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ProfileSR-GAN: A GAN Based Super-Resolution Method for Generating High-Resolution Load Profiles
Not provided.
Significance of the Direct Excitation Mechanism for High-Frequency Response of Premixed Flames to Flow Oscillations
Premixed flames are sensitive to flow disturbances, which can arise from acoustic or vortical fluctuations. For transverse instabilities, it is known that a dominant mechanism for flame response is “injector coupling,” whereby pressure oscillations associated with transverse waves excite axial flow disturbances. These axial flow disturbances then excite heat release oscillations. The objective of this paper is to consider another mechanism—the direct sensitivity of the unsteady heat release to transverse acoustic waves—and to compare its significance relative to the induced axial disturbances, in a linear framework. The rate at which the flame adds energy to the disturbance field is quantified using the Rayleigh criterion and evaluated over a range of control parameters, such as flame length and swirl number. The results show that radial modes induce heat release fluctuations that always add energy to the acoustic field, whereas heat release fluctuations induced by mixed radial-azimuthal modes can add or remove energy. These amplification rates are then compared to the flame response from induced axial fluctuations. For combustor-centered flames, these results show that the direct excitation mechanism has negligible amplification rates relative to the induced axial mechanism for radial modes. For transverse modes, the fact that the nozzle is located at a pressure node indicates that negligible induced axial velocity disturbances are excited; as such, the direct mechanism dominates. As a result, for flames that are not centered on pressure nodes, the direct mechanism for mixed modes dominates for certain nozzle locations and flame angles.
Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence
Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.
Design study of a kinematic Stirling engine for dispered solar electric power systems
The concept evaluation shows that the four cylinder double acting U type Stirling engine with annular regenerators is the most suitable engine type for the 15 kW solar application with respect to design, performance and cost. Results show that near term performance for a metallic Stirling engine is 42% efficiency. Further improved components show an impact on efficiency of the future metallic engine to 45%. Increase of heater temperature, through the introduction of ceramic components, contribute the greatest amount to achieve high efficiency goals. Future ceramic Stirling engines for solar applications show an efficiency of around 50%.
Navigating a National Lab Career: Science, Technology, & Engineering Priorities at Los Alamos [Slides]
Abstract not provided.
2022 Presentation for UCSD Structural Engineering Department [Slides]
Abstract not provided.
Validation of (not-historical) large-event near-fault ground-motion simulations for use in civil engineering applications
Ground-motion simulations generated from physics-based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance-based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground-motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four-step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground-Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near-field ground motions (<10km) from large earthquakes (M w 7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near-field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse-like near-field real records from multiple events and, when applicable, with NGA-W2 GMPEs. Lastly, the proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground-motion simulations, and for building confidence in their use in engineering domains.
Multiple redox mechanisms for water-gas shift reaction on Fe 3 O 4 (1 1 1) surface: A density functional theory and mean-field microkinetic modeling study
In this work, we investigated redox mechanisms for the high temperature (HT) water–gas shift reaction (WGSR) on the Fe 3 O 4 (1 1 1) surface using density functional theory (DFT) calculations and mean-field microkinetic modeling (MKM). The redox pathways branched into three Langmuir-Hinshelwood processes (LH1, LH2 and LH3) and one Mars-van-Krevelen (MVK) process (in the presence of oxygen vacancy) from multidentate binding of CO and CO 2 over four available reactive sites (Fe oct2 , Fe tet1 , Fe bridge, and O1). We found that the LH1 and LH2 processes have CO adsorption at a single iron site (Fe oct2 or Fe tet1 ), while the LH3 and MVK processes have stronger chemisorption of CO or CO 2 by both the Fe oct2 site and the O 1 site. From the mechanistic study of these reaction path, we recognized that availability of O 1 sites was key to proceed to either CO oxidation by the single site (LH1, LH2) or by the dual site (LH3, MVK). We observed that the reaction energetics were significantly different in the CO oxidation steps, where the single or dual site results in markedly different apparent activation energy (73–281 kJ/mol) and reaction rates (10 -3 –10 -10 mol∙m -2 ∙s -1 ) among the four reaction mechanisms. The utilization of mean field MKM with DFT reaction energetics helps explain the experimental debates for the catalytic reaction details as well as providing a possible direction to engineer the catalyst with higher activity.
A Functional Reference Architecture and Assessment Thereof for the National Ignition Facility Industrial Controls Systems
The Industrial Control System (ICS) at the National Ignition Facility (NIF) has an effective, well-established architecture based off a conventional cyclical software paradigm focused on maintainability and the identification of issues. This architecture exhibits scalability in ensuring expansions of the system conform to the existing architecture, modularity enough to allow relatively easy integrations of such expansions and works as a successful tool to introduce control system engineers new to the NIF ICS to the structure of the system at each layer. This architecture, like most software architectures, is object-oriented, lending itself to ease of understanding by control systems engineers and software engineers familiar with an object-oriented perspective. There are occasions, however, where engineers of other disciplines require insight into the functionality and structure of the ICS for the purposes of understanding fundamentally how their own system is or will be governed by the ICS, without the need for the details of operation of the ICS or the object-oriented view. For this reason, a functional architecture of the ICS could be a potent tool for communicating this insight. Even more powerful, a generalization of this proposed functional ICS architecture in the form of a National Ignition Facility and Photon Science (NIF & PS) Industrial Controls Reference Architecture could communicate this insight not just to systems governed by the ICS in the NIF proper, but across entirety of the NIF & PS Principal Associate Directorate (PAD), anywhere an instance of the ICS architecture is present, such as the approximately 40 “small labs” distributed across the directorate. Such a tool will provide an alternative means of understanding the implementation of these control systems, conducive to a larger variety of engineering and scientific disciplines.
MIT Capstone Project Assignment 1 - Existing System Design
“FPU in 5”: The National Nuclear Security Administration (NNSA) and upper management at Lawrence Livermore National Lab (LLNL) have informally set a long-term goal of decreasing the development cycle of new systems to five years or less. The driver is to be able to respond to emerging situations which require new capabilities more quickly. There are numerous approaches to solve the problem, and any single improvement is likely insufficient. Our capstone project focuses on modernizing the design development processes, production development processes, and communication between design agencies (DAs) and production agencies (PAs) to decrease the timeline from conceptual study to first production unit. Our project’s system problem statement (SPS) is below: TO decrease the time from conceptual study to first production unit BY centralizing design development, production development, and information exchange USING digital engineering techniques.
Cyber-Informed Engineering Briefing for ABET
Cyber-Informed Engineering (CIE) is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system, energy or otherwise, to mitigate or even eliminate avenues for cyber-enabled attacks.?CIE concepts use design decisions and engineering controls to prioritize defense against the worst possible consequences of cyberattacks facing critical infrastructure systems and asset owners. These slides offer a deep dive into Cyber-Informed Engineering for engineering educators.
Restructuring and Optimizing Reactor Building Lifting Processes & Designing a Solution for an Overhead Door Handling Forklift Attachment
This poster presents two innovative projects aimed at enhancing operational efficiency and safety at the Advanced Test Reactor (ATR) Complex. The first project focuses on restructuring the existing lift book used for hoisting operations within the ATR Reactor Building. The current lift book, a cumbersome 150+ page document, is being transformed into a more efficient format using charts that allow for quick identification of maximum lift heights based on object footprint and weight. This new method also considers various parameters such as floor integrity and reactor status, dividing entries into four distinct charts to improve usability and safety during lifts. The second project involves designing a specialized forklift attachment for handling an overhead door at the ATR Reactor Building. The attachment is engineered to securely lift and lower a 1200lb, 14ft long door, ensuring safe replacement operations. The design process included research on forklift attachment codes, modeling in Autodesk Inventor, and testing through Finite Element Analysis (FEA) and hand calculations to confirm the attachment's structural integrity. Future work includes completing detailed drawings and an Engineering Calculations Analysis and Review (ECAR) document to proceed with manufacturing and assembly. Together, these projects demonstrate a commitment to optimizing reactor building operations through innovative engineering solutions and safety considerations.
Engineered Solutions Group's SMR Containment Cable and Electrical Penetration Assembly System
GAIN Project CRADA Number NFE-21-08839, entitled “SMR Containment Cable and EPA System” was initiated by the partnership of Oak Ridge National Laboratory (ORNL) and Engineered Solutions Group (ESG) to test an ESG-designed Electrical Penetration Assembly and Containment Cabling System Qualified for not only legacy LWR designs, but also Small Modular Reactors designs currently being designed by several different suppliers. This project was undertaken to fill the equipment gap of EPA and Cabling Systems that require much more severe environmental requirements than legacy plant applications present due to their smaller containment volumes that result in high energy densities compared to legacy designs. This high energy density results in severe accident environments and more severe normal operating conditions as well. We developed two approaches to qualify Electrical Penetration Assembly (EPA) and Containment Cabling Systems for SMRs and Advanced Reactors. We take into consideration the more severe environmental parameters found with SMR designs. The system will need to meet a qualification test program addressing wear/cyclic aging, potential radiation exposure, thermal aging, vibration aging, thermal cycling, seismic qualification, electrical fault testing (per IEEE 317) and accident simulation. The equipment must meet the requirements of 10CFR50.49, GDC 50 in 10CFR50 Appendix A, and 10CFR50 Appendix J. NRC Regulatory Guides (RGs) identify an acceptable way of meeting regulatory requirements. RGs frequently endorse a standard for meeting these requirements. Specific to this review, equipment would be qualified in accordance with the following IEEE Standards. • IEEE 317-2013 (Electrical Penetration Assemblies), which is endorsed by RG 1.63, Rev. 3, • IEEE 323-2003 and the more current IEC/IEEE 60780-323 (Environmental Qualification of 1E Equipment) which is endorsed by RG 1.89. IEEE 323-2003 is endorsed by RG 1.209, • IEEE 344-2020 (Seismic Qualification. The 2013 version endorsed by RG 1.100, Rev. 4, with exceptions), • IEEE 383-2015 (Electrical Cables) (which is endorsed by RG 1.189 Rev. 4 and the -2003 version endorsed by RG 1.211 rev. 0), • IEEE 572-2019 (Electrical Connectors and Assemblies), which is endorsed by 1.156 Rev 1, and • [IEEE 1202 (endorsed by RG 1.189) would normally be applicable but the advanced cable designs are impervious to this cable flame test.] The primary goal of such a program is to provide an EPA design that can meet the qualification requirements for all legacy light water reactor plants currently operating as well as new plant designs including light water Small Modular Reactors. Thus, these requirements are applicable to plants licensed under 10CFR50 and 10CFR52. Other reactor designs may be evaluated, and this test system and qualification method applied to those applications if the requirements would satisfy the requirements of the intended plant. A secondary benefit of this work is to document some of the history and background in these requirements as there have been recent delays in an SMR licensing process due to NRC Requests for Additional Information in this subject matter area.
Modeling of the effects of non-equilibrium excitation and electrode geometry on H 2 /air ignition in a nanosecond plasma discharge
In this work, we present the results of two-dimensional modeling of the effects of non-equilibrium excitation and electrode geometry on H 2 /air ignition in a nanosecond plasma discharge. A multiscale adaptive reduced chemistry solver for plasma assisted combustion (MARCS-PAC) based on PASSKEy discharge modeling package and compressible multi-component reactive flow solver ASURF+ is developed and validated. This model is applied to simulate the impact of non-equilibrium plasma excitation and electrode geometry and heat loss on the dynamics of the discharge from streamer to spark and ignition kernel development in a H 2 /air mixture with a pair of cylindrical electrodes. The results show that the plasmagenerated species (N 2 (A), N 2 (B), N 2 (a'), N 2 (C), O( 1 D), O and H) in the spark and afterglow significantly accelerate the ignition kernel development. The increase of discharge voltage at the same total discharge energy promotes the non-equilibrium active species production. It is found that the production of electronically excited species at higher reduced electric field strength is more efficient in enhancing ignition in comparison to the vibrational excitation and heating. Moreover, the 2D simulation clearly reveals that the electric field and active species distribution are highly non-uniform. The streamers are initiated at the sharp outer edges of the negative and positive electrodes by a strong electric field while the electric field is much weaker at the centerline of the electrodes. Furthermore, the simulations reveal that the ignition enhancement is sensitive to the variation of electrode shape, diameter, and gap size due to the changes of electric field distribution and location of streamer formation. A cylindrical electrode produces a larger discharge volume and ignition kernel than the parabolic and spherical electrodes, when the discharge is localized near the axis of the gap. It is found that there is a non-monotonic dependence of ignition kernel size on the electrode diameter and inter-electrode distance. The increase of electrode diameter and gap size above the optimal conditions leads to the reduction of ignition kernel volume, due to the decrease of active species concentration and gas temperature. At a larger electrode surface area and electrode diameter as well as smaller electrode gap size, the heat loss to electrode plays a greater role in reducing the ignition kernel size and slowing ignition kernel development. This work provides insights and guidance to understand the kinetic enhancement of non-equilibrium plasma and the effects of electrode geometries on ignition for the optimization ignitors in advanced engines.
Phase diagram to demarcate supercritical, transcritical, and continuous phase regimes for binary fluid equilibrium mixing relevant to combustion applications
Here, a robust methodology to develop phase diagrams of binary fluid mixtures at fixed thermo dynamic conditions (pressure, temperature, and mole fraction) as well as of two initially separated fluids undergoing mixing near critical conditions are presented for fluids and con ditions relevant to rockets, gas turbines, and diesel engine applications. Phase equilibria of mixtures is first examined to provide insight into the continuous-phase mixing behavior (including but not limited to supercritical behavior), and to develop a broadly applicable phase-diagram for binary fluid mixtures at fixed conditions. Next, adiabatic mixing theory and reduced Helmholtz equations of state are used to predict the thermodynamic conditions required to attain continuous-phase binary fluid mixing near critical conditions. Then, a 3D surface diagram (P,T fuel ,T amb ) separating single and two-phase regions is constructed by varying the ambient pressure and the initial temperatures of the two fluids. Polynomial fits of the 3D surfaces for 10 different binary mixtures are tabulated for nitrogen-alkane and methane-oxygen blends relevant to air-breathing and propellant based engines, respectively.
Scalable algorithms for physics-informed neural and graph networks
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.