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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mapping the Path Forward to Next Generation Algal Technologies: Workshop on Understanding the Rules of Life and Complexity in Algal Systems

Algal technology offers enormous potential as a source of renewable biomass and bioproducts for fuels, biochemicals, nutrients, and numerous other applications. While tremendous opportunities exist, significant challenges are also present in harnessing and utilizing this valuable and potentially sustainable bioproduction platform. Funded by a joint partnership between the Bioenergy Technologies Office (BETO) at the Department of Energy (DOE) and the National Science Foundation (NSF), the recent virtual workshop, "Understanding the Rules of Life: Complexity in Algal Systems Workshop," was organized to bring together thought leaders from academia, national laboratories and other government agencies, and industry to identify key challenges and propose research strategies to overcome these barriers. Two parallel sessions were undertaken: 1) algae strain and toolkit development and 2) algal ecosystem and microbiome dynamics, in order to advance algae engineering and cultivation efforts. In each session, attendees discussed the most pressing challenges from the perspective of model systems and omics analysis. Themes such as genetic engineering toolboxes, translation from the laboratory to outdoor cultivation, functional genomics, metabolomics, algal ecology, consortial design, microbiome dynamics, mathematical models, and data analytics emerged from the discussion. Overall, this workshop has served to elucidate the key hurdles and many opportunities present as we continue to advance algal science and technology from the laboratory and into the commercial realm.

09 BIOMASS FUELS↗
DOE OSTI2023

Assessing parallel path cooling tower performance via artificial neural networks

Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Data-driven prediction of α IIb β 3 integrin activation paths using manifold learning and deep generative modeling

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αI I b β3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. Furthermore, this work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multi-scale technique to predict transition pathways for biomolecular systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Membrane design for non-aqueous redox flow batteries: Current status and path forward

Redox flow batteries are promising technologies for large-scale, long-duration energy storage applications. Among them, non-aqueous redox flow batteries (NARFB) represent a transformative flow battery system since NARFBs potentially offer a higher energy density than aqueous flow batteries. However, many technical challenges remain for NARFBs, including the lack of high-performance membranes, low solubility of redox materials, and poor cycling efficiencies. Membranes serve a vital function in NARFBs, as they allow for selective ion transport while providing separation between the anolyte and catholyte. NARFB membrane development is an emerging research area, and this article reviews their design and critical factors that influence membrane properties, including solvent uptake, ion transport, and redox species permeability. A greater understanding of membrane behavior in non-aqueous solutions provides design principles for developing next-generation membranes for NARFB. In conclusion, we summarize the challenges, target metrics, and future perspectives for NARFBs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Modified alkaline peroxide pretreatment: An efficient path forward for bioethanol production from bamboo

To overcome the typical delignification saturation point of alkaline peroxide pretreatment and further facilitate lignin removal, a novel modified alkaline hydrogen peroxide pretreatment (MAHP) was proposed by introducing ethanol into the reaction system. The dosages of H 2 O 2 , ethanol, and pretreatment temperature were optimized, and the results revealed that a maximum lignin removal as high as 79.25% could be achieved at only 100 °C, 3 wt% H 2 O 2 concentration and 1 wt% ethanol concentration. Meanwhile, 76.5% of glucan and 56.0% of xylan were preserved at this pretreatment condition. By overcoming the delignification saturation point, enzymatic hydrolysis efficiency was remarkably enhanced, achieving 96.76% and 97.38% of glucan and xylan conversion, respectively, which are 7.4 and 11.4 times as compared to that of the untreated bamboo. Furthermore, the simultaneous saccharification and fermentation (SSF) result indicated an identical ethanol yield of ~75% when elevating the SSF solid loading from 5% to 30%. Based on the sequential SSF and xylose fermentation results, about 5.6 tons of bamboo would be consumed to produce 1 ton of ethanol. Finally, the energy balance revealed that a positive balance of 1255.4 KJ could be generated via processing 1 kg bamboo. Here, the results demonstrate that the MAHP is a promising high-efficiency pretreatment technology for bamboo due to the mild pretreatment severity and robust ethanol yield.

30 DIRECT ENERGY CONVERSION↗