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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Design of Microwave Resonant Cavity Transducer. Development of sensor performance model of microwave cavity flow meter for advanced reactor high temperature fluids

High-temperature fluid reactors, such as molten salt cooled reactors (MSCR) and sodium fast reactors (SFR), are a promising advanced reactor option. Measurement of high-temperature fluid process variables, in particular the flow inside the pressure vessel, is a challenging task because of harsh environment, which includes high radiation, high temperature, and contact with highly corrosive coolant fluid. We are investigating a microwave cavity-based transducer for high-temperature fluid flow sensing. This sensor is a hollow metallic cylindrical cavity, which can be fabricated from stainless steel, and as such is expected to be resilient to radiation, high temperature and corrosive environment of MSCR and SFR. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. A cavity is characterized by its resonant frequencies. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. Feasibility of the flow sensor is evaluated with signal sensitivity using COMSOL computer simulations. A right cylinder geometry stainless steel cavity with dimeter of 0.8in was investigated. We choose membrane thickness of 10mil, so that corrosion anticipated to proceed at the rate of 1mil/year in liquid sodium would affect no more than 10% of the membrane. Using the properties of liquid sodium fluid, and stainless-steel material property values at 500oC, we calculate frequency shift for a range of values of fluid velocity from 0.5m/s to 2m/s. Results of computer simulations indicate measurable sensitivity to flow for this cavity design. Following these simulations, we have developed a preliminary design for fabrication of a transducer operating in microwave K-band for proof-of-principle tests.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Contaminant Transport Parameters for the Groundwater Flow and Contaminant Transport Model of Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada, Revision 1

This document presents a summary and framework of available transport data and other information directly relevant to the development of the groundwater transport model for Central and Western Pahute Mesa (PM) corrective action units (CAU). This model is referred to as the “PM CAU transport model” in this document. Central and Western PM are two areas of the Nevada National Security Site (NNSS) that historically were used for underground nuclear testing. These nuclear tests resulted in groundwater contamination in the vicinity of the underground test areas. As a result, the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program is currently conducting a corrective action investigation (CAI) of the PM underground test areas. This work is a part of the Underground Test Area (UGTA) Activity in accordance with the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Unclassified Source Term and Radionuclide Data for the Groundwater Flow and Contaminant Transport Model for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada, Revision 1

This report documents the evaluation of information and data available on the unclassified source term and radionuclide (RN) contamination for Central and Western Pahute Mesa (PM): Corrective Action Units (CAUs) 101 and 102. The Central and Western PM CAUs constitute two areas of the Nevada National Security Site (NNSS) used for underground nuclear testing (Figure 1-1). Eighty-five underground nuclear tests were conducted at the PM CAUs between 1965 and 1992 (NNSA/NFO, 2015b). Underground nuclear tests deposit radioactive elements in the subsurface, resulting in groundwater contamination in the vicinity of the underground test locations. At the PM CAUs, the testing resulted in more than 2.68 × 10 7 curies (Ci) of radioactivity (decay-corrected to September 30, 2012) remaining underground (Finnegan et al., 2016). Of the 85 underground nuclear tests conducted at the PM CAUs, three were shallow tests detonated within 200 meters (m) of the surface and referred to as the “near-surface tests”; the remaining 82 were detonated deeper than 200 m below ground surface (bgs) and are referred to as the “deep underground tests” in this document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

17 WIND ENERGY↗