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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Minimization of nanosatellite low frequency magnetic fields

Small weight and dimensions of the micro- and nanosatellites constrain researchers to place electromagnetic sensors on short booms or on the satellite body. Therefore the electromagnetic cleanliness of such satellites becomes a central question. This paper describes the theoretical base and practical techniques for determining the parameters of DC and very low frequency magnetic interference sources. One of such sources is satellite magnetization, the reduction of which improves the accuracy and stability of the attitude control system. We present design solutions for magnetically clean spacecraft, testing equipment, and technology for magnetic moment measurements, which are more convenient, efficient, and accurate than the conventional ones.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Sample-minimizing co-flow cell for time-resolved pump–probe X-ray solution scattering

A fundamental problem in biological sciences is understanding how macromolecular machines work and how the structural changes of a molecule are connected to its function. Time-resolved techniques are vital in this regard and essential for understanding the structural dynamics of biomolecules. Time-resolved small- and wide-angle X-ray solution scattering has the capability to provide a multitude of information about the kinetics and global structural changes of molecules under their physiological conditions. However, standard protocols for such time-resolved measurements often require significant amounts of sample, which frequently render time-resolved measurements impossible. A cytometry-type sheath co-flow cell, developed at the BioCARS 14-ID beamline at the Advanced Photon Source, USA, allows time-resolved pump–probe X-ray solution scattering measurements to be conducted with sample consumption reduced by more than ten times compared with standard sample cells and protocols. The comparative capabilities of the standard and co-flow experimental setups were demonstrated by studying time-resolved signals in photoactive yellow protein.

36 MATERIALS SCIENCE↗
DOE OSTI2014

MO-E-BRE-01: Determination, Minimization and Communication of Uncertainties in Radiation Therapy

Medical Physicists have a general understanding of uncertainties in the radiation treatment process, both with respect to dosimetry and geometry. However, there is a desire to be more quantitative about uncertainty estimation. A recent International Atomic Energy Agency (IAEA) report (about to be published) recommends that we should be as “accurate as reasonably achievable, technical and biological factors being taken into account”. Thus, a single recommendation as a goal for accuracy in radiation therapy is an oversimplification. That report also suggests that individual clinics should determine their own level of uncertainties for their specific treatment protocols. The question is “how do we implement this in clinical practice”? AAPM Monograph 35 (2011 AAPM Summer School) addressed many specific aspects of uncertainties in each of the steps of a course of radiation treatment. The intent of this symposium is: (1) to review uncertainty considerations in the entire radiation treatment process including uncertainty determination for each step and uncertainty propagation for the total process, (2) to consider aspects of robust optimization which optimizes treatment plans while protecting them against uncertainties, and (3) to describe various methods of displaying uncertainties and communicating uncertainties to the relevant professionals. While the theoretical and research aspects will also be described, the emphasis will be on the practical considerations for the medical physicist in clinical practice. Learning Objectives: To review uncertainty determination in the overall radiation treatment process. To consider uncertainty modeling and uncertainty propagation. To highlight the basic ideas and clinical potential of robust optimization procedures to generate optimal treatment plans that are not severely affected by uncertainties. To describe methods of uncertainty communication and display.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Implementation of flowsheet change to minimize hydrogen and ammonia generation during chemical processing of high level waste in the Defense Waste Processing Facility

Testing was completed to develop a chemical processing flowsheet for the Defense Waste Processing Facility (DWPF), designed to vitrify and stabilize high level radioactive waste. DWPF processing uses a reducing acid (formic acid) and an oxidizing acid (nitric acid) to rheologically thin the slurry and complete the necessary acid base and reduction reactions (primarily mercury and manganese). Formic acid reduces mercuric oxide to elemental mercury, allowing the mercury to be removed during the boiling phase of processing through steam stripping. In runs with active catalysts, formic acid can decompose to hydrogen and nitrate can be reduced to ammonia, both flammable gases, due to rhodium and ruthenium catalysis. Replacement of formic acid with glycolic acid eliminates the generation of rhodium- and ruthenium-catalyzed hydrogen and ammonia. In addition, mercury reduction is still effective with glycolic acid. Hydrogen, ammonia and mercury are discussed in the body of the report. Ten abbreviated tests were completed to develop the operating window for implementation of the flowsheet and determine the impact of changes in acid stoichiometry and the blend of nitric and glycolic acid as it impacts various processing variables over a wide processing region. Three full-length 4-L lab-scale simulations demonstrated the viability of the flowsheet under planned operating conditions. The flowsheet is planned for implementation in early 2017.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Deployment of Dynamic Neural Network Optimization to Minimize Heat Rate During Ramping for Coal Power Plants (Final Technical Report)

Much success was achieved throughout the course of this project. A successful implementation of Dynamic Neural Network Optimization (D-NNO) was coupled with Adaptive Predictive Controls (APC) and a novel hardware installation comprised of an advanced sensor network (ASN) measuring mass-weighted averages of flue gas constituents above the horizontal superheater of a coal-fired utility boiler. From 2019 through 2023 (including an extension due to COVID delays), the team was able to prototype, evaluate, deploy, iterate, and ultimately finalize an advanced closed-loop control D-NNO system which demonstrated the ability to: •improve unit efficiency ~2.0% relative to unoptimized operation (represented as total fuel fired per MWh generated) •improve unit NOx emission rates 10%+ beyond static optimization baselines •improve unit temperature stability as much as 58% and on average 12% •improve operating load stability as much as 35% The culmination of this project has generated an advanced methodology of deploying specially designed recurrent neural networks (long short-term memory, gated recurrent unit, encoder-decoder networks, transformers, etc.), customized trajectory planning and closed-loop optimization modules capable of adapting to live electric grid responses and demands, self-tuning and adaptive expert controls constantly adjusting prediction parameters to real-time unit behavior, and a hardware/software package able to reliably calculate net unit heat rate (NUHR) in real-time using flue gas constituents, machine learning, and known combustion relationships. Through this real-time NUHR value, immediate feedback on system adjustments relative to operating efficiency was available, allowing for rapid improvements to system performance. In addition to development and deployment of the advanced D-NNO system, the approach methodology has been readily commercialized through the project platform Griffin Open Systems, LLC, the D-NNO software platform host. Similar methodologies to those developed by this project have already been deployed at 5 other units across the United States, with another 6 implementations scheduled, and more expected. Over the course of the project, multiple academic papers were submitted and accepted for publication within esteemed academic journals, and PhD students were trained and graduated, as well as undergraduate students becoming involved and participating to project objectives.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Nuclear Energy Critical Material Waste Minimization Enabled by AM Techniques

This project provides evidence of the successful recycling of solid waste offcuts resulting from conventional manufacturing processes from three relevant alloys to next-generation nuclear reactor developers, providing a potentially upscalable circular process where no critical elements will be lost. Furthermore, iMOF-based adsorbents were successfully designed for CM extraction from aqueous solution, thereby providing a pathway for future upscaling for salvaging dissolved Ni ions. This research has achieved its goal of showing the impact of novel applications of recycling technologies for solid and liquid wastes that can be upscaled for application.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Minimizing Interfacial Recombination in 1.8 eV Triple‐Halide Perovskites for 27.5% Efficient All‐Perovskite Tandems

Abstract All‐perovskite tandem solar cells show great potential to enable the highest performance at reasonable costs for a viable market entry in the near future. In particular, wide‐bandgap (WBG) perovskites with higher open‐circuit voltage ( V OC ) are essential to further improve the tandem solar cells’ performance. Here, a new 1.8 eV bandgap triple‐halide perovskite composition in conjunction with a piperazinium iodide (PI) surface treatment is developed. With structural analysis, it is found that the PI modifies the surface through a reduction of excess lead iodide in the perovskite and additionally penetrates the bulk. Constant light‐induced magneto‐transport measurements are applied to separately resolve charge carrier properties of electrons and holes. These measurements reveal a reduced deep trap state density, and improved steady‐state carrier lifetime (factor 2.6) and diffusion lengths (factor 1.6). As a result, WBG PSCs achieve 1.36 V V OC , reaching 90% of the radiative limit. Combined with a 1.26 eV narrow bandgap (NBG) perovskite with a rubidium iodide additive, this enables a tandem cell with a certified scan efficiency of 27.5%.

14 SOLAR ENERGY↗
DOE OSTI2021

A Palette of Minimally Tagged Sucrose Analogues for Real-Time Raman Imaging of Intracellular Plant Metabolism

Sucrose is the main saccharide used for longdistance transport in plants and plays an essential role in energy metabolism; however, there are no analogues for real-time imaging in live cells. We have optimised a synthetic approach to prepare sucrose analogues including very small ( 50 Da or less) Raman tags in the fructose moiety. Spectroscopic analysis identified the alkyne-tagged compound 6 as a sucrose analogue recognised by endogenous transporters in live cells and with higher Raman intensity than other sucrose derivatives. Herein, we demonstrate the application of compound 6 as the first optical probe to visualise real-time uptake and intracellular localisation of sucrose in live plant cells using Raman microscopy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Realization of an Asymmetric Non‐Aqueous Redox Flow Battery through Molecular Design to Minimize Active Species Crossover and Decomposition

Abstract This communication presents a mechanism‐based approach to identify organic electrolytes for non‐aqueous redox flow batteries (RFBs). Symmetrical flow cell cycling of a pyridinium anolyte and a cyclopropenium catholyte resulted in extensive capacity fade due to competing decomposition of the pyridinium species. Characterization of this decomposition pathway enabled the rational design of next‐generation anolyte/catholyte pairs with dramatically enhanced cycling performance. Three factors were identified as critical for slowing capacity fade: (1) separating the anolyte–catholyte in an asymmetric flow cell using an anion exchange membrane (AEM); (2) moving from monomeric to oligomeric electrolytes to limit crossover through the AEM; and (3) removing the basic carbonyl moiety from the anolyte to slow the protonation‐induced decomposition pathway. Ultimately, these modifications led to a novel anolyte–catholyte pair that can be cycled in an AEM‐separated asymmetric RFB for 96 h with >95 % capacity retention at an open circuit voltage of 1.57 V.

Shrestha, Anuska↗
DOE OSTI2015

Experiences in autotuning matrix multiplication for energy minimization on GPUs

In this study, we report extensive results and analysis of autotuning the computationally intensive graphics processing units kernel for dense matrix–matrix multiplication in double precision. In contrast to traditional autotuning and/or optimization for runtime performance only, we also take the energy efficiency into account. For kernels achieving equal performance, we show significant differences in their energy balance. We also identify the memory throughput as the most influential metric that trades off performance and energy efficiency. Finally, as a result, the performance optimal case ends up not being the most efficient kernel in overall resource use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Minimal Membrane Metal Transport System: Dynamics and Energetics of mer Proteins

The mer operon in bacteria encodes a set of proteins and enzymes that impart resistance to environmental mercury toxicity by importing Hg 2+ and reducing it to volatile Hg(0). Because the reduction occurs in the cytoplasm, mercuric ions must first be transported across the cytoplasmic membrane by one of a few known transporters. MerF is the smallest of these, containing only two transmembrane helices and two pairs of vicinal cysteines that coordinate mercuric ions. In this work, we use molecular dynamics simulations to characterize the dynamics of MerF in its apo and Hg 2+ -bound states. We find that the apo state positions one of the cysteine pairs closer to the periplasmic side of the membrane, while in the bound state the same pair approaches the cytoplasmic side. This finding is consistent with the functional requirement of accepting Hg 2+ from the periplasmic space, sequestering it on acceptance, and transferring it to the cytoplasm. Conformational changes in the TM helices facilitate the functional interaction of the two cysteine pairs. Free-energy calculations provide a barrier of 16 kcal/mol for the association of the periplasmic Hg 2+ -bound protein MerP with MerF and 7 kcal/mol for the subsequent association of MerF's two cysteine pairs. Despite the significant conformational changes required to move the binding site across the membrane, coarse-grained simulations of multiple copies of MerF support the expectation that it functions as a monomer. Finally, our results demonstrate how conformational changes and binding thermodynamics could lead to such a small membrane protein acting as an ion transporter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Generalized Multiscale Finite Element Methods with energy minimizing oversampling

In this work, we propose a general concept for constructing multiscale basis functions within Generalized Multiscale Finite Element Method, which uses oversampling and stable decomposition. The oversampling refers to using larger regions in constructing multiscale basis functions and stable decomposition allows estimating the local errors. The analysis of multiscale methods involves decomposing the error by coarse regions, where each error contribution is estimated. In this estimate, we often use oversampling techniques to achieve a fast convergence. We demonstrate our concepts in the mixed, the Interior Penalty Discontinuous Galerkin, and Hybridized Discontinuous Galerkin discretizations. One of the important features of the proposed basis functions is that they can be used in online Generalized Multiscale Finite Element Method, where one constructs multiscale basis functions using residuals. In these problems, it is important to achieve a fast convergence, which can be guaranteed if we have a stable decomposition. In our numerical results, we present examples for both offline and online multiscale basis functions. Our numerical results show that one can achieve a fast convergence when using online basis functions. Moreover, we observe that coupling using Hybridized Discontinuous Galerkin provides a better accuracy compared with Interior Penalty Discontinuous Galerkin, which is due to using multiscale glueing functions.

42 ENGINEERING↗
DOE OSTI2019

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort

Scientific Named Entity Referent Extraction is often more complicated than traditional Named Entity Recognition (NER). For example, in polymer science, chemical structure may be encoded in a variety of nonstandard naming conventions, and authors may refer to polymers with conventional names, commonly used names, labels (in lieu of longer names), synonyms, and acronyms. As a result, accurate scientific NER methods are often based on task-specific rules, which are difficult to develop and maintain, and are not easily generalized to other tasks and fields. Machine learning models require substantial expert-annotated data for training. Here we propose polyNER: a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based word vector classifier. Evaluation on materials science publications shows that the polyNER approach enables improved precision or recall relative to a state-of-the-art chemical entity extraction system at a dramatically lower cost: it required just two hours of expert time, rather than extensive and expensive rule engineering, to achieve that result. This result highlights the potential for human-computer partnership for constructing domain-specific scientific NER systems.

Crowdsourcing↗