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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Loss of P53 regresses cardiac remodeling induced by pressure overload partially through inhibiting HIF1α signaling in mice

The tumor suppressor p53 is recognized as the guardian of the genome in cell cycle and cell death. P53 expression increases as cardiac hypertrophy worsens to heart failure, suggesting that p53 may play important role in cardiac remodeling. In the present study, deletion of p53 in the mice heart would ameliorate cardiac hypertrophy induced by pressure overload. The role of p53 on heart was investigated using in vivo models. Cardiac hypertrophy in mice was induced by transverse aortic banding surgery. The extent of cardiac hypertrophy was examined by echocardiography, as well as pathological and molecular analyses of heart tissue. Global knockout of p53 in the mice reduced the hypertrophic response and markedly reduced cardiac apoptosis, and fibrosis. Ejection fraction of heart was also improved in hearts without p53 in response to pressure overload. Protein determination further suggested loss of p53 expression markedly increased Hypoxia-inducible factor 1-alpha (HIF1α) and vascular endothelial growth factor (VEGF) expression. The study indicated p53 deteriorated cardiac functions and cardiac hypertrophy, apoptosis, and fibrosis by partially inhibition of HIF1α and VEGF.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Correlation of rocket propulsion fuel properties with chemical composition using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry followed by partial least squares regression analysis

There is an increased need to more fully assess and control the composition of kerosene based rocket propulsion fuels, namely RP-1 and RP-2. In particular, it is crucial to be able to make better quantitative connections between the following three attributes: (a) fuel performance, (b) fuel properties (flash point, density, kinematic viscosity, net heat of combustion, hydrogen content, etc) and (c) the chemical composition of a given fuel (i.e., specific chemical compounds and compound classes present as a result of feedstock blending and processing). Indeed, recent efforts in predicting fuel performance through modeling put greater emphasis on detailed and accurate fuel properties and fuel compositional information. In this regard, advanced distillation curve (ADC) metrology provides improved data relative to classical boiling point and volatility curve techniques. Using ADC metrology, data obtained from RP-1 and RP-2 fuels provides compositional variation information that is directly relevant to predictive modeling of fuel performance. Often, in such studies, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is typically employed to provide chemical composition information. Building on approaches using GC-MS, but to glean substantially more chemical composition information from these complex fuels, we have recently studied the use of comprehensive two dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC - TOFMS) to provide chemical composition data that is significantly richer than that provided by GC-MS methods. In this report, by applying multivariate data analysis techniques, referred to as chemometrics, we are able to readily model (correlate) the chemical compositional information from RP-1 and RP-2 fuels provided using GC × GC - TOFMS, to the fuel property information such as that provided by the ADC method and other specification properties. We anticipate that this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an optimized approach to fuel formulation and specification for advanced engine cycles.

fuel↗
DOE OSTI2021

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cases where the target functions to be approximated exhibit high-frequency or multi-scale features. Here in this work we investigate this limitation through the lens of Neural Tangent Kernel (NTK) theory and elucidate how PINNs are biased towards learning functions along the dominant eigen-directions of their limiting NTK. Using this observation, we construct novel architectures that employ spatio-temporal and multi-scale random Fourier features, and justify how such coordinate embedding layers can lead to robust and accurate PINN models. Numerical examples are presented for several challenging cases where conventional PINN models fail, including wave propagation and reaction–diffusion dynamics, illustrating how the proposed methods can be used to effectively tackle both forward and inverse problems involving partial differential equations with multi-scale behavior.

42 ENGINEERING↗