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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Modeling methane emissions from arctic lakes: Model development and site‐level study

Abstract To date, methane emissions from lakes in the pan‐arctic region are poorly quantified. In order to investigate the response of methane emissions from this region to global warming, a process‐based climate‐sensitive lake biogeochemical model was developed. The processes of methane production, oxidation, and transport were modeled within a one‐dimensional sediment and water column. The sizes of 14 C‐enriched and 14 C‐depleted carbon pools were explicitly parameterized. The model was validated using observational data from five lakes located in Siberia and Alaska, representing a large variety of environmental conditions in the arctic. The model simulations agreed well with the measured water temperature and dissolved CH 4 concentration (mean error less than 1°C and 0.2 μM, respectively). The modeled CH 4 fluxes were consistent with observations in these lakes. We found that bubbling‐rate‐controlling nitrogen (N 2 ) stripping was the most important factor in determining CH 4 fraction in bubbles. Lake depth and ice cover thickness in shallow waters were also controlling factors. This study demonstrated that the thawing of Pleistocene‐aged organic‐rich yedoma can fuel sediment methanogenesis by supplying a large quantity of labile organic carbon. Observations and modeling results both confirmed that methane emission rate at thermokarst margins of yedoma lakes was much larger (up to 538 mg CH 4 m −2 d −1 ) than that at nonthermokarst zones in the same lakes and a nonyedoma, nonthermokarst lake (less than 42 mg CH 4 m −2 d −1 ). The seasonal variability of methane emissions can be explained primarily by energy input and organic carbon availability.

Tan, Zeli↗
DOE OSTI2016

Statistical tests of simple earthquake cycle models: Testing Earthquake Cycle Models

A central goal of observing and modeling the earthquake cycle is to forecast when a particular fault may generate an earthquake: a fault late in its earthquake cycle may be more likely to generate an earthquake than a fault early in its earthquake cycle. Models that can explain geodetic observations throughout the entire earthquake cycle may be required to gain a more complete understanding of relevant physics and phenomenology. Previous efforts to develop unified earthquake models for strike-slip faults have largely focused on explaining both preseismic and postseismic geodetic observations available across a few faults in California, Turkey, and Tibet. An alternative approach leverages the global distribution of geodetic and geologic slip rate estimates on strike-slip faults worldwide. Here we use the Kolmogorov-Smirnov test for similarity of distributions to infer, in a statistically rigorous manner, viscoelastic earthquake cycle models that are inconsistent with 15 sets of observations across major strike-slip faults. We reject a large subset of two-layer models incorporating Burgers rheologies at a significance level of α = 0.05 (those with long-term Maxwell viscosities η M <~ 4.0 × 10 19 Pa s and η M >~ 4.6 × 10 20 Pa s) but cannot reject models on the basis of transient Kelvin viscosity η K . Finally, we examine the implications of these results for the predicted earthquake cycle timing of the 15 faults considered and compare these predictions to the geologic and historical record.

58 GEOSCIENCES↗
DOE OSTI2017

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

13 HYDRO ENERGY↗
DOE OSTI2021

Siloxyaluminate and Siloxygallate Complexes as Models for Framework and Partially Hydrolyzed Framework Sites in Zeolites and Zeotypes.

Anionic molecular models for nonhydrolyzed and partially hydrolyzed aluminum and gallium framework sites on silica, M[OSi(OtBu) 3 ] 4 - and HOM[OSi(OtBu) 3 ] 3 - (where M=Al or Ga), were synthesized from anionic chlorides Li{M[OSi(OtBu) 3 ] 3 Cl} in salt metathesis reactions. Sequestration of lithium cations with [12]crown-4 afforded charge-separated ion pairs composed of monomeric anions M[OSi(OtBu) 3 ] 4 - with outer-sphere [([12]crown-4) 2 Li] + cations, and hydroxides {HOM[OSi(OtBu) 3 ] 3 } with pendant [([12]crown-4)Li] + cations. These molecular models were characterized by single-crystal X-ray diffraction, vibrational spectroscopy, mass spectrometry and NMR spectroscopy. Upon treatment of monomeric [([12]crown-4)Li]{HOM[OSi(OtBu) 3 ] 3 } complexes with benzyl alcohol, benzyloxide complexes were formed, modeling a possible pathway for the formation of active sites for Meerwin-Ponndorf-Verley (MPV) transfer hydrogenations with Al/Ga-doped silica catalysts.

Dombrowski, James P↗
DOE OSTI2022

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology – A Proof-of-Concept Model Development for Remdesivir

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. Here, the disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

60 APPLIED LIFE SCIENCES↗