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Results for “in situ data processing”

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 37 records · Page 2

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NASA NTRS2009

An Empirical Approach to Ocean Color Data: Reducing Bias and the Need for Post-Launch Radiometric Re-Calibration

A new empirical approach is developed for ocean color remote sensing. Called the Empirical Satellite Radiance-In situ Data (ESRID) algorithm, the approach uses relationships between satellite water-leaving radiances and in situ data after full processing, i.e., at Level-3, to improve estimates of surface variables while relaxing requirements on post-launch radiometric re-calibration. The approach is evaluated using SeaWiFS chlorophyll, which is the longest time series of the most widely used ocean color geophysical product. The results suggest that ESRID 1) drastically reduces the bias of ocean chlorophyll, most impressively in coastal regions, 2) modestly improves the uncertainty, and 3) reduces the sensitivity of global annual median chlorophyll to changes in radiometric re-calibration. Simulated calibration errors of 1% or less produce small changes in global median chlorophyll (less than 2.7%). In contrast, the standard NASA algorithm set is highly sensitive to radiometric calibration: similar 1% calibration errors produce changes in global median chlorophyll up to nearly 25%. We show that 0.1% radiometric calibration error (about 1% in water-leaving radiance) is needed to prevent radiometric calibration errors from changing global annual median chlorophyll more than the maximum interannual variability observed in the SeaWiFS 9-year record (+/- 3%), using the standard method. This is much more stringent than the goal for SeaWiFS of 5% uncertainty for water leaving radiance. The results suggest ocean color programs might consider less emphasis of expensive efforts to improve post-launch radiometric re-calibration in favor of increased efforts to characterize in situ observations of ocean surface geophysical products. Although the results here are focused on chlorophyll, in principle the approach described by ESRID can be applied to any surface variable potentially observable by visible remote sensing.

Gregg, Watson W.↗

DOE OSTI2023

Application of Machine Learning to Monitor Metal Powder-Bed Fusion Additive Manufacturing Processes

The use of additive manufacturing (AM) is increasing for high-value, critical applications across a range of disparate industries. This article presents a discussion of high-valued engineering components predominantly used in the aerospace and medical industries. Applications involving metal AM, including methods to identify pores and voids in AM materials, are the focus. The article reviews flaw formation in laser-based powder-bed fusion, summarizes sensors used for in situ process monitoring, and outlines advances made with in situ process-monitoring data to detect AM process flaws. It reviews investigations of ML-based strategies, identifies challenges and research opportunities, and presents strategies for assessing anomaly detection performance.

Reutzel, Edward W.↗

DOE OSTI2025

A Versatile Simulated Data Transport Layer for in Situ Workflows Performance Evaluation

In situ processing does not only allow scientific applications to face the explosion in data volume and velocity but also to address the time constraints of many simulation-analysis workflows by providing scientists with early insights about their applications at runtime. Multiple frameworks implement the concept of a data transport layer (DTL) to enable such in situ workflows. These tools are very versatile, directly or indirectly access the data generated on the same node, another node of the same compute cluster, or a completely distinct node, and allow data publishers and subscribers to run on the same computing resources or not. This versatility puts on researchers the onus of taking key decisions related to resource allocation and how to transport data to ensure the most efficient execution of their in situ workflows. However, domain scientists and workflow practitioners lack the appropriate tools to assess the respective performance of particular design and deployment options. In this paper we introduce a versatile simulated DTL designed to provide researchers with insights on the respective performance of different execution scenarios of in situ workflows. This open-source, standalone library builds on the SimGrid toolkit and can be linked to any SimGrid-based simulator. It facilitates the evaluation of the performance behavior, at scale, of different data transport configurations and the study of the effects of resource allocation strategies. We demonstrate the scalability, versatility, and accuracy of this simulated DTL by reproducing the execution of two synthetic benchmarks and of a real-world in situ workflow composed of an MPI application and a parallel data analysis. Results of simulations run on a single core show that the proposed library can simulate the interactions of tens of thousands of simulated processes deployed on two interconnected commodity clusters in a few seconds, and the execution by a thousand simulated processes of an in situ workflow in less than three minutes.

Suter, Fred [ORNL] (ORCID:0000000319021955)↗

DOE OSTI2025

In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data

This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and 102 mm tall, were printed out of stainless steel 316H on a Colibrium Additive Concept Laser M2 Series 5 machine. Parameters known to produce dense material were used to construct 56 of these cylinders, while the remaining 8 cylinders were printed with relatively high energy density parameters prone to producing keyhole pores. In addition, two spatter generation blocks were constructed upstream of the 64 cylinders such that ejecta produced during the melting of the spatter generators were stochastically seeded onto the 64 cylinders. Based on previous experiments, these spatter particles were theorized to produce stochastic lack-of-fusion pores. During the construction of the build, high-resolution images of reflected light in the visible spectrum were captured both before and after recoating for each print layer. Additionally, temporally integrated thermal imaging in the near infrared spectrum produced integrated sum and max images on a layerwise basis. The multimodal in situ data has been co-registered to the build plate coordinate system, allowing for identification of process anomalies (e.g., spatter particles) apparent in the two sensors. Following construction of the build, the cylinders were subjected to XCT to identify internal flaws, and the resulting data have also been registered to the build plate coordinate system. Finally, 60 of the 64 cylinders were machined into fatigue coupons conforming to ASTM E466 and subsequently subjected to either high- or -low-cycle fatigue testing. The results of the fatigue tests have also been included in the dataset, and the XCT data corresponded to the approximate location of the gauge sections of the machine fatigue specimen geometry.

42 ENGINEERING↗

NASA NTRS1982

A comparison of HCMM surface temperatures with in situ temperature data

All HCMM required were processed. Calibrated and atmospherically corrected sea surface temperatures were developed for the Nantucket Shoals and Gulf of Mexico regions. These data were analyzed and the sea surface temperatures along the various transacts were compared with in situ data. The comparisons indicate that there is, on the average, a root mean square difference between the in situ data and the HCMM sea surface temperatures of + or - 1.0 C. The linear correlation coefficient was 0.97.

Vukovich, F. M.↗

NASA NTRS1996

Real-Time Visualization of an HPF-based CFD Simulation

Current time-dependent CFD simulations produce very large multi-dimensional data sets at each time step. The visual analysis of computational results are traditionally performed by post processing the static data on graphics workstations. We present results from an alternate approach in which we analyze the simulation data in situ on each processing node at the time of simulation. The locally analyzed results, usually more economical and in a reduced form, are then combined and sent back for visualization on a graphics workstation.

Kremenetsky, Mark↗

DOE OSTI2023

An artificial intelligence’s interpretation of complex high-resolution in situ transmission electron microscopy data

Complicated nano- and atomic-scale processes with sub-angstrom spatial resolution and millisecond time resolution visualized by in situ transmission electron microscopy (TEM) are often highly dynamical and time consuming to analyze and interpret. Here, we report how variational autoencoders (VAEs) can provide an artificial intelligence’s interpretation of high-resolution in situ TEM data by condensing and deconvoluting complicated atomic-scale dynamics into a latent space with reduced dimensionality. We designed a VAE model with high latent dimensions capable of deconvoluting information from complex high-resolution TEM data. We demonstrate how this model, with high latent dimensions trained on atomically resolved TEM images of lead sulfide (PbS) nanocrystals, is able to capture movements and perturbations of periodic lattices in both simulated and real in situ TEM data. Importantly, the VAE model shows the capability of detecting and deconvoluting dynamical nanoscale physical processes, such as the rotation of crystal lattices and intra-particle ripening during the annealing of semiconductor nanocrystals.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.

42 ENGINEERING↗

DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗

NASA NTRS1991

Solid state devices

The Solid State Device research program is directed toward developing innovative devices for space remote and in-situ sensing, and for data processing. Innovative devices can result from the standard structures in innovative materials such as low and high temperature superconductors, strained layer superlattices, or diamond films. Innovative devices can also result from innovative structures achieved using electron tunneling or nanolithography in standard materials. A final step is to use both innovative structures and innovative materials. A new area of emphasis is the miniaturization of sensors and instruments molded by using the techniques of electronic device fabrication to micromachine silicon into micromechanical and electromechanical sensors and actuators.

Source record↗

DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗

NASA NTRS1989

Plate motions and deformations from geologic and geodetic data

The very long baseline interferometry (VLBI) measurements made in the western U.S. since 1979 provide discrete samples of the temporal and spatial deformation field. The interpretation of the VLBI derived rates of deformation requires an examination of geologic information and more densely sampled ground based geodetic data. Triangulation and trilateration data measured on two regional networks, one in the central Mojave Desert and one in the Coast Ranges east of the San Andreas fault, were processed. At the spatial scales spanned by these local geodetic networks, auxiliary geologic and geophysical data were utilized to examine the relation between measured incremental strain and the accommodation of strain seen in local geologic structures, strain release in earthquakes, and principal stress directions inferred from in situ measurements. VLBI data was also processed from stations distributed across the Pacific-North America plate boundary zone in the western U.S. The VLBI data were used to constrain the integrated rate of deformation across portions of the continental plate boundary in California and to provide a tectonic framework to interpret regional geodetic and geologic studies.

Jordan, Thomas H.↗

DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗

NASA NTRS1997

An Analysis of Closure Mechanisms in the Plasma Wake of the TSS-1R Satellite

Collisionless Plasma Expansion (CPE), also known as "plasma expansion into a vacuum," in its most fundamental form, is the process by which a plasma expands into a void, or region highly depleted of particles. In CPE, the expansion is driven by the highly mobile electron constituent of the plasma as it moves across the density gradient and into the void region. A bi-polar electric field is set up between this rapidly expanding electron front and the massive ions, which have a low thermal speed and lag behind in the region of the plasma-void interface. These ions are quickly accelerated by the expansion electric field and can, theoretically, approach the thermal speed of the electrons. CPE is a fundamental and a very robust process in that it only requires that a sharp density gradient exist in a plasma. It has already been observed to exist in various plasmas ranging over five orders of magnitude in density. The range of phenomena in which CPE has been observed includes the closure of plasma wakes created in simulated space plasmas in the laboratory; it was found to be the process controlling the refilling of the wake created by the Space Shuttle in the ionospheric plasma; and recent in situ data, obtained by the WIND spacecraft as it passed through the wake of the earth's moon last year, have shown CPE to be involved in the closure of the Lunar wake. CPE is also expected to influence the morphology and physics of the solar wind interactions with Mercury and Mars, and has been suggested as a potential factor in such wide ranging phenomena as the acceleration of plasma away from the coma of comets and the energization of ions that underlies the upward expansion of the polar wind into the earth's magnetosphere. The Tethered Satellite System (TSS)-1R data provides an opportunity to test this process in situ under semi-controlled conditions. The available data shows the intensity, drift energy and angle of inclination to the wake of the converging ion streams at a down stream distance of one satellite radius (80 CM).

Stone, N. H.↗

DOE OSTI2024

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Simpson, Abigayle↗

DOE OSTI2024

Alternating conduction and convection drying of paper – an experimental analysis with a continuous data acquisition approach

In conventional multi-cylinder drying of paper and board, both conductive drying from steam-heated dryer cylinders and convective drying by flowing air over the paper surface in the pockets are used. Conductive drying from steam-heated drying cylinders is a critical component in providing the necessary thermal energy to paper and board as they dry. Steam temperature and internal and external resistances at the contacting surface are critical process parameters influencing the conductive drying process. An experimental setup was developed to study the alternating conductive and convective drying of paper and board. Paper sheet moisture, temperature, and temperature distribution within the heated platen and the instantaneous heat flux as the sheet was being dried were measured. The instantaneous heat flux, contact heat transfer coefficient, and drying rates were determined as drying proceeds. Experimental results, as well as comparisons to literature and commercial data, are presented. The conductive heat transfer coefficients determined were compared to traditional correlations normally used in the modeling of paper drying. Similarly, the convective heat and mass transfer coefficients are also determined and compared to literature data. In addition to the evaluation of alternating conductive and convective drying characteristics of paper and board, the potential inclusion of auxiliary energy components will also be included. Experimental results from the conduction and convection drying system are presented. Furthermore, this data will be useful in process development, intensification of manufacturing processes, and modeling and simulation of paper drying processes.

42 ENGINEERING↗

DOE OSTI2022

Proximity Portability and in Transit , M-to-N Data Partitioning and Movement in SENSEI [Book Chapter]

In high-performance parallel in situ processing, the term in transit processing refers to those configurations where data must move from a producer to a consumer that runs on separate resources. In the context of parallel and distributed computing on an HPC platform one of the central challenges is to determine a mapping of data from producer ranks to consumer ranks. This problem is complicated by the heterogeneity that arises in producer-consumer pairs, such as when producer and consumer codes have different levels of concurrency, different scaling characteristics, or different data models. The resulting mapping and movement of data from M producer to N consumer ranks can have a significant impact on aggregate application performance, particularly when the data consumer requires only a subset of the overall data for its task. This chapter focuses on the design considerations that underlie SENSEI’s implementation to this challenging problem. These design considerations extend the core SENSEI architecture and include ideas like the need to accommodate flexibility in the choice of different partitioning methods, the ability for a data consumer to request and receive only the subset of data needed for its particular operation, and the ability to leverage any of several different data transport tools. The idea of proximity portability, being able to use different data transport methods as part of an in transit workflow, is illustrated through the use of three different transport layers where switching from one transport tool to another is accomplished with only a configuration file change. Here, the chapter also includes a performance analysis summary showing the performance gains that are possible in terms of multiple metrics, such as memory footprint, time to solution, and amount of data moved, when using optimized partitioners in an in transit setting, gains that are made possible by the implementation shaped by specific design considerations.

Bethel, E. Wes↗

DOE OSTI2024

Review of In Situ Sensing for Directed Energy Deposition for Industrial Part Quality Assessment

As the use additive manufacturing (AM) processes continues to grow in critical industries, improved quality assurance methods are becoming increasingly sought after for qualification and certification of AM components. Traditional nondestructive evaluation of printed components is often unable to supply the required confidence in print quality to justify qualification and certification, but the layer-by-layer nature of AM provides unprecedented opportunities for in situ quality inspection. This document summarizes recent developments in process monitoring research specifically related to Directed Energy Deposition (DED). Particular attention is given to three aspects of the highlighted manuscripts: (1) the type of sensors used, (2) features extracted from each sensor modality, and (3) analysis of extracted features for AM quality assessment. Based on the review of the state-of-the-art, several observations have been made. First, none of the reviewed works have applied their trained models to real part geometries, with many of the works relying on single track experiments, thin-walled structures, and cubes. Similarly, there have not been any works demonstrating model generalizability, i.e., a model trained on data from one build allows for fruitful analysis of data from another build. Many works used machine learning techniques to distinguish different process regimes (i.e., normal, keyholing, lack-of-fusion), but very few papers have investigated stochastic variation in an already “optimized” process. Sensor fusion approaches are also limited in the DED sensing literature, but the few works that have employed such techniques have demonstrated the benefits. Finally, registration of in situ data to the build coordinate system is of paramount importance to producing industrially relevant in situ monitoring systems. Data registration allows direct correlations between process anomalies detected in the process monitoring data to localized departures in part quality, but such techniques are generally lacking in the current literature.

36 MATERIALS SCIENCE↗