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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
DOE OSTI2023

Combining Agent Based Modeling and System Dynamics to Investigate the Circularity of Plastics

The United States currently produces about 1 million metric ton of ocean plastic pollution annually. One proposed solution to combat plastic waste is a circular economy (CE), which aims to transition from today's take-make-waste linear pattern of production and consumption to a system where the value of resources is maximized over time. Two key methods in industrial ecology are useful in assessing the viability of CE: (1) System Dynamics (SD) and (2) Agent Based Modeling (ABM). In prior work, the plastic life cycle was modeled with SD and ABM. The two models calculate recycling rates and costs in different ways, making it difficult to pinpoint necessary next steps. We integrate the ABM and SD models - linking the emergent patterns from micro-level human decisions to system level processes - which allows a more comprehensive understanding of feedbacks, costs, and environmental impacts. The integrated model is more accurate, and can be used to visualize recycling rates and human health and environmental impacts over time. The difference between the integrated and original SD model prompts a Sobol sensitivity analysis, which is used to understand which behavioral factors most affect plastic recycling patterns. We find that the habitual component is typically the most influential in promoting positive recycling behavior. Additionally, we utilize the combined model to understand and visualize how various behavioral intervention scenarios, like improved access to recycling programs and cart tagging, influence recycling patterns; these results can guide future policy-making.

agent-based modeling↗
DOE OSTI2025

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 $\frac{𝑀𝐻𝑧}{°C}$. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1992

Frequency response measurements in battery electrodes

Electrical impedance spectroscopy was used to investigate the behavior of porous zinc, silver, cadmium, and nickel electrodes. State of charge could be correlated with impedance data for all but the nickel electrodes. State of health was correlated with impedance data for two AgZn cells, one apparently good and the other bad. The impedance data was fit to equivalent circuit models.

Thomas, Daniel L.↗
NASA NTRS2018

Development of a Ground Test and Analysis Protocol for NASA's NextSTEP Phase 2 Habitation Concepts

The NASA Next Space Technologies for Exploration Partnerships (NextSTEP) program is a public-private partnership model that seeks commercial development of deep space exploration capabilities to support human spaceflight missions around and beyond cislunar space. NASA first issued the Phase 1 NextSTEP Broad Agency Announcement to U.S. industries in 2014, which called for innovative cislunar habitation concepts that leveraged commercialization plans for low-Earth orbit. These habitats will be part of the Deep Space Gateway (DSG), the cislunar space station planned by NASA for construction in the 2020s. In 2016, Phase 2 of the NextSTEP program selected five commercial partners to develop ground prototypes. A team of NASA research engineers and subject matter experts (SMEs) have been tasked with developing the ground-test protocol that will serve as the primary means by which these Phase 2 prototypes will be evaluated. Since 2008, this core test team has successfully conducted multiple spaceflight analog mission evaluations utilizing a consistent set of operational tools, methods, and metrics to enable the iterative development, testing, analysis, and validation of evolving exploration architectures, operations concepts, and vehicle designs. The purpose of implementing a similar evaluation process for the Phase 2 Habitation Concepts is to consistently evaluate different commercial partner ground prototypes to provide data-driven, actionable recommendations for Phase 3. This paper describes the process by which the ground test protocol was developed and the objectives, methods, and metrics by which the NextSTEP Phase 2 Habitation Concepts will be rigorously and systematically evaluated. The protocol has been developed using both a top-down and bottom-up approach. Top-down development began with the Human Exploration and Operations Mission Directorate (HEOMD) exploration objectives and ISS Exploration Capability Study Team (IECST) candidate flight objectives. Strategic questions and associated rationales, derived from these candidate architectural objectives, provide the framework by which the ground-test protocol will address the DSG stack elements and configurations, systems and subsystems, and habitation, science, and EVA functions. From these strategic questions, high-level functional requirements for the DSG were drafted and associated ground-test objectives and analysis protocols were established. Bottom-up development incorporated objectives from NASA SMEs in autonomy, avionics and software, communication, environmental control and life support systems, exercise, extravehicular activity, exploration medical operations, guidance navigation and control, human factors and behavioral performance, human factors and habitability, logistics, Mission Control Center operations, power, radiation, robotics, safety and mission assurance, science, simulation, structures, thermal, trash management, and vehicle health. Top-down and bottom-up objectives were integrated to form overall functional requirements - ground-test objectives and analysis mapping. From this mapping, ground-test objectives were organized into those that will be evaluated through inspection, demonstration, analysis, subsystem standalone testing, and human-in-the-loop (HITL) testing. For the HITL tests, mission-like timelines, procedures, and flight rules have been developed to directly meet ground test objectives and evaluate specific functional requirements. Data collected from these assessments will be analyzed to determine the acceptability of habitation element configurations and the combinations of capabilities that will result in the best habitation platform to be recommended by the test team for Phase 3.

Gernhardt, Michael L.↗
NASA NTRSDate not supplied

Sensor-based Prognostics for Safe Batteries for Electric Aircraft

With a new start awarded under CAS for Fiscal Year 2020, the Sensor-based Prognostics to Avoid Runaway Reactions and Catastrophic Ignition project, or SPARRCI, aims to eliminate safety risks of lithium-ion and next-generation lithium-based batteries. SPARRCI is taking a deeper dive into the inner workings of battery cells by introducing miniature multifunctional sensors within the cells to detect early signs of failure. Combined with nondestructive evaluation techniques to gather complementary battery health data, battery data will be monitored in real-time via prognostics which will ultimately be able to identify when the cell is exhibiting abnormal behavior bordering on failure, with the ability to provide early warning such that the battery may be taken offline prior to catastrophic failure events.

Brianne Demattia↗
DOE OSTI2025

Aging matrix visualizes complexity of battery aging across hundreds of cycling protocols

To reliably deploy lithium-ion batteries, a fundamental understanding of cycling aging behavior is critical. Battery aging consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 359 high energy density commercial Li(Ni,Co,Al)O 2 /graphite + SiO x cylindrical 21 700 cells cycled across 207 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory metrics. We develop a framework using interpretable machine learning and explainable features to generate an aging matrix that visually deconvolutes the complex battery degradation behavior. This generalizable data-driven mechanistic framework simplifies the complex interplay between cycling conditions, degradation modes, and SOH, acting as a hypothesis-generation tool to aid battery users in identifying key degradation regimes for further study and experimentation.

25 ENERGY STORAGE↗
NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Banerjee, Portia↗
NASA NTRS2011

Crew Health and Performance Improvements with Reduced Carbon Dioxide Levels and the Resource Impact to Accomplish Those Reductions

Carbon dioxide (CO2) removal is one of the primary functions of the International Space Station (ISS) atmosphere revitalization systems. Primary CO2 removal is via the ISS s two Carbon Dioxide Removal Assemblies (CDRAs) and the Russian carbon dioxide removal assembly (Vozdukh); both of these systems are regenerable, meaning that their CO2 removal capacity theoretically remains constant as long as the system is operating. Contingency CO2 removal capability is provided by lithium hydroxide (LiOH) canisters, which are consumable, meaning that their CO2 removal capability disappears once the resource is used. With the advent of 6 crew ISS operations, experience showing that CDRA failures are not uncommon, and anecdotal association of crew symptoms with CO2 values just above 4 mmHg, the question arises: How much lower do we keep CO2 levels to minimize the risk to crew health and performance, and what will the operational cost to the CDRAs be to do it? The primary crew health concerns center on the interaction of increased intracranial pressure from fluid shifts and the increased intracranial blood flow induced by CO2. Typical acute symptoms include headache, minor visual disturbances, and subtle behavioral changes. The historical database of CO2 exposures since the beginning of ISS operations has been compared to the incidence of crew symptoms reported in private medical conferences. We have used this database in an attempt to establish an association between the CO2 levels and the risk of crew symptoms. This comparison will answer the question of the level needed to protect the crew from acute effects. As for the second part of the question, operation of the ISS s regenerable CO2 removal capability reduces the limited life of constituent parts. It also consumes limited electrical power and thermal control resources. Operation of consumable CO2 removal capability (LiOH) uses finite consumable materials, which must be replenished in the long term. Therefore, increased CO2 removal means increased resource use, with increased logistical capability to maintain necessary resources on board ISS. We must strike a balance between sufficiently low CO2 levels to maintain crew health and CO2 levels which are operationally feasible for the ISS program

James, John T.↗
NASA NTRSDate not supplied

Oxidative Stress and the Neuroconsequences of Spaceflight Environment - Immune Dysregulation and Antioxidant Dietary Countermeasure Efficacy

In this project, we will test the hypothesis that Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure and function, and neurobehavioral and cognitive performance. Specific Aims for this project are: (1) Determine dose-response curves for acute ‘Five-Ion GCR Simulation’ exposure for immune, brain and performance responses in crew age-matched adult male and female mice; (2) Determine effects of acute ‘Five-Ion GCR Simulation’ exposure singly and in combination with simulated microgravity and social isolation, on immune, brain and performance responses in crew age-matched male and female mice mimicking deep space missions; and (3) Determine efficacy of the dietary antioxidant, Nicotinamide Mononucleotide (NMN), a key intermediate in nicotinamide adenine dinucleotide (NAD+) biosynthesis. The project relies on established and highly translatable ground-based mouse models and assays with IR exposures to be performed at the NASA Space Radiation Laboratory (NSRL). The experimental approach will provide definitive data on the timing and mechanisms involved in the oxidative stress response, immune and brain changes, and ensuing functional (behavioral/cognitive) impairments expected during human transit to Mars. This project will identify potential biomarkers for, and mechanisms underlying, structural and functional changes in the immune and nervous systems leading to behavioral/cognitive performance deficits, and its potential application to develop effective countermeasures to mitigate negative health effects of long duration space habitation. The project will address NASA’s efforts to rapidly advance the characterization of risks and identify appropriate countermeasures in anticipation of future deep space missions. Ensuring crew health and performance during extended transits necessitates that sensorimotor and cognitive abilities remain strong to avoid potentially catastrophic health and safety outcomes. Further, despite historically low numbers of female astronauts, the two most recent NASA Astronaut Corps class selections, comprised of ~50% women as compared to men, signal the need to understand how sex differences affect physiological adaptation and health in the space environment. Here, we will determine how key features of the deep space environment may interact to increase risk to crewmembers by negatively impacting health and performance, and identify and develop strategies to characterize and mitigate the potential risks via countermeasures. Supported by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18-18FLAG_2-0028.

radiation↗
NASA NTRSDate not supplied

Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation

National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.

Machine Learning (ML)↗
NASA NTRS2001

Research activities at the Loma Linda University and Proton Treatment Facility--an overview

The Loma Linda University (LLU) Radiobiology Program coordinates basic research and proton beam service activities for the university and extramural communities. The current focus of the program is on the biological and physical properties of protons and the operation of radiobiology facilities for NASA-sponsored projects. The current accelerator, supporting facilities and operations are described along with a brief review of extramural research projects supported by the program. These include space craft electronic parts and shielding testing as well as tumorigenesis and animal behavior experiments. An overview of research projects currently underway at LLU is also described. These include: 1) acute responses of the C57Bl/6 mouse immune system, 2) modulation of gene expression in the nematode C. elegans and rat thyroid cells, 3) quantitation of dose tolerance in rat CNS microvasculature, 4) behavioral screening of whole body proton and iron ion-irradiated C57Bl/6 mice, and 5) investigation of the role of cell integration into epithelial structures on responses to radiation.

Non-NASA Center↗
NASA NTRS2005

Integrated Systems Health Management for Space Exploration

Integrated Systems Health Management (ISHM) is a system engineering discipline that addresses the design, development, operation, and lifecycle management of components, subsystems, vehicles, and other operational systems with the purpose of maintaining nominal system behavior and function and assuring mission safety and effectiveness under off-nominal conditions. NASA missions are often conducted in extreme, unfamiliar environments of space, using unique experimental spacecraft. In these environments, off-nominal conditions can develop with the potential to rapidly escalate into mission- or life-threatening situations. Further, the high visibility of NASA missions means they are always characterized by extraordinary attention to safety. ISHM is a critical element of risk mitigation, mission safety, and mission assurance for exploration. ISHM enables: In-space maintenance and repair; a) Autonomous (and automated) launch abort and crew escape capability; b) Efficient testing and checkout of ground and flight systems; c) Monitoring and trending of ground and flight system operations and performance; d) Enhanced situational awareness and control for ground personnel and crew; e) Vehicle autonomy (self-sufficiency) in responding to off-nominal conditions during long-duration and distant exploration missions; f) In-space maintenance and repair; and g) Efficient ground processing of reusable systems. ISHM concepts and technologies may be applied to any complex engineered system such as transportation systems, orbital or planetary habitats, observatories, command and control systems, life support systems, safety-critical software, and even the health of flight crews. As an overarching design and operational principle implemented at the system-of-systems level, ISHM holds substantial promise in terms of affordability, safety, reliability, and effectiveness of space exploration missions.

Uckun, Serdar↗
NASA NTRS2005

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Iverson, David L.↗
NASA NTRS2015

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders

The NASA commitment to human space flight includes continuing to fly astronauts on the ISS until it is decommissioned as well as possibly returning astronauts to the moon or having astronauts venture to an asteroid or Mars. As missions leave low Earth orbit and explore deeper space, BHP supports and conducts research to enable a risk posture that considers the risk of adverse cognitive or behavioral conditions and psychiatric disorders “acceptable given mitigations,” for pre-, in, and post-flight.The Human System Risk Board (HSRB) determines the risk of various mission scenarios using a likelihood (per person per year) by consequences matrix examining those risks across two categories—long term health and operational (within mission). Colors from a stoplight signal are used by HSRB and quickly provide a means of assessing overall perceived risk for a particular mission scenario. Risk associated with the current six month missions on the ISS are classified as “accepted with monitoring” while planetary missions, such as a mission to Mars, are recognized to be a “red” risk that requires mitigation to ensure mission success.Currently, the HSRB deems that the risk of adverse cognitive or behavioral conditions and psychiatric outcomes requires mitigation for planetary missions owing to long duration isolation and radiation exposure (see Table 1). While limited research evidence exists from spaceflight, it is well known anecdotally that the shift from the two week shuttle missions to the six month ISS missions renders the psychological stressors of space as more salient over longer duration missions. Shuttle astronauts were expected just to tolerate any stressors that arose during their mission and were successful at doing so (Whitmire et al, 2013). While it is possible to deal with stressors such as social isolation and to live with incompatible crewmembers for two weeks on shuttle, “ignoring it” is much less likely to be a successful coping mechanism on station. For the longer missions of the ISS, astronauts require a larger, more robust set of coping skills and more psychological support. Evidence of this are the number of BHP’s Operational Psychology (Op Psy) staff who have been awarded silver Snoopys by long duration astronauts†, in the statements of praise for the Op Psy and Family Support Office teams, and in the written and oral statements from flown astronauts regarding difficulty of longer missions and how much Op Psy helped.

Slack, Kelley J.↗
DOE OSTI2026

Diagnostics, Prognostics, and Optimization for Lithium-Ion Battery Systems

Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NLR's industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed.

25 ENERGY STORAGE↗
NASA NTRS2019

Deciphering the Effects of Microgravity Cell by Cell

Forces generated by gravity have a profound impact on the behavior of cells in tissues affecting the course of the cell cycle and differentiation fate of progenitors in mammalian tissues. These cells are contributing to normal tissue regenerative health and defence against disease. In Human space exploration context, it is extremely important to determine spaceflight provoked changes in tissue's regenerational capabilities. Microgravity experienced during spaceflight causes unloading and mechanical disuse on all orthostatic support tissues, therefore impacting stem cell fate and lineage commitment decisions. Investigating how ESCs respond to mechanical stimulation is a platform for fundamental developmental and regeneration research applicable for spaceflight. However, the gene expression programs associatiated with early committment stem cell pathways in response to physical stimulation are not readily known. Single-cell RNA-seq technologies have recently revolutionized the world of molecular biology by providing the capability to assess gene expression pattern within a single cell. Our method isolates and separately barcodes mRNAs from thousands of single cells and sequences their expressomes. Understanding regenerative processes on a molecular level would not only help reduce long-term spaceflight impact on health, but also may enable the development of novel tissue regenerative approaches to tissue degeneration on Earth.

Single cell sequencing↗
DOE OSTI2024

From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health

With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 MATHEMATICS AND COMPUTING↗