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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A novel vacuum spectrometer for total reflection x-ray fluorescence analysis with two exchangeable low power x-ray sources for the analysis of low, medium, and high Z elements in sequence

The extension of the detectable elemental range with Total Reflection X-ray Fluorescence (TXRF) analysis is a challenging task. In this paper, it is demonstrated how a TXRF spectrometer is modified to analyze elements from carbon to uranium. Based on the existing design of a vacuum TXRF spectrometer with a 12 specimen sample changer, the following components were renewed: the silicon drift detector with 20 mm{sup 2} active area and having a special ultra-thin polymer window allowing the detection of elements from carbon upwards. Two exchangeable X-ray sources guarantee the efficient excitation of both low and high Z elements. These X-ray sources were two light-weighted easily mountable 35 W air-cooled low-power tubes with Cr and Rh anodes, respectively. The air cooled tubes and the Peltier-cooled detector allowed to construct a transportable tabletop spectrometer with compact dimensions, as neither liquid nitrogen cooling for the detector nor a water cooling circuit and a bulky high voltage generator for the X-ray tubes are required. Due to the excellent background conditions as a result of the TXRF geometry, detection limits of 150 ng for C, 12 ng for F, and 3.3 ng for Na have been obtained using Cr excitation in vacuum. For Rh excitation, the detection limits of 90 pg could be achieved for Sr. Taking 10 to 20 μl of sample volume, extrapolated detection limits in the ng/g (ppb) range are resulting in terms of concentration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia

Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging. Here in this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets. The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration. The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test. With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

OrfM: a fast open reading frame predictor for metagenomic data

Finding and translating stretches of DNA lacking stop codons is a task common in the analysis of sequence data. However, the computational tools for finding open reading frames are sufficiently slow that they are becoming a bottleneck as the volume of sequence data grows. This computational bottleneck is especially problematic in metagenomics when searching unassembled reads, or screening assembled contigs for genes of interest. Here, we present OrfM, a tool to rapidly identify open reading frames (ORFs) in sequence data by applying the Aho–Corasick algorithm to find regions uninterrupted by stop codons. Benchmarking revealed that OrfM finds identical ORFs to similar tools (‘GetOrf’ and ‘Translate’) but is four-five times faster. While OrfM is sequencing platform-agnostic, it is best suited to large, high quality datasets such as those produced by Illumina sequencers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Mapping and controlling ultrafast dynamics of highly excited H 2 molecules by VUV-IR pump-probe schemes

Here, we used ultrashort femtosecond vacuum ultraviolet (VUV) and infrared (IR) pulses in a pump-probe scheme to map the dynamics and nonequilibrium dissociation channels of excited neutral H 2 molecules. A nuclear wave packet is created in the B 1 Σ$+\atop{u}$ state of the neutral H 2 molecule by absorption of the ninth harmonic of the driving infrared laser field. Due to the large stretching amplitude of the molecule excited in the B 1 Σ$+\atop{u}$ electronic state, the effective H 2 + ionization potential changes significantly as the nuclear wave packet vibrates in the bound, highly electronically and vibrationally excited B potential-energy curve. We probed such dynamics by ionizing the excited neutral molecule using time-delayed VUV-or-IR radiation. We identified the nonequilibrium dissociation channels by utilizing three-dimensional momentum imaging of the ion fragments. We also found that different dissociation channels can be controlled, to some extent, by changing the IR laser intensity and by choosing the wavelength of the probe laser light. Furthermore, we concluded that even in a benchmark molecular system such as H 2 *, the interpretation of the nonequilibrium multiphoton and multicolor ionization processes is still a challenging task, requiring intricate theoretical analysis.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application-specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. Here, we present the software-defined accelerator (SODA) synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multilevel representations to progressively lower and optimize the input code. Our approach does not require the application developer to write any register-transfer level code, and it is able to reach up to 364 giga floating point operations per second (GFLOPS)/W efficiency (32-bit precision) on typical convolutional neural network operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗
DOE OSTI2016

Net Shape Fabricated Low Cost MHK Pass-Through the Hub Turbine Blades with Integrated Health Management Technology

The primary objective of this project is to develop a three-blade MHK rotor with low manufacturing and maintenance costs. The proposed program will design, fabricate and test a novel half-scale low cost, net shape fabricated single piece three-blade MHK rotor with integrated health management technology to demonstrate significant Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) cost reductions due to the novel design and manufacturing process. The proposed project is divided into three major tasks: Task 1: Single Piece Three-blade Kinetic Hydropower System (KHPS) Rotor Full-Scale and Half-Scale Design; Task 2: Composite Manufacturing Trials and Half-Scale Prototype Rotor Fabrication; and Task 3: Material Characterization and Half-Scale Prototype Test and Evaluation. These three tasks include design and analysis of full-scale and half-scale three-blade rotor prototypes using computational fluid dynamics (CFD) and finite-element analysis (FEA), demonstration of a novel half-scale net shape fabrication process, determination of a fatigue threshold composite strain allowable, three-blade rotor mold design, manufacture of half-scale rotor clam shell mold, three-blade rotor test fixture design and fabrication, development of final manufacturing and test plans, manufacture of the half-scale net shape composite single blade and three-blade prototypes, and test and evaluation of the half-scale rotor.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Energy Saving Glass Lamination via Selective Radio Frequency Heating

This project focused on advancing radio-frequency (RF) lamination technology closer to commercial implementation, in order to reduce the energy intensity of glass lamination by up to 90%. Lamination comprises a wide range of products including autoglass, architectural safety and innovative design glass, transparent armor (e.g. bullet proof glass), smart glass, mirrors, and encapsulation of photovoltaics. Lamination is also the fastest growing segment of glass manufacturing, with photovoltaics, architectural needs, and an anticipated transition to laminated side windows in vehicles. The state-of-the-art for glass lamination is to use autoclaves, which apply heat and uniform gas pressure to bond the laminates over the course of 1 to 18 hours. Laminates consist of layers of glass or other materials bonded with vinyl or urethane interlayers. In autoclaving, significant heat energy is lost heating the chamber, pressurized air, glass racks, and the glass. In RF lamination, the heat is generated directly in the vinyl interlayer, causing it to heat and melt quickly, in just 1 to 10 minutes, without significantly heating the glass or the equipment. The main purpose of this project was to provide evidence that low energy, rapid RF lamination quality met the same standards as conventionally autoclaved windows. The development of concepts for laminating curved glass with RF lamination was a major goal. Other primary goals included developing a stronger understanding of the lamination product markets described above, and to refine the potential benefits of commercial implementation. The scope of the project was to complete implementation concept studies in preparation for continuation into advanced development, pilot studies, and commercial implementation. The project consisted of 6 main tasks. The first dealt with lamination with poly-vinyl butyral (PVB) interlayers, which prior work had shown difficulties in achieving good quality laminates, working with Pilkington North America. The second task dealt with a study of current lamination processes in the various laminate industries, and development of concepts for integrating RF lamination into new or existing processes. The third task explored the use of a non-destructive technique for analyzing laminate adhesion with the University of Illinois at Urbana-Champaign. The fourth task focused on developing concepts for curved glass lamination using RF lamination. The fifth and sixth tasks together comprised an analysis of laminate product markets, ranking for applicability and commercialization potential, and the development of commercialization strategies for those products. In addition, throughout the project as new experimental data and conventional process data were obtained, the benefits analysis of RF lamination was refined. The goals of the project described above were achieved, positioning RF lamination for the next stage growth envisioned in the original Industrial Grand Challenge proposal. Working with Pilkington North America, lamination of flat autoglass with PVB was achieved, meeting all 16 stringent industry tests. In particular, PVB laminates made with RF lamination passed environmental tests including the high temperature, 120 C bake test, without significant formation of bubbles (defects). The adhesion of PVB to glass was measured using the pummel method. Adhesion values ranging from 1 to 7 out of 10 were obtained. The significant process parameters affecting the environmental and adhesion performance were identified through a designed experiment. Pre-lamination process variables including PVB storage humidity and the de-airing process (vacuum or nip rolling) were significant, as well as the level of pressure applied to the laminate during the RF process. Analysis of manufacturing with RF lamination equipment, based on the processes developed indicated that 3 RF presses could replace a typical auto-industry autoclave to achieve equal or greater throughput with possibly less capital cost and smaller footprint. Concepts for curved lamination identifying castable molds for prototyping were developed, which allowed Ceralink to obtain commitment to begin curved tooling development. The project significantly helped to advance RF lamination past the feasibility and novelty stage and into the realm of commercial acceptance as a viable alternative to autoclaves. The demonstration of autoclave-quality autoglass produced in just 1 minute with RF lamination, with validation by Pilkington, has fueled industry motivation to seriously consider RF lamination. The industry and other contacts and outreach made in the study of laminate markets (including 3 technical publications and 5 conference presentations), has resulted in a recent surge in RF lamination activity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Energy Saving Glass Lamination via Selective Radio Frequency Heating

This project focused on advancing radio-frequency (RF) lamination technology closer to commercial implementation, in order to reduce the energy intensity of glass lamination by up to 90%. Lamination comprises a wide range of products including autoglass, architectural safety and innovative design glass, transparent armor (e.g. bullet proof glass), smart glass, mirrors, and encapsulation of photovoltaics. Lamination is also the fastest growing segment of glass manufacturing, with photovoltaics, architectural needs, and an anticipated transition to laminated side windows in vehicles. The state-of-the-art for glass lamination is to use autoclaves, which apply heat and uniform gas pressure to bond the laminates over the course of 1 to 18 hours. Laminates consist of layers of glass or other materials bonded with vinyl or urethane interlayers. In autoclaving, significant heat energy is lost heating the chamber, pressurized air, glass racks, and the glass. In RF lamination, the heat is generated directly in the vinyl interlayer, causing it to heat and melt quickly, in just 1 to 10 minutes, without significantly heating the glass or the equipment. The main purpose of this project was to provide evidence that low energy, rapid RF lamination quality met the same standards as conventionally autoclaved windows. The development of concepts for laminating curved glass with RF lamination was a major goal. Other primary goals included developing a stronger understanding of the lamination product markets described above, and to refine the potential benefits of commercial implementation. The scope of the project was to complete implementation concept studies in preparation for continuation into advanced development, pilot studies, and commercial implementation. The project consisted of 6 main tasks. The first dealt with lamination with poly-vinyl butyral (PVB) interlayers, which prior work had shown difficulties in achieving good quality laminates, working with Pilkington North America. The second task dealt with a study of current lamination processes in the various laminate industries, and development of concepts for integrating RF lamination into new or existing processes. The third task explored the use of a non-destructive technique for analyzing laminate adhesion with the University of Illinois at Urbana-Champaign. The fourth task focused on developing concepts for curved glass lamination using RF lamination. The fifth and sixth tasks together comprised an analysis of laminate product markets, ranking for applicability and commercialization potential, and the development of commercialization strategies for those products. In addition, throughout the project as new experimental data and conventional process data were obtained, the benefits analysis of RF lamination was refined. The goals of the project described above were achieved, positioning RF lamination for the next stage growth envisioned in the original Industrial Grand Challenge proposal. Working with Pilkington North America, lamination of flat autoglass with PVB was achieved, meeting all 16 stringent industry tests. In particular, PVB laminates made with RF lamination passed environmental tests including the high temperature, 120°C bake test, without significant formation of bubbles (defects). The adhesion of PVB to glass was measured using the pummel method. Adhesion values ranging from 1 to 7 out of 10 were obtained. The significant process parameters affecting the environmental and adhesion performance were identified through a designed experiment. Pre-lamination process variables including PVB storage humidity and the de-airing process (vacuum or nip rolling) were significant, as well as the level of pressure applied to the laminate during the RF process. Analysis of manufacturing with RF lamination equipment, based on the processes developed indicated that 3 RF presses could replace a typical auto-industry autoclave to achieve equal or greater throughput with possibly less capital cost and smaller footprint. Concepts for curved lamination identifying castable molds for prototyping were developed, which allowed Ceralink to obtain commitment to begin curved tooling development. The project significantly helped to advance RF lamination past the feasibility and novelty stage and into the realm of commercial acceptance as a viable alternative to autoclaves. The demonstration of autoclave-quality autoglass produced in just 1 minute with RF lamination, with validation by Pilkington, has fueled industry motivation to seriously consider RF lamination. The industry and other contacts and outreach made in the study of laminate markets (including 3 technical publications and 5 conference presentations), has resulted in a recent surge in RF lamination activity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

RolX: Role Extraction and Mining in Large Networks

Given a graph, how can we automatically discover roles for nodes? Roles could be, eg., ’bridges’, or ’periphery-nodes’, etc. Roles are compact summaries of a node’s behavior that generalize across networks. They enable numerous novel and useful network mining tasks, such as sense-making, searching for similar nodes, and node classification. We propose RolX (Role eXtraction), a scalable (linear in the number of edges), unsupervised learning approach for automatically extracting roles from general network data. We demonstrate the effectiveness of RolX on several network mining tasks, from exploratory data analysis to network transfer learning. Moreover, we compare network role discovery with network community discovery. We highlight fundamental differences between the two (e.g., roles generalize across disconnected networks, communities do not).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Clustering Graph Generator

In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps to understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

DOE/DOT Crude Oil Characterization Research Study, Task 2 Test Report on Evaluating Crude Oil Sampling and Analysis Methods

The Crude Oil Characterization Research Study is designed to evaluate whether crude oils currently transported in North America, including those produced from "tight" formations, exhibit physical or chemical properties that are distinct from conventional crudes, and how these properties associate with combustion hazards with may be realized during transportation and handling.

02 PETROLEUM↗
DOE OSTI2020

Evaluation of Steam Cycle Upgrades to Improve the Competitiveness of U.S. Coal Power Plants (Final Scientific / Technical Report)

Increasing the competitiveness of the existing pulverized-coal utility fleet in the United States may be achieved by decreasing heat rate, via increases in steam cycle efficiency through upgraded steam temperatures and use of latest technology available in steam turbine and blading design. The average net plant efficiency of the US coal-fired fleet is approximately 33% (HHV). Plant efficiency increases to approximately 41.4% (HHV) at 1,350°F (732°C) steam temperature. However, achieving these Advanced Ultra-Super Critical (AUSC) steam conditions requires the use of advanced high-temperature materials. While there has been a significant amount of DOE-funded materials R&D, most of the related design work has focused on new (greenfield) units, rather than on opportunities to retrofit this advanced technology to the existing utility fleet. If technology, based upon the advanced materials required for AUSC steam conditions, may be applied to the existing fleet, using an economically viable retrofit, a higher capacity factor can be expected as a result of the increased plant competitiveness. The Electric Power Research Institute (EPRI) was awarded a project by the US Department of Energy to examine the technical and economic feasibility of a series of steam cycle upgrades to the two most prevalent types of U.S. coal power plants: 2,400 psig (16.6 MPa) subcritical and 3,500 psig (24.1 MPa) supercritical pulverized coal units. The nine upgrade options that were originally being considered included increasing the main and reheat steam temperatures from 1,000°F (538°C) to 1,100°, 1,200°, and 1,350°F (593°C, 649°C, and 732°C) while holding the steam pressures constant at their original design values, and cases where just the main steam or reheat steam temperatures were increased. The objective was to minimize the modifications required to the existing power plant while still providing a significant improvement in heat rate. The upgrade options assumed that the boiler enclosure envelope remained unchanged from each base case, and that all applicable OEM design guidelines for normal commercial units were imposed. For the highest temperature supercritical case, an option of using a low-pressure molten salt loop to transfer heat from the furnace to the steam was examined. The first major task of the work scope was designed to examine the technical feasibility of various upgrade options, while the subsequent work determined economic viability of the technically feasible upgrade options. Prior to evaluating the effect of these increased temperatures, a “base case” model of a subcritical and supercritical PC boiler was created, which was used for comparative purposes. Upgrade options were evaluated at full-load, part-load and dynamic transient conditions. Once the technical feasibility of each upgrade option was evaluated, the economic value of the heat rate improvement of each feasible option was determined by detailed modeling of unit dispatch in several regional power markets. The dispatch model was used to estimate the amount of revenue from power sales the upgraded unit would receive in comparison to a non-upgraded version of the same power plant. As a parallel task to the dispatch analysis, the capital cost of implementing the upgrades was estimated. The capital cost estimates were then compared to the increased revenue estimated by the dispatch modeling to determine the economic attractiveness of each upgrade option. Several upgrade options were determined to be technically feasible. The net present value (NPV) of the costs for steam cycle upgrades considered in this study ranged from approximately $\$$111 to $\$$130 million. The economic modeling results show that the unit dispatch changes resulting from steam cycle upgrades are relatively small, due largely to heat rate (and operating cost) changes being relatively small. Additionally, the cost of each upgrade exceeds the net revenue increases associated with the upgrade case. Note that the breakeven values are higher for subcritical retrofits, but the capital costs for the subcritical upgrades are also slightly higher. In typical new pulverized coal plants, fuel accounts for approximately 25% of the cost of electricity (COE), while capital costs represent around 50% of the COE. Therefore, in order to improve the heat rate by 4% one can only afford to increase the capital cost by 2%, at the same cost of electricity. The conclusion of this study is that without a cost for emitting CO 2 , it will be difficult to pay for significant efficiency improvements on plants firing low cost coals.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

The Argon Power Cycle, a zero emission, flexible and reliable power generation technology

The proposed project was aimed at assisting in the development of the Argon Power Cycle (APC), a zero emission, flexible, and efficient power generation technology. In the effort of bringing the APC to the market, NTS proposed in the scope of this project several partial but key activities to advance the development of the APC. These tasks encompassed 1) the analysis of the market, developing a techno-economic model with integrated market inputs, 2) further optimization of our chemical kinetics models for more accurate simulation of the process and 3) scale up of our original prototype to a larger, commercially viable platform. NTS expanded on this scope of work to secure additional R&D funding in amounts north of $7M, carry out customer discovery with the sponsorship of the NSF and grow the team’s engineering talent.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Similar Exposure Group [Stick Welding without LEV - Manganese - RiskLevel 4]

This memorandum is presented to summarize quantitative worker exposure data collected while performing shielded metal arc welding (“SMAW” or “stick”) without local exhaust ventilation (LEV) and to validate if the appropriate risk level has been assigned to this task using Bayesian Decision Analysis (BDA). The purpose of this exposure assessment is to determine if it can be stated with at least 75% certainty that the true exposure profile (i.e., 95 th percentile) of the SEG is at or below the risk level currently assigned to this SEG.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

SECARB-USA: Preliminary Regional Commercialization Plan (Task 4.4.a)

The SECARB-USA region refers to the states in the southeastern portion of the United States including Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, eastern Oklahoma, South Carolina, Tennessee, eastern Texas, Virginia, and West Virginia. This region is a hub for economic activity and accordingly emits almost 1 Gtpa of CO 2 which is the motivation for the development of Carbon Capture and Storage (CCS). This preliminary commercialization plan reviews the status of commercialization of CCS within the region, outlines the infrastructure buildout to achieve CO 2 reduction goals by 2050 described by the Los Alamos National Lab in Task 4.1.b (see the Task 4.1.b “Technoeconomic Analysis of Infrastructure Buildout Scenarios” report), and provides an early estimate of the economic activity and employment creation resulting from the increase in CCS activity. Additionally, this report addresses the challenges associated with commercializing CCS within the region at a large scale.

54 ENVIRONMENTAL SCIENCES↗