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Results for “life data analysis”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1970

Generation of long time creep data on refractory alloys at elevated temperatures

Creep tests were conducted on two tantalum alloys (ASTAR 811C and T-111 alloy), on a molybdenum alloy (TZM), and on CVD tungsten. The T-111 alloy 1% creep life data have been subjected to Manson's station function analysis, and the progress on this analysis is described. In another test program, the behavior of T-111 alloy with continuously varying temperatures and stresses has been studied. The results indicated that the previously described analysis predicts the observed creep behavior with reasonable accuracy. In addition to the T-111 test program, conventional 1% creep life data have been obtained for ASTAR 811C alloy. Previously observed effects of heat treatment on the creep strength of this material have been discussed and a model involving carbide strengthening primarily at the grain boundaries, rather than in a classical dispersion hardening mechanism, has been proposed to explain the observed results.

Sheffler, K. D.↗
DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗
NASA NTRS1989

Methods and decision making on a Mars rover for identification of fossils

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Eberlein, Susan↗
NASA NTRS2008

Probabilistic Analysis of Space Shuttle Body Flap Actuator Ball Bearings

A probabilistic analysis, using the 2-parameter Weibull-Johnson method, was performed on experimental life test data from space shuttle actuator bearings. Experiments were performed on a test rig under simulated conditions to determine the life and failure mechanism of the grease lubricated bearings that support the input shaft of the space shuttle body flap actuators. The failure mechanism was wear that can cause loss of bearing preload. These tests established life and reliability data for both shuttle flight and ground operation. Test data were used to estimate the failure rate and reliability as a function of the number of shuttle missions flown. The Weibull analysis of the test data for the four actuators on one shuttle, each with a 2-bearing shaft assembly, established a reliability level of 96.9 percent for a life of 12 missions. A probabilistic system analysis for four shuttles, each of which has four actuators, predicts a single bearing failure in one actuator of one shuttle after 22 missions (a total of 88 missions for a 4-shuttle fleet). This prediction is comparable with actual shuttle flight history in which a single actuator bearing was found to have failed by wear at 20 missions.

Oswald, Fred B.↗
DOE OSTI2021

A data engineering approach for sustainable chemical end-of-life management

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. Furthermore, a case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e.g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗
DOE OSTI2023

Predicting fatigue from heart rate signatures using functional logistic regression

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Assessment of Accelerated Stress Testing Data for Silicon Photovoltaics Using Tensor Decomposition Methods

In this work, we examine the use of high-order tensor decompositions to analyze degradation pathways emerging from accelerated stress testing of silicon photovoltaic (PV) modules. Matrix-based decompositions are powerful tools for studying two-dimensional data arrays and form the foundation of a host of classical data analysis techniques. Tensors are high-order extrapolations of matrices that are able to account for more parameter dimensions, and a variety of tensor decomposition methods have been developed that similarly seek to extend insights from matrix decompositions to higher dimensions. Applying and interpreting tensor decomposition methods to sequences of PV module image data, we seek to uncover and isolate different degradation modes occurring from accelerated stress testing procedures. Further, we consider the contributions of different modes to PV module performance degradations.

data analysis↗
DOE OSTI2023

A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. Here, the proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.

Data balancing↗
NASA NTRS1974

A procedure for verifying the structural integrity of an existing pressurized wind tunnel

The present work describes the approach employed in verifying the structural integrity at maximum operating pressure of a low turbulence pressurized wind tunnel built in 1939-1940. The approach utilized existing ASME Code guidelines and fracture mechanics techniques in combination with the latest nondestructive examination methods to validate the desired life of the facility while minimizing reconstruction or major modifications. Detailed stress analyses using a finite element computer program and a numerical integration program were performed on critical areas of the tunnel. Critical vessel areas were evaluated for remaining fatigue life. Fracture mechanics analyses of the shell were performed at various regions, and critical flaw sizes at each location were determined. Analysis of the test data permitted the remaining useful life of the tunnel to be estimated at 8 to 10 years.

Taylor, J. T.↗
NASA NTRS1993

Earthbound applications for NASA's physician workstation

The dream of a space probe to Mars or an astronaut colony on the moon persists. Despite years of setbacks and delays, NASA continues to lay the foundation for a new frontier in space. The necessity of a self contained health maintenance facility is an integral part of this stellar venture. As a subsystem of this health maintenance facility, the physician or astronaut workstation was envisioned as the vehicle of interface between the computer resources of the space station and the care provider. Our efforts to define and build this interface have resulted in a series of programs which can now be tested and refined using earth-based applications. The modules which have dual-use application from the NASA workstation include: patient scheduling and master patient index, pharmacy, laboratory, medical library, problem list/progress notes, and digital medical records. Our current plan is to develop these tools as objects that can be assembled in a variety of configurations. This will allow the technology to be used by the private sector where each doctor can select the starting point of his outpatient office system and add modules as he makes progress in system integration and training.

NASA Discipline Number 70-30↗
NASA NTRS1945

An analysis of life expectancy of airplane wings in normal cruising flight

In order to provide a basis for judging the relative importance of wing failure by fatigue and by single intense gusts, an analysis of wing life for normal cruising flight was made based on data on the frequency of atmospheric gusts. The independent variables considered in the analysis included stress-concentration factor, stress-load relation, wing loading, design and cruising speeds, design gust velocity, and airplane size. Several methods for estimating fatigue life from gust frequencies are discussed. The procedure selected for the analysis is believed to be simple and reasonably accurate, though slightly conservative.

Putnam, Abbott A↗
NASA NTRS2018

Temporal RNA Integrity Analysis of Archived Spaceflight Biological Samples from ALSDA from 1991 to 2016

The purpose of this study is to assess the quality of spaceflight tissues stored in Ames Life Science Data Archive (ALSDA) freezers. Garnering information for downstream functional analysis such as generation of omics datasets from tissues is, in part, dependent on the state of sample preservation. To assess the viability of a select group of tissues, RNA integrity number (RIN) values were calculated for RNA extracted from rodent livers. Rat livers from Spacelab Life Sciences 1 (SLS-1) and mouse livers from Commercial Biomedical Test Module 3 (CBTM-3), Rodent Research 1 (RR1), and Rodent Research 3 (RR3) were tested. It was found that mean RIN values from CBTM3, RR1, and RR3 were suitable for downstream functional analysis (RIN greater than 5) while the mean RIN value for SLS-1 was not (RIN equal to 2.5 plus or minus 0.1). Information from this study could lay the foundation for future efforts in determining the types of assays that are most appropriate for different tissues in ALSDA freezers, which would maximize the scientific return on rare spaceflight samples.

ALSDA↗
NASA NTRS2002

The origin and evolution of model organisms

The phylogeny and timescale of life are becoming better understood as the analysis of genomic data from model organisms continues to grow. As a result, discoveries are being made about the early history of life and the origin and development of complex multicellular life. This emerging comparative framework and the emphasis on historical patterns is helping to bridge barriers among organism-based research communities.

Review, Academic↗
DOE OSTI2025

ampworks: Battery analysis tools in Python [SWR-25-39]

Ampworks is a collection of tools designed to process experimental battery data with a focus on model-relevant analyses. It currently provides functions for incremental capacity analysis and GITT data processing, helping extract key properties for life and physics-based models (e.g., SPM and P2D). Some tools, like the incremental capacity analysis module, also include graphical user interfaces for ease of use. https://github.com/NREL/ampworks/ https://pypi.org/project/ampworks/

Randall, Corey [National Renewable Energy Laborato↗
NASA NTRS2007

DSN Antenna Array Architectures Based on Future NASA Mission Needs

A flexible method of parametric, full life-cycle cost analysis has been combined with data on NASA's future communication needs to estimate the required number and operational dates of new antennas for the Deep Space Network (DSN). The requirements were derived from a subset of missions in the Integrated Mission Set database of NASA's Space Communications Architecture Working Group. Assuming that no new antennas are 'constructed', the simulation shows that the DSN is unlikely to meet more than 20% of mission requirements by 2030. Minimum full life-cycle costs result when antennas in the diameter range, 18m-34m, are constructed. Architectures using a mixture of antenna diameters produce a slightly lower full life-cycle cost.

array↗
NASA NTRS2007

Space Environment Effects on Stability of Medications Flown on Space Shuttles and the International Space Station (ISS)

The purpose is to assess physical and chemical degradation of select pharmaceutical formulations from the Shuttle and ISS medical kits. Eleven pharmaceuticals dispensed as different dosage forms were selected based on their physical / chemical characteristics and susceptibility to environmental factors such as, temperature, humidity and light sensitivity. When available, ground-controls of the study medications with matching brand and lot numbers were used for comparison. Samples retrieved from flight were stored along with their matching controls in a temperature and humidity controlled environmental chamber. Temperature, humidity, and radiation data from the Shuttle and ISS were retrieved from onboard HOBO U12 Temp/RH Data Loggers, and from passive dosimeters. Physical and chemical analyses of the pharmaceuticals were conducted using validated United States Pharmacopeia (USP) methods. Results indicated degradation of 6 of the 11 formulations returned from space flights. Four formulations, Amoxicillin / Clavulanate, promethazine, sulfamethoxazole / trimethoprim, and ciprofloxacin tablets depicted discoloration after flight. Chemical content analyses using High or Ultra Performance Liquid Chromatography (HPLC / UPLC) methods revealed that dosage forms of Amoxicillin / Clavulanate, promethazine, sulfamethoxazole / trimethoprim, lidocaine, ciprofloxacin and mupirocin contained less than 95% of manufacturer s labeled claim of active drug compound. Shuttle and ISS environments affect stability and shelf life of certain mediations flown on these missions. Data analysis is in progress to examine the effect of specific space flight environmental factors on pharmaceutical stability. The degradation profiles generated from ground studies in analog environments will be useful in establishing predictive shelf-life profiles for medications intended for use during long-term space exploration missions.

Daniels, Vernie↗