PHM Society Conference Tutorial Session 1: A Guide to NASA The NASA Python Prognostics Packages
A guide to NASA Python Prognostics Packages
Prognostics↗
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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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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↗
Steroid responsiveness in alcohol-associated hepatitis is linked to glucocorticoid metabolism, mitochondrial repair, and heat shock proteins
Alcohol-associated hepatitis (AH) is one of the clinical presentations of alcohol-associated liver disease. AH has poor prognosis, and corticosteroids remain the mainstay of drug therapy. However, ~40% of patients do not respond to this treatment, and the mechanisms underlying the altered response to corticosteroids are not understood. The current study aimed to identify changes in hepatic protein expression associated with responsiveness to corticosteroids and prognosis in patients with AH. Patients with AH were enrolled based on the National Institute on Alcohol Abuse and Alcoholism inclusion criteria for acute AH and further confirmed by a diagnostic liver biopsy. Proteomic analysis was conducted on liver samples acquired from patients with AH grouped as nonresponders (AH-NR, n = 7) and responders (AH-R, n = 14) to corticosteroids, and nonalcohol-associated liver disease controls (n = 10). The definition of responders was based on the clinical prognostic model, the Lille Score, where a score < 0.45 classified patients as AH-R and a score > 0.45 as AH-NR. Primary outcomes used to assess steroid response were Lille Score (eg, improved liver function) and survival at 24 weeks. Reduced levels of the glucocorticoid receptor and its transcriptional co-activator, glucocorticoid modulatory element-binding protein 2, were observed in the hepatic proteome of AH-NR versus AH-R. The corticosteroid metabolizing enzyme, 11-beta-hydroxysteroid dehydrogenase 1, was increased in AH-NR versus AH-R along with elevated mitochondrial DNA repair enzymes, while several proteins of the heat shock pathway were reduced. Analysis of differentially expressed proteins in AH-NR who survived 24 weeks relative to AH-NR nonsurvivors revealed several protein expression changes, including increased levels of acute phase proteins, elevated coagulation factors, and reduced mast cell markers. This study identified hepatic proteomic changes that may predict responsiveness to corticosteroids and mortality in patients with AH.
59 BASIC BIOLOGICAL SCIENCES↗
A 12-year (1987-1998) Ensemble Simulation of the US Climate with a Variable Resolution Stretched Grid GCM
The variable-resolution stretched-grid (SG) GEOS (Goddard Earth Observing System) GCM has been used for limited ensemble integrations with a relatively coarse, 60 to 100 km, regional resolution over the U.S. The experiments have been run for the 12-year period, 1987-1998, that includes the recent ENSO cycles. Initial conditions 1-2 days apart are used for ensemble members. The goal of the experiments is analyzing the long-term SG-GCM ensemble integrations in terms of their potential in reducing the uncertainties of regional climate simulation while producing realistic mesoscales. The ensemble integration results are analyzed for both prognostic and diagnostic fields. A special attention is devoted to analyzing the variability of precipitation over the U.S. The internal variability of the SG-GCM has been assessed. The ensemble means appear to be closer to the verifying analyses than the individual ensemble members. The ensemble means capture realistic mesoscale patterns, especially those of induced by orography. Two ENSO cycles have been analyzed in terms their impact on the U.S. climate, especially on precipitation. The ability of the SG-GCM simulations to produce regional climate anomalies has been confirmed. However, the optimal size of the ensembles depending on fine regional resolution used, is still to be determined. The SG-GCM ensemble simulations are performed as a preparation or a preliminary stage for the international SGMIP (Stretched-Grid Model Intercomparison Project) that is under way with participation of the major centers and groups employing the SG-approach for regional climate modeling.
Fox-Rabinovitz, Michael S.↗