and UltraDeep layers have ~80% of the originally planned integration times, and are considered done, as we have slightly changed the observing strategy in order to compensate for various time losses. There are a number of updates in the image processing pipeline. Of particular importance is the change in the sky subtraction algorithm; we subtract the sky on small scales before the detection and measurement stages, which has significantly reduced the number of false detections. Thanks to this and other updates, the overall quality of the processed data has improved since the previous release. However, there are limitations in the data (for example, the pipeline is not optimized for crowded fields), and we encourage the user to check the quality assurance plots as well as a list of known issues before exploiting the data. The data release website is https://hsc-release.mtk.nao.ac.jp.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Snowmass Computational Frontier: Topical Group Report on Experimental Algorithm Parallelization

The substantial increase in data volume and complexity expected from future experiments will require significant investment to prepare experimental algorithms. These algorithms include physics object reconstruction, calibrations, and processing of observational data. In addition, the changing computing architecture landscape, which will be primarily composed of heterogeneous resources, will continue to pose major challenges with regard to algorithmic migration. Portable tools need to be developed that can be shared among the frontiers (e.g., for code execution on different platforms) and opportunities, such as forums or cross-experimental working groups, need to be provided where experiences and lessons learned can be shared between experiments and frontiers. At the same time, individual experiments also need to invest considerable resources to develop algorithms unique to their needs (e.g., for facilities dedicated to the experiment), and ensure that their specific algorithms will be able to efficiently exploit external heterogeneous computing facilities. Common software tools represent a cost-effective solution, providing ready-to-use software solutions as well as a platform for R&D work. These are particularly important for small experiments which typically do not have dedicated resources needed to face the challenges imposed by the evolving computing technologies. Workforce development is a key concern across frontiers and experiments, and additional support is needed to provide career opportunities for researchers working in the field of experimental algorithm development. Finally, cross-discipline collaborations going beyond high-energy physics are a key ingredient to address the challenges ahead and more support for such collaborations needs to be created. This report targets future experiments, observations and experimental algorithm development for the next 10-15 years.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Reaction Rover

Reaction Rover is a software application designed to enable reaction optimization with little or no human intervention and to provide a flexible platform to facilitate rapid testing, automation, and implementation of new reaction optimization algorithms. Toward these goals, Reaction Rover is designed to be modular, allowing the optimization algorithm, analyzer, and reactor components to be changed for any given process using easily integrated plugins. The Reaction Rover platform is a key development that enables the testing, use, and automation of machine learning (ML) and non-ML optimizers in a variety of laboratory situations.

Windus, Theresa↗
DOE OSTI2023

Future Response of Coastal Wetlands to Environmental Stresses: Algorithm Comparison of Numerical Models

Coastal wetlands are a critical component of the earth system that strongly influence the global water and biogeochemical cycles. They are also likely important sentinel of climate change. Because eco-geomorphological processes have long been recognized to be important for coastal wetland survival under accelerated sea-level rise (SLR), many eco-geomorphology models have been developed to assess the impact of climate change on coastal wetlands. Although these models differ substantially in complexity and numerical methods, few studies have investigated the algorithm-level uncertainties in these models. In this study, we developed a multiple-algorithm model framework of coastal wetlands that represents coastal hydrodynamics (such as water level, significant wave height and bottom shear stress) and four eco-geomorphological processes: mineral accretion, organic matter accretion, storm surge erosion and landward migration. We validated the model at three representative coastal wetland sites (Venice Lagoon, Plum Island Estuary and Hunter Estuary) for hydrodynamics, mineral accretion and organic matter accretion. Through model-data comparison, we showed that the model can well capture the dynamics of hydrodynamical and eco-geomorphological conditions in the study sites. Importantly, analysis of the multiple-algorithm simulations suggests that differences in the process representation of mineral and organic matter accretion may contribute to the recent contradicting predictions of coastal wetland evolution under accelerated SLR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2021

Geometry and dynamics of passive scalar structures in compressible turbulent mixing

We present a structure-based numerical analysis of passive scalar mixing in decaying homogeneous isotropic turbulence (DHIT) and shock-turbulence interaction canonical configurations. The analysis focuses on the temporal evolution of ensembles of passive scalar structures, initialized as spheres of different sizes relative to the Taylor microscale. An algorithm is introduced to track the evolution of each individual structure and the interactions with other structures in the ensemble, relating changes in the surface geometry and the underlying physical processes (turbulent transport, scalar dissipation, and shock compression). The tracking algorithm is applied to datasets from shock-capturing direct numerical simulations of DHIT, with Taylor microscale Reynolds number Re λ =40 and turbulence Mach number M t =0.2 ⁠, and STI cases in which the turbulence is processed by a shock wave at Mach numbers M = 1.5 and 3.0. Temporal surface convolution increases for initially larger structures, resulting in a higher probability of locally hyperbolic geometries where breakup into smaller structures occurs. Shock-induced deformation of the structures amplifies breakup processes, enhancing mixing, particularly for larger structures. Mixing enhancement by the shock is manifested as an amplification of the surface-averaged scalar gradient, which increases for initially larger structures. The alignment between the scalar gradient and the most extensional strain-rate eigendirection on the scalar isosurfaces also increases across the shock. Larger magnitudes of the scalar gradient and its alignment with the most compressive strain-rate eigendirection correlate with flatter surface regions. Shock-induced structure compression increases the area coverage of flat regions, where the amplification of scalar gradient is localized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol nucleation, emissions, and growth so that the model–measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm, and the total dry volume of aerosol ( N 3–6 , N 10 , V dry ), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional “box” model as a proof of concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in the bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in situ size distribution measurements in western Europe from May 2006 through June 2007. At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm –3 s –1 , respectively. The normalized absolute error in estimated N 3–6 , N 10 , and V dry at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model–measurement bias while constraining uncertain aerosol microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microcam: A Low Power and Privacy Preserving Multi-modal Platform for Occupancy Detection (Final Report)

Heating, ventilation, and air conditioning (HVAC) consumes a significant portion of the energy used in buildings. Much of this is wasted energy, used when buildings are either not occupied at all, or occupied well under their maximum design conditions. This project has focused on residential occupancy detection to autonomously control HVAC systems and save energy. Limitations of existing occupancy sensors include one or more of the following: (i) they employ sensors or algorithms that are not able to detect stationary occupants; (ii) they cannot classify the source of the motion (such as a pet); (iii) depending on the camera resolution and employed algorithms, they do not allow for embedded or onboard computation, and require external or cloud-based processing; (iv) many algorithms developed for camera-based systems are sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) Most existing systems depend on adjustment of settings for different scenarios, complicating self-commissioning; (vi) they cannot provide high enough accuracy; (vii) they are costly; (viii) they are not battery-powered, thus limiting ease of use and installation. In this project, Syracuse University and its partner SRI have developed a low-cost, high accuracy, standalone residential occupancy sensing platform, referred to as the MicroCam, to address all of the aforementioned challenges. MicroCam can operate on typical alkaline batteries without relying on the “cloud” or external computing resources, and consists of low-power, Artificial Intelligence (AI)-based, IoT platforms. Each platform has multi-modal sensors and can process motion, audio and video data, and send binary occupancy result to a lead platform. All sensor data is processed locally on platforms, and the only transmitted data is the binary occupancy state. In addition, preliminary work has been done on images wherein occupants are not discernable. Thus, MicroCam is a standalone solution preserving privacy of the occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A multi-algorithm approach for modeling coastal wetland eco-geomorphology

Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change makes it more difficult for these ecosystems to adapt to the fluctuation in sea levels and other environmental changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. However, their divergent estimates of the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetlands (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model–data comparison, we showed the importance of using a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also found that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites in diverse environments can help reduce the model uncertainty.

58 GEOSCIENCES↗
DOE OSTI2024

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Wavefront distortion correction in scanning tunneling microscope image

Here, we report an algorithm to identify and correct distorted wavefronts in atomic resolution scanning tunneling microscope images. This algorithm can be used to correct nonlinear in-plane distortions without prior knowledge of the physical scanning parameters, the characteristics of the piezoelectric actuator, or individual atom positions. The 2D image is first defined as a sum of sinusoidal plane waves, where a nonlinear distortion renders a curve for an otherwise ideal linear wavefront. Using the Fourier transforms of local areas of the image, the algorithm generates a wavefront vector field. The identified wavefronts are subsequently linearized for each plane wave without changing lattice orders, giving rise to distortion corrections. Our algorithm is complementary to conventional post-processing algorithms that require prior detection of real space features, which can also be used to correct nonlinear distortions in 2D images acquired by other microscopy techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

36 MATERIALS SCIENCE↗