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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High Performance Computing to Quantify the Evolution of Microscopic Concentration Gradients During Flash Processing

During the Flash process, the cross section of a plain-carbon or a low-alloy steel is austenitized through rapid heating and transformed on rapid cooling to a predominantly martensite + bainite structure with small amounts of retained austenite. Unlike conventional heat treating, homogeneity is intentionally avoided during Flash processing of steels. The Flash process assembly consists of a pair of rolls that transfer the steel sheets through the heating and cooling stage of the thermal cycle. The initial microstructure of the steel consists of ferrite (body-centered cubic iron) + carbide ((Fe,X)mCn) mixture. The heating rate through the peak temperature is a function of temperature and reaches a peak of about 300-400°C/s and the cooling rate has a maximum value of 3,000-4,000°C/s. The on-heating phase transformations include carbide dissolution, austenite (face-centered cubic iron) nucleation and growth, and diffusion of carbon and other substitutional elements in the steel. The on-cooling phase transformations include formation of martensite (body-centered tetragonal phase containing supersaturated solute) and bainite (ferrite plates with or without fine carbides). In this project, the focus is on Fe-C-Cr steels that are currently Flash processed for armor applications. The modeling effort proposed here will help optimize the Flash thermal cycle for these low alloy steels to achieve the target performance, which is an ongoing effort at SFP Works. A significant feature of Flash processed Fe-C-Cr steels is the presence of scatter in the through-thickness in the sheet. The variability in hardness results from a variability in the bainite + martensite microstructure that is sensitive to the local chemical concentration of C and Cr. Such a chemical inhomogeneity is intentionally obtained in the Flash process. Although such a microstructural gradient is presumably responsible for the exceptional properties of the Flash processed steel, it is very important to quantify the gradients as a function of Flash variabilities in processing parameters and the input microstructure. Understanding the mechanistic pathway that leads to microstructural gradients could be ground-breaking and instrumental for achieving better process control and optimized microstructural state to meet application-specific strength-ductility requirements. Since the final microstructure depends on setting up precise solute concentration gradients through a rapid heating process, and transforming these regions into various phases, it is important to understand how small changes in steel chemistry, input microstructure (carbide size and distribution), and process variables (Flash thermal cycle) will impact the solute concentration gradients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2021

The fluorescence quantum yield parameter in Förster resonance energy transfer (FRET)—Meaning, misperception, and molecular design

The fluorescence quantum yield parameter in Förster resonance energy transfer (FRET) processes underpins vital phenomena ranging from light harvesting in photosynthesis to design of sensors for monitoring physiological processes. The criteria for choosing a donor for use in FRET processes include chemical features (solubility, bioconjugatability, synthetic accessibility, and stability) as well as photophysical properties pertaining to absorption (wavelength and molar absorption coefficient) and fluorescence (wavelength and fluorescence quantum yield). The value of the donor fluorescence quantum yield (Φf, or emphatically, Φf(D)) alone has sometimes been thought (erroneously) to place a ceiling on the possible quantum yield of energy transfer (Φtrans). A high value of the donor Φf, while attractive, is not at all essential; indeed, many valuable candidates for use as FRET donors have likely been excluded on the basis of this injudiciously applied filter. Such disregard is unwarranted. In this tutorial overview, the equations for FRET are reviewed along with pertinent core concepts in photophysics. An analogy using simple hydraulics provides a pedagogical tool for the non-aficionado to better understand photochemical kinetics. Ten examples are presented of donor–acceptor systems with donors that exhibit a range of Φf values (0.60, 0.59. 0.21, 0.17, 0.12, 0.118, 0.04, 0.018, 0.007, and 0.003; i.e., 60%–0.3%), yet for each corresponding donor–acceptor pair, the value of Φtrans is at least 0.70 and in some cases nearly 1.00 (i.e., 70%–100%). The systems encompass protein, synthetic inorganic, and synthetic organic architectures. The objectives of this illustrative review are to deepen understanding of FRET and to broaden molecular design considerations by enabling selection from among a far richer set of donors for use in FRET processes.

Lindsey, Jonathan S. (ORCID:0000000248722040)↗
DOE OSTI2016

SU-F-T-683: Cancer Stem Cell Hypothesis and Radiation Treatments

Purpose: The tumor control probability in radiation therapy allows comparing different radiation treatments to each other by means of calculating the probability that a prescribed dose of radiation eradicates or controls the tumor. In the conventional approach, all cancer cells can divide unlimited number of times and the tumor control often means eradicating every malignant cell by the radiation. In recent years however, there is a mounting consensus that in a given tumor volume there is a sub-population of cells, known as cancer stem cells (CSCs) that are responsible for tumor initiation and growth. Other or progenitor cancer cells can only divide limited number of times. This entails that only cancer stem cells may nned to be eliminated in order to control the tumor. Thus one may define TCP as the probability of eliminating CSCs for the given dose of radiation. Methods: Using stochastic methods, specifically the birth-and-death Markov processes, an infinite system of equations is set for probabilities of having m cancer stem cells at time t after the start of radiation. The TCP is calculated as the probability of no cancer stem cells surviving the radiation. Two scenarios are studied. In the first situation, the TCP is calculated for a unidirectional case when CSC gives birth to another CSC or a progenitor cell. In the second scenario, a bidirectional model is studied where the progenitor cell gives rise to CSC. Results: The proposed calculations show that the calculated TCP for CSC depends on whether one adopts unidirectional or bidirectional conversion models. The bidirectional model shows significantly lower TCP values for the given dose delivered to the tumor. Conclusion: Incorporating CSC hypothesis into the TCP modeling may notably influence the dose prescription as well as the concept of the expected TCP after the radiation treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Fault tolerant filtering and fault detection for quantum systems driven by fields in single photon states

The purpose of this paper is to solve the fault tolerant filtering and fault detection problem for a class of open quantum systems driven by a continuous-mode bosonic input field in single photon states when the systems are subject to stochastic faults. Optimal estimates of both the system observables and the fault process are simultaneously calculated and characterized by a set of coupled recursive quantum stochastic differential equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Auto Procedure Parsing: A Natural Language Processing Approach

Nuclear Power Plant (NPP) operating procedure is “a set of rules that describes how actions on the plant should be made if a certain system goal should be accomplished. U.S. NPPs use paper-based procedures (PBPs). PBPs are difficult to use. Common errors with PBPs are: following the wrong procedure, omit a step etc. Computer based procedures (CPBs) offer great improvement in ensuring plant safety. Some studies rely on experienced operators to understand the procedure content and then reorganize the procedure with digitally executable capabilities. Other studies utilize the procedure format to design rules to extract information from the operating procedures. Existing studies in procedure parsing are manual, laborious and extract limited information. This study aims to automatically extract critical information from operating procedures for generating computer interpretable representation of procedures. Such representation can further be used for automatic dynamic human reliability analysis and CPB design etc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Use of Grid-Forming Medium-Voltage Power Electronics Hub in a Microgrid Setting: Preprint

This paper presents the application of a new design of a multiport, modular, medium-voltage power electronics hub (M3PE-HUB) in a microgrid setting. The M3PE-HUB system was modeled in a digital real-time simulator (DRTS) and integrated into the Banshee microgrid test system. This paper presents the preliminary DRTS simulation-based results of the M3PE- HUB system connected in a test microgrid system. Verification and validation of the M3PE-HUB architecture and controls in the test microgrid setting are the primary contributions of this work. The results of the operation during the islanding and resynchronization process indicate the feasibility of the proposed architecture in a microgrid setting. This paper also presents results for a system reconfiguration use case where the M3PE- HUB was used to reconfigure the system under a fault condition.

grid forming↗
DOE OSTI2016

Non-commutative Nash inequalities

A set of functional inequalities—called Nash inequalities—are introduced and analyzed in the context of quantum Markov process mixing. The basic theory of Nash inequalities is extended to the setting of non-commutative L{sub p} spaces, where their relationship to Poincaré and log-Sobolev inequalities is fleshed out. We prove Nash inequalities for a number of unital reversible semigroups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Fast Active-Set Thresholding Method for Nonnegative Least Squares

Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.

Cobb, Benjamin [Georgia Institute of Technology]↗
DOE OSTI2021

PRECISE™ PREconfiguring and Controlling Inverter SEt-points: A Solution for Easy Solar Integration

PRECISE™ is a software solution for preconfiguring solar inverter settings. It can be used by utilities and system operators to efficiently process new inverter applications at much less the time and effort, and with greater precision for settings that optimize stability and energy savings. PRECISE has received an R&D 100 award, and has been used to dramatically cut wait times and labor costs for new residential solar installations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Adaptive time scaling for accelerating explicit finite element analysis

A method for accelerating an explicit finite element analysis (FEA) simulation of a modeled system or process includes performing an initial iteration of the FEA simulation according to a baseline time interval via an FEA computing network, and calculating a criteria ratio of a predetermined set of scaling criteria for the modeled system or process. The method includes determining a time-scaling factor using the criteria ratio via the FEA computing network as a function of the criteria ratio, and then applying the time-scaling factor to the baseline time interval to generate a scaled time interval. The scaled time interval accelerates simulation time of the FEA simulation. The method includes performing a subsequent iteration of the explicit FEA simulation at the scaled time interval using the FEA computing network. The process continues for subsequent iterations, with the time-scaling factor adapting with each iteration.

Chen, Jian↗
DOE OSTI2015

Building a sustained climate assessment process

The leaders and authors of the Third US National Climate Assessment (NCA3) developed new modes of engaging academia, the private sector, government agencies and civil society to support their needs for usable, rigorous, and timely information and better connect science and decision-making. A strategic vision for assessment activities into the future was built during the NCA3 process, including recommendations on how to establish a sustained assessment process that would integrate evolving scientific understanding into decision making to manage the risks of climate change over time. This vision includes a collaborative assessment process that involves partnerships across a diverse and widely distributed set of non-governmental and governmental entities. The new approach to assessments would produce timely, scientifically sound climate information products and processes, rather than focusing on the production of single quadrennial synthesis reports. If properly implemented, a sustained assessment would be more efficient and cost-effective, avoiding the painful and time-consuming process of beginning the assessment process anew every 4 years. This ongoing assessment would also encourage scientific and social innovations and explore new insights and opportunities, building the capacity to advance the development and delivery of climate information to meet societal requirements and benefit from scientific opportunities.

Buizer, James L.↗
DOE OSTI2016

Four-Dimensional Dose Reconstruction for Scanned Proton Therapy Using Liver 4DCT-MRI

Purpose: Four-dimensional computed tomography-magnetic resonance imaging (4DCT-MRI) is an image-processing technique for simulating many 4DCT data sets from a static reference CT and motions extracted from 4DMRI studies performed using either volunteers or patients. In this work, different motion extraction approaches were tested using 6 liver cases, and a detailed comparison between 4DCT-MRI and 4DCT was performed. Methods and Materials: 4DCT-MRI has been generated using 2 approaches. The first approach used motion extracted from 4DMRI as being “most similar” to that of 4DCT from the same patient (subject-specific), and the second approach used the most similar motion obtained from a motion library derived from 4DMRI liver studies of 13 healthy volunteers (population-based). The resulting 4DCT-MRI and 4DCTs were compared using scanned proton 4D dose calculations (4DDC). Results: Dosimetric analysis showed that 93% ± 8% of points inside the clinical target volume (CTV) agreed between 4DCT and subject-specific 4DCT-MRI (gamma analysis: 3%/3 mm). The population-based approach however showed lower dosimetric agreement with only 79% ± 14% points in the CTV reaching the 3%/3 mm criteria. Conclusions: 4D CT-MRI extends the capabilities of motion modeling for dose calculations by accounting for realistic and variable motion patterns, which can be directly employed in clinical research studies. We have found that the subject-specific liver modeling appears more accurate than the population-based approach. The former is particularly interesting for clinical applications, such as improved target delineation and 4D dose reconstruction for patient-specific QA to allow for inter- and/or intra-fractional plan corrections.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Characterizing source fingerprints and ageing processes in laboratory-generated secondary organic aerosols using proton-nuclear magnetic resonance ( 1 H-NMR) analysis and HPLC HULIS determination

The study of secondary organic aerosol (SOA) in laboratory settings has greatly increased our knowledge of the diverse chemical processes and environmental conditions responsible for the formation of particulate matter starting from biogenic and anthropogenic volatile compounds. However, characteristics of the different experimental setups and the way they impact the composition and the timescale of formation of SOA are still subject to debate. In this study, SOA samples were generated using a potential aerosol mass (PAM) oxidation flow reactor using α -pinene, naphthalene and isoprene as precursors. The PAM reactor facilitated exploration of SOA composition over atmospherically relevant photochemical ageing timescales that are unattainable in environmental chambers. The SOA samples were analyzed using two state-of-the-art analytical techniques for SOA characterization – proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy and HPLC determination of humic-like substances (HULIS). Results were compared with previous Aerodyne aerosol mass spectrometer (AMS) measurements. The combined 1 H-NMR, HPLC, and AMS datasets show that the composition of the studied SOA systems tend to converge to highly oxidized organic compounds upon prolonged OH exposures. Further, our 1 H-NMR findings show that only α -pinene SOA acquires spectroscopic features comparable to those of ambient OA when exposed to at least 1×10 12 molec OH cm -3 × s OH exposure, or multiple days of equivalent atmospheric OH oxidation. Over multiple days of equivalent OH exposure, the formation of HULIS is observed in both α -pinene SOA and in naphthalene SOA (maximum yields: 16 and 30 %, respectively, of total analyzed water-soluble organic carbon, WSOC), providing evidence of the formation of humic-like polycarboxylic acids in unseeded SOA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Chung, Seung Whan (ORCID:0000000302501549)↗
DOE OSTI2022

Minimum feature size control in level set topology optimization via density fields

A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optimized designs is explored. The level set field determines the solid-void interface and the density field describes the distribution of a fictitious porous material using the solid isotropic material with penalization. These fields are governed by two sets of independent optimization variables which are initially coupled using a penalty for hole nucleation. The strength of the density field penalization and projection is gradually increased during the optimization process to promote a 0-1 density distribution. In addition, a second penalty regulates the evolution of the density field in the void phase. The treatment of the density field combined with the second penalty mitigate the appearance of small design features. The minimum feature size of optimized designs is controlled by the radius of the linear filter applied to the density optimization variables. The structural response is predicted by the extended finite element method, the sensitivities by the adjoint method, and the optimization variables are updated by a gradient-based optimization algorithm. Numerical examples investigate the robustness of this approach with respect to algorithmic parameters and mesh refinement. The results show the applicability of the combined density level set topology optimization approach for both optimal hole nucleation and for minimum feature size control in 2D and 3D. This comes, however, at the cost of a more complex problem formulation and additional computational cost due to an increased number of optimization variables.

42 ENGINEERING↗
DOE OSTI2024

Additive Manufacturing for Large Displacement Unmanned Underwater Vehicles (LDUUVs)

Oak Ridge National Laboratory partnered with Anduril Industries to develop the process for adapting large format additive manufacturing for the production of underwater vehicles. This research started by defining and selecting a polymer for the vehicle panels. Coatings were tested at 9000PSI to ensure proper bonding to the 3D printed panels, then tensile tested to understand the mechanical properties. A vehicle panel fairing set was design optimized for the 3D printing process and then 3D printed. Additional work optimized coatings and tested additional reinforcements methods such as fiber weave and z-casting with fiber rods and resin. Finally, a full vehicle was constructed and successfully tested in open water by Anduril.

36 MATERIALS SCIENCE↗