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Flynn, Thomas

Publications and source records attributed to Flynn, Thomas.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning

Abstract Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNi x Mn y Co 1-x-y O 2 systems with high confidence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stochastic projective splitting

Here, we present a new, stochastic variant of the projective splitting (PS) family of algorithms for inclusion problems involving the sum of any finite number of maximal monotone operators. This new variant uses a stochastic oracle to evaluate one of the operators, which is assumed to be Lipschitz continuous, and (deterministic) resolvents to process the remaining operators. Our proposal is the first version of PS with such stochastic capabilities. We envision the primary application being machine learning (ML) problems, with the method’s stochastic features facilitating “mini-batch” sampling of datasets. Since it uses a monotone operator formulation, the method can handle not only Lipschitz-smooth loss minimization, but also min–max and noncooperative game formulations, with better convergence properties than the gradient descent-ascent methods commonly applied in such settings. The proposed method can handle any number of constraints and nonsmooth regularizers via projection and proximal operators. We prove almost-sure convergence of the iterates to a solution and a convergence rate result for the expected residual, and close with numerical experiments on a distributionally robust sparse logistic regression problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning

While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate microarchitecture simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional CPU-based simulators significantly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics

In this article, we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and optimization takes place over this control variable. Since the derivative of the fixed-point with respect to the parameter can usually not be evaluated exactly, an adjoint dynamical system can be used to estimate gradients. Using this estimation procedure, the optimization algorithm alternates between derivative estimation and an approximate gradient descent step. We analyze a variant of this approach involving dynamic time-scaling, where after each parameter update the adjoint system is iterated until a convergence threshold is passed. Here, we prove that, under certain conditions, the algorithm can find approximate stationary points of the objective function. We demonstrate the approach in the settings of an inverse problem in chemical kinetics, and learning in attractor networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

10 Megawatts Electric Coal Direct Chemical Looping Large Pilot Plant - Pre-Front End Engineering and Design Study

The Peer Review presentation covered the following items: Perform pilot studies to obtain design information. Perform a pre-FEED study of a modular 10 MWeCDCL pilot plant. Provide Functional specifications for integration with host site. Provide risk assessment, schedule and cost estimate for fabrication, construction and testing. Update commercial 550 MWeCDCL plant design and economic analysis

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2018

Update on Design of 10 MWe Iron-Based Coal Direct Chemical Looping Demonstration Plant

The Coal-Direct Chemical Looping (CDCL) process is emerging as a promising technology capable of high efficiency for producing electricity with low cost for carbon capture. In the CDCL process, coal is fully combusted to form carbon dioxide (CO2) and water via reaction with Fe2O3-based oxygen-carrier particles in a moving-bed reducer reactor. During reaction with coal, the Fe2O3 is reduced to form a mixture of Fe and FeO. The reduced particles are then transported to the combustor reactor where they are re-oxidized with air. The oxidation of the Fe and FeO mixture generates heat that can be used to produce steam for power generation. Once the particles are fully regenerated, they are pneumatically recycled to the reducer reactor where another reduction-oxidation (Redox) cycle begins. The Babcock & Wilcox Company (B&W) and The Ohio State University (OSU) have been developing the CDCL technology based on iron-based metal oxide particles circulating in a moving-bed reducer coupled with an entrained-flow combustor. As part of a Phase I effort of a DOE-sponsored project, B&W identified scale-up technology gaps for the coal-direct chemical looping reactor process, most of which fall under the moving-bed reactor design and operation. However, after the Phase I research, B&W concluded that the CDCL technology has the potential of generating electricity while meeting the DOE goals of greater than 90% CO2 capture and less than 35% increase in levelized cost of electricity (LCOE). B&W then moved to a Phase II project to design, build and demonstrate the CDCL technology at a 250 kWth capacity. B&W finished the construction and commissioning of the pilot test facility in 2017. The approach used to design a 250 kWth pilot-scale CDCL facility to investigate the technology gaps is presented. Important lessons from the commissioning activities that would transfer to commercial plant start-up are also shared. The findings from the test program are being incorporated into a Pre-FEED study for a 10 MWe large pilot demonstration. Under a separately-funded heat integration project, B&W and OSU are developing a multiphase computational fluid dynamic model (MFIX) of the combustor to quantify heat transfer to embedded steam generation surface, utilizing pinch analysis (ASPEN Energy Analyzer) to identify potential improvements of the heat integration scheme, and developing an integrated dynamic model (ProTRAX) of the steam turbine coupled with the chemical looping process to identify potential start-up and transient operation issues. Under an advanced control system for optimizing operation, B&W is supporting OSU to install a rules-based optimizer (B&W’s FocalPoint) on their 25 kWt bench-scale facility on the OSU West Campus. Finally, B&W and OSU were recently selected to perform a Phase I Feasibility study to evaluate installation of the 10 MWe pilot at the Dover Power & Light Municipal Power Plant in Dover, Ohio. The feasibility study includes compiling an Environmental Information Volume and generating a cost estimate for the design, construction and commissioning of the plant as well as lining up commitments from potential project participants and funding contributors. Updates on each project will be presented. The status of the technology development will be discussed. We focus on the progress made to date towards closing the technology gaps under each project.

20 FOSSIL-FUELED POWER PLANTS↗