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Results for “in situ analytics”

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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers

The current practice for fusion scientists running first principle simulations on high performance computing plat-forms is to either run their simulations and output their data for post-hoc analysis, or to place in situ analytics into their code. In this paper we examine a complex workflow using XGC fusions simulation run on the Oak Ridge Leadership Computing Facility's supercomputer Summit, which also involve three anal-yses as part of the results necessary for scientific discovery. We discuss the challenges faced when implementing these algorithms and present an original hybrid staging technique to help enable the physicists to make discoveries during the execution of the simulation. By creating this infrastructure, we can examine complicated physics results, which may not have been possible without the infrastructure. For example, our work enables the online visualization of turbulent homoclinic tangle around the magnetic X-point, breaking the last confinement surface. This visualization could help fusion scientists to better understand and improve the turbulence spread of plasma exhaust heat, which is crucial toward realizing plasmas beyond the currently accessible physics regimes of present-day tokamak reactors. The physics of turbulent homoclinic tangle will be reported in a future physics publication, by utilizing the original online analysis/visualization framework presented in this paper.

Suchyta, Eric↗
DOE OSTI2025

ElementLIBS User's Guide: An operational aid for use and development

Laser-Induced Breakdown Spectroscopy or LIBS is a rapid, in-situ analytical technique where a laser of known energy is pulsed at the surface of an analyte. The laser pulse rapidly heats a localized area to many thousand degrees Kelvin, ablating part of the analyte and turning it into a plasma. As the plasma cools, excited atoms return to a ground state with known emission energies. This emitted energy is captured by various spectrometers and provides a spectrum of the emitted energies and intensities. This spectrum can be analyzed to provide the elemental composition of a sample by using known emission lines and relative abundance. ElementLIBS was developed for the SciAps hand-held LIBS model Z300 but will work with any model that provides a LIBS spectrum of similar resolution. The Z300 has an integrated resolution of 1/30 nm with a range of approximately 180nm – 960nm, providing an output spectrum of 23431 pixels across three spectrometers. These criteria are only provided as a reference, as the software was designed to work with any size spectrum, provided the input file is of the correct format and the models were developed using the same framework. If spectra of varying dimensions are used, the software will fail without warning and unusual events could occur.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Protein-Nanoparticle Interactions Govern the Interfacial Behavior of Polymeric Nanogels: Study of Protein Corona Formation at the Air/Water Interface

Biomedical applications of nanoparticles require a fundamental understanding of their interactions and behavior with biological interfaces. Protein corona formation can alter the morphology and properties of nanomaterials, and knowledge of the interfacial behavior of the complexes, using in situ analytical techniques, will impact the development of nanocarriers to maximize uptake and permeability at cellular interfaces. In this study we evaluate the interactions of acrylamide-based nanogels, with neutral, positive, and negative charges, with serum-abundant proteins albumin, fibrinogen, and immunoglobulin G. The formation of a protein corona complex between positively charged nanoparticles and albumin is characterized by dynamic light scattering, circular dichroism, and surface tensiometry; we use neutron reflectometry to resolve the complex structure at the air/water interface and demonstrate the effect of increased protein concentration on the interface. Surface tensiometry data suggest that the structure of the proteins can impact the interfacial properties of the complex formed. These results contribute to the understanding of the factors that influence the bio-nano interface, which will help to design nanomaterials with improved properties for applications in drug delivery.

Traldi, Federico (ORCID:0000000161251012)↗
NASA NTRS1992

An in Situ Technique for Elemental Analysis of Lunar Surfaces

An in situ analytical technique that can remotely determine the elemental constituents of solids has been demonstrated. Laser-Induced Breakdown Spectroscopy (LIBS) is a form of atomic emission spectroscopy in which a powerful laser pulse is focused on a solid to generate a laser spark, or microplasma. Material in the plasma is vaporized, and the resulting atoms are excited to emit light. The light is spectrally resolved to identify the emitting species. LIBS is a simple technique that can be automated for inclusion aboard a remotely operated vehicle. Since only optical access to a sample is required, areas inaccessible to a rover can be analyzed remotely. A single laser spark both vaporizes and excites the sample so that near real-time analysis (a few minutes) is possible. This technique provides simultaneous multielement detection and has good sensitivity for many elements. LIBS also eliminates the need for sample retrieval and preparation preventing possible sample contamination. These qualities make the LIBS technique uniquely suited for use in the lunar environment.

Kane, K. Y.↗
NASA NTRS1999

Life Sciences Division Spaceflight Hardware

The Ames Research Center (ARC) is responsible for the development, integration, and operation of non-human life sciences payloads in support of NASA's Gravitational Biology and Ecology (GB&E) program. To help stimulate discussion and interest in the development and application of novel technologies for incorporation within non-human life sciences experiment systems, three hardware system models will be displayed with associated graphics/text explanations. First, an Animal Enclosure Model (AEM) will be shown to communicate the nature and types of constraints physiological researchers must deal with during manned space flight experiments using rodent specimens. Second, a model of the Modular Cultivation System (MCS) under development by ESA will be presented to highlight technologies that may benefit cell-based research, including advanced imaging technologies. Finally, subsystems of the Cell Culture Unit (CCU) in development by ARC will also be shown. A discussion will be provided on candidate technology requirements in the areas of specimen environmental control, biotelemetry, telescience and telerobotics, and in situ analytical techniques and imaging. In addition, an overview of the Center for Gravitational Biology Research facilities will be provided.

Yost, B.↗
NASA NTRS2004

Analysis of Organic Compounds in Mars Analog Samples

The detailed characterization of organic compounds that might be preserved in rocks, ices, or sedimentary layers on Mars would be a significant step toward resolving the question of the habitability and potential for life on that planet. The fact that the Viking gas chromatograph mass spectrometer (GCMS) did not detect organic compounds should not discourage further investigations since (a) an oxidizing environment in the near surface fines analyzed by Viking is likely to have destroyed many reduced carbon species; (b) there are classes of refractory or partially oxidized species such as carboxylic acids that would not have been detected by the Viking GCMS; and (c) the Viking landing sites are not representative of Mars overall. These factors motivate the development of advanced in situ analytical protocols to carry out a comprehensive survey of organic compounds in martian regolith, ices, and rocks. We combine pyrolysis GCMS for analysis of volatile species, chemical derivatization for transformation of less volatile organics, and laser desorption mass spectrometry (LDMS) for analysis of elements and more refractory, higher-mass organics. To evaluate this approach and enable a comparison with other measurement techniques we analyze organics in Mars simulant samples.

Mahaffy, P. R.↗
NASA NTRS2008

A State-of-the-Art Contamination Effects Research and Test Facility

In the ongoing effort to better understand various spacecraft contamination phenomena, a new state of the art contamination effects research and test facility was designed, and recently brought on-line at The Aerospace Corporation s Space Materials Laboratory. This high vacuum test chamber employs multiple in-situ analytical techniques, making it possible to study both the qualitative and quantitative aspects of contaminant film formation in the presence or absence of VUV radiation. Adsorption and desorption kinetics, "photo-fixing efficiency", transmission loss of uniform contaminant films, light scatter from non-uniform films, and film morphology have been studied in this facility. This paper describes this new capability in detail and presents data collected from several of the analytical instruments.

Olson, Keith R.↗
NASA NTRS2013

Two-step Laser Time-of-Flight Mass Spectrometry to Elucidate Organic Diversity in Planetary Surface Materials.

Laser desorption/ionization time-of-flight mass spectrometry (LD-TOF-MS) holds promise to be a low-mass, compact in situ analytical capability for future landed missions to planetary surfaces. The ability to analyze a solid sample for both mineralogical and preserved organic content with laser ionization could be compelling as part of a scientific mission pay-load that must be prepared for unanticipated discoveries. Targeted missions for this instrument capability include Mars, Europa, Enceladus, and small icy bodies, such as asteroids and comets.

Europa↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
NASA NTRS2000

ISHMAEL: In-Situ Sample Handling Modular Analytical Experimental Laboratory

In-Situ instruments are an integral part of mission designs for exploration of planetary surfaces. A technology gap exists today between sample acquisition and sample analysis tools. Integrated science payload packages need an integrated sample handling system.

microfluidics sample handling sample transport par↗
DOE OSTI2021

Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins

In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.

36 MATERIALS SCIENCE↗
NASA NTRS2024

Analytical Capabilities in-Situ Versus Mass of Returned Lunar Samples

This paper provides a brief overview of the science enabled both by conducting analyses in situ on the lunar surface and by returning lunar samples to Earth. Several examples illustrate how both in-situ and returned sample analyses can address the lunar/planetary science (LPS) goals of the Moon to Mars Objectives.

Andrew W Needham↗