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DOE OSTI2018

Using Machine Learning to Analyze Factors Determining Cycle-to-Cycle Variation in a Spark-Ignited Gasoline Engine

In this work, we have applied a machine learning (ML) technique to provide insights into the causes of cycle-to-cycle variation (CCV) in a gasoline spark-ignited (SI) engine. The analysis was performed on a set of large eddy simulation (LES) calculations of a single cylinder of a four-cylinder port-fueled SI engine. The operating condition was stoichiometric, without significant knock, at a load of 16 bar brake mean effective pressure (BMEP), at an engine speed of 2500 revolutions per minute. A total of 123 cycles was simulated. Of these, 49 were run in sequence, while 74 were run in parallel. For the parallel approach, each cycle is initialized with its own synthetic turbulent field to generate CCV, as part of another work performed by us. In the current work, we used 3D information from all 123 cycles to compute flame topology and pre-ignition flow-field metrics. We then evaluated correlations between these metrics, and peak cylinder pressure (PCP) employing an ML technique called random forest. The computed metrics form the inputs to the random forest model, and PCP is the output. This model captures the effect of all inputs, as well as interactions between them owing to its decision-tree structure. The goal of this work is to demonstrate (as a first step) that ML models can implicitly learn complex relationships between pre-ignition flow-fields, flame shapes, and the eventual outcome of the cycle (whether a cycle will be a high or a low cycle).

42 ENGINEERING↗

DOE OSTI2022

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Topology of pocket formation in turbulent premixed flames

In turbulent premixed flame propagation, the formation of isolated pockets of reactants or products is associated with flame pinch–off events which cause rapid changes in the flame surface area. Previous topological analysis of these phenomena has been carried out based on Morse theory and Direct Numerical Simulation (DNS) in two spatial dimensions. The present work extends the topological analysis to three dimensions with emphasis on the formation and subsequent burnout of reactant pockets. Singular behaviour observed previously for terms of the Surface Density Function (SDF) transport equation in the two–dimensional case is shown to occur also in three dimensions. Further singular behaviour is observed in the displacement speed close to reactant pocket burnout. In conclusion, the theory is compared against DNS data from hydrogen–air flames and good agreement is obtained.

42 ENGINEERING↗

DOE OSTI2023

Mid–height seismic isolation of equipment in nuclear power plants

An innovative seismic isolation solution for designers of safety-class equipment in advanced nuclear power plants is introduced. The test specimen was a tall, slender, carbon steel vessel that could represent a reactor vessel, steam generator, or a heat exchanger in a nuclear power plant: 240 inches tall, outer diameter of 60 inches, and wall thickness of 1 inch. The vessel was supported by three radial mounts at its mid-height, near its center of gravity, on a steel frame. The vessel was subjected to three-component ground motions using a 6DOF earthquake simulator. The specimen was filled with water for testing to indirectly account for the fluid and internal equipment present inside a prototype vessel. Three configurations were tested: non-isolated, isolated using single Friction Pendulum (SFP) bearings, and isolated using triple Friction Pendulum (TFP) bearings. The test results demonstrate that mid-height seismic isolation is practical and enables a significant reduction in horizontal spectral accelerations. Furthermore, these outcomes are not specific to the spherical sliding bearings used in the experiments but are broadly applicable to mid-height, seismically isolated equipment.

42 ENGINEERING↗

DOE OSTI2019

Assessing overall building energy performance of a large population of residential single-family homes using limited field data

The U.S. Department of Energy (DOE), through the Building Energy Codes Program (BECP), supports building energy code development and implementation processes to achieve the maximum practicable, cost-effective improvements in energy efficiency while providing safe, healthy buildings for occupants. Currently, there is a lack of statistically representative data that shows how energy codes are implemented in the field and the energy savings opportunities that exist. To address this gap, DOE initiated a research study targeted at new residential single-family homes in 8 states starting in the fall of 2014. The study comprises of a baseline phase to determine current levels of compliance with the key energy provisions of the residential building energy code, identify issues, and calculate potential energy savings from improved compliance; a second phase implementing education, training and outreach activities designed to increase targeted code compliance; and a post-study phase to measure the impact of the training and outreach activities compared to the baseline phase. Initial analyses determined the code requirements with the largest energy impact and the minimum number of observations required to detect statistically significant differences in energy use. Data was collected in the field during a single site visit to avoid bias from repeat visits. However, the single site visit principle prevented energy simulation of individual homes due to the lack of all required model inputs. To address this, energy simulation was conducted using Pacific Northwest National Laboratory’s prototype building models and employing bootstrap random sampling on the data collected from all homes visited. The modeled energy consumption using the observed field data was evaluated relative to that associated with the minimum code requirements and potential savings associated with improved compliance of select items were assessed. This paper discusses the methodology developed for this field study, and presents the results and findings from the baseline phase.

Xie, YuLong↗

DOE OSTI2016

Simulations of a Helical Tube Bundle in Cross-Flow for Application to Flow-Induced Vibration

Helical tubes have been used in a number of systems, including the energy and chemical processing industries. Helical-Coil Steam Generators (HCSGs) in particular have special application in systems for which spatial constraints are important. Variants of these designs are notably included in a number of integral-vessel small modular reactor (SMR) designs which are currently in development by multiple companies. These designs typically have the primary side located within the vessel, with the secondary side flowing upward through the helical tubes. One complexity in implementing an HCSG within the reactor pressure vessel is that any potential flow-induced vibration (FIV) can impact reactor safety. In a typical power plant, heat exchanger tube failures caused by FIV can be costly, but generally do not have immediate impact on the core safety due to their relative isolation from the core area. In an integral-vessel design, a tube rupture could eject material from the secondary side into the primary flow, potentially damaging the core itself or other crucial components. Thus a higher degree of confidence in predicting and/or preventing FIV should be attained for implementing the helical tubes in these designs. A significant campaign is currently underway to address the topic of FIV in helical tube arrays. The U.S. Department of Energy, under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) initiative, has deemed this a 'high-impact problem'. This will include both experimental and computational contributions from a consortium consisting of multiple universities, industry partners, and national laboratories, including Argonne National Laboratory. The primary industrial partner is NuScale, which will see direct benefits from this initiative to the development of their SMR and its component design. The primary focus of this paper is to establish some of the computational efforts that have been made to this stage and the initial benchmarking of computational tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗

DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Hybrid physics-based and machine learning tools for materials assessment

In this work, we develop novel physics-based and machine learning computational techniques to predict fundamental properties of metallic systems that affect radiation damage behavior in reactor structural materials. Oftentimes, atomistic predictions of engineering alloys simplify chemical compositions to a single element to reduce computational cost and complexity, introducing large sources of uncertainty and potentially missing important behavior. In addition, engineering alloys such as SS 316 are particularly challenging to simulate with first principles methods because of the additional degrees of freedom introduced by magnetic natures of the constituent elements, and very little data of this type exists within the literature. These novel methods aim to improve the qualitative prediction of radiation damage in engineering alloys by more accurately simulating their compositions, both by accelerating the computations and by developing novel analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Study on balance of axial force in helium circulator for HTR-PM - 18490

The helium circulator is one of the key equipment in the first loop of the High Temperature gas-cooled Reactor Pebble bed Module (HTR-PM). The active magnetic bearings are employed in the helium circulator, so that the inlet ring sealing clearance is about 5 to 10 times larger than that if the mechanical bearings are employed. Bigger clearance results in bigger leakage flow and bigger axial force. A new design of balance holes was proposed to reduce the axial aerodynamic force and the design principle was analyzed. To investigate the axial aerodynamic force due to leakage flow, numerical simulation of fluid dynamic was conducted in the impeller with two kinds of inlet ring sealing clearance and four types of balance holes, respectively. The calculation results shows that the balance holes with diameter 12 mm have high performance and that the designed balance holes reduce effectively about 78% of the axial aerodynamic force. The axial force was obtained from the measured real-time electric current of the axial active magnetic bearings in one helium circulator prototype. The comparison of experimental data with the CFD analysis results showed that the new design of balance holes was successful. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Analyses of Integrated Waste Treatment Unit (IWTU) TPR-8023 (1&2) Samples Including Simulant, Bed Products, and Wall Scale Formed during Fluidized Bed Steam Reforming of Sodium Bearing Waste into a Carbonate Form

This report documents Savannah River National Laboratory (SRNL) analyses of the products from the various vessels in the Integrated Waste Treatment Unit (IWTU) at the Idaho Nuclear Technology and Engineering Center (INTEC) during non-radioactive simulant processing of campaign TPR-8023 (1&2) in November/December 2015. The analyses focus on the identification of unwanted materials deposited in the IWTU during that campaign. The IWTU is designed to stabilize the acidic low-activity waste known as Sodium Bearing Waste (SBW) through a Fluidized Bed Steam Reformer (FBSR) system. The THOR® FBSR mineralizing technology uses dual reformers to pyrolyze organics in the presence of a fluidization media of steam. Organics not pyrolyzed in the Denitration Mineralization Reformer (DMR) and excess hydrogen are oxidized in the second reformer known as the Carbon Reduction Reformer (CRR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Simulating interactions of detonation, ionization chemistry, and magnetohydrodynamics

Detonation engines have the potential to remove NOx emissions from combustion based power generation by using high-temperature oxyfuel combustion. A magnetohydrodynamic (MHD) generator can directly extract power from ionized gases behind the detonation front (via the Lorentz force). This eliminates fragile moving components found in gas turbine engines unable to withstand high temperatures. Modeling detonations coupled with MHD requires the simultaneous consideration of interactions between compressible flow, chemical reactions, and an electro-magnetic field. This is achieved by coupling a Riemann solver, implicit ordinary differential equation integrators, and a Galerkin finite-element solver, which respectively solve the Euler, stiff chemical kinetics, and decoupled Maxwell equations. Each solver is separately verified and validated, and the coupled system is validated with literature results. Simulations of hydrogen and methane oxyfuel combustion are performed over a range of parameters to determine potential power extraction and emissions. In addition, seed particles are typically necessary to increase a fluid’s electrical conductivity through ionization. Therefore, impact of seed composition and quantity is also assessed. These results provide insight into the complex parasitic interactions between detonation, ionization, and MHD effects.

Zaiger, Matthew↗

DOE OSTI2016

Graphite Dust Deposition on HTGR Steam Generator: Effect of Particle-wall Impact - 18520

Generated by abrasion of fuel spheres, graphite dust is an important issue for the operation and maintenance of high-temperature gas-cooled reactor (HTGR), since the transport of fission product is closely coupled with graphite dusts. AVR experiments on dust specific radioactivity indicate that graphite dust deposition on steam generator (SG) should be of special interest. Most previous simulations on graphite dust deposition assume a hit-and-stick nature for particles, which may overestimate the deposition rate. In this work we uses a critical velocity model to account for particle-wall interaction. Combined with computational fluid dynamics simulation and discrete phase method, the deposition of graphite dust in the upper 22 layers of SG tubes was studied with particle-wall interaction considered. The result shows that the deposition rate is highly overestimated if hit-and-stick is assumed. For particles larger than 5 {mu}m, the difference can be as large as 3 times. Particle deposition rate reaches maximum at about 3 {mu}m. As for the spatial distribution, few particles deposit on the first layer of tubes due to relative low Stokes number while more depositions occur after entering bundle zone, which indicates that the unsteady wake among tubes could be a major mechanism for the deposition. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Micromovements and discomfort associated with flight mission with helmet operation tasks with different levels of cognitive workload

When performing stationary tasks under elevated cognitive workload, individuals must perform continual muscle contractions to maintain stability of the body, resulting in fatigue of the postural muscles. When the muscles perform these contractions in a prolonged manner, the body potentially responds through small changes in body movements—micromovements that may lead to discomfort. The study purpose was to evaluate impact of cognitive load on micromovements. The micromovements were measured during three different cognitive workloads; low, medium, and high. The NASA-TLX score was used to evaluate the perceived mental workload and discomfort was assessed by visual analog scale. In total, 60 subjects (30 males and 30 females) were recruited and performed cognitive tasks that simulated flight operations such as changing the radio frequency based on air traffic control messages, balancing the fuel levels in simulated fuel tanks, and aiming a reticle in a designated moving target using the cyclic control. Cognitive load was defined by the frequency of events. Micromovements were defined by changes in the center of pressure (COP) of the seat pan and COP standard deviation. It was found that the high cognitive workloads had the highest NASA-TLX scores including mental demands, temporal demands, and effort. The neck area had the highest overall levels of discomfort followed by upper back. The highest standard deviation for COP shift and number of micromovements occurred for medium cognitive workloads. In conclusion, while there were some interesting trends, few trends reached a statistical significance due to high variability among subjects for the outcome variables.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Practical Introduction to Benchmarking and Characterization of Quantum Computers

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

open quantum systems & decoherence↗

DOE OSTI2019

Experimental and modeling study of the autoignition behavior of a standard oxygenated gasoline fuel

Gasoline surrogate models can be conveniently used to simulate the combustion behavior of fuels in internal combustion engines. Because of limitations in the number of robust models available and the high computational cost of running them, simple surrogates with only 2 or 3 components (namely n-heptane, iso-octane and toluene) have been long preferred to simulate the ignition behavior of gasoline in engine calculations. More recently, thanks to the development of new, well-validated kinetic models and the access to cheap high-performance computing, the scientific and engineering community is utilizing the higher fidelity provided by more complex mixture models. The added detail in composition can in fact better reproduce variation in the fuel reactivity with pressure (an important feature for downsized, boosted conditions) and blending behavior of real fuels. In this study, the ignition behavior of a research grade full blend oxygenated fuel matching the typical specification of US standard grade gasoline has been investigated by means of rapid compression experiments and detailed kinetic calculations. A multicomponent fuel surrogate model has been developed using compositional information and a surrogate optimization tool. Based on the experimental and computational results presented in this study, general considerations about guidelines in the formulation of gasoline fuel surrogates are provided.

autoignition↗

DOE OSTI2017

Standard High Solids Vessel Design Non-Newtonian Simulant Qualification

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) is working to develop a Standard High Solids Vessel Design process vessel. To support testing of this new design, WTP engineering staff requested that a non-Newtonian simulant be developed that would represent the Most Adverse Design Condition (MADC), denoted as MADC-4, with respect to mixing performance as specified by WTP. The majority of the simulant requirements are specified in 24590-PTF-RPT-PE-16-001, Rev. 0, and a discussion of suggested non-Newtonian rheological parameters for the simulant was provided in WTP RPT-241, Rev. 0. This document describes the simulant composition that will satisfy the basis requirement along with ancillary testing to assess the rheological behavior of the simulant depending on the formulation approach used, simulant aging, and the effects of dilution. From this data, a simulant recipe to meet the basis is recommended. The simulant recipe was designed to meet a target rheology (as expressed by Bingham model parameters) of 34.5 ± 1.5 Pa [yield stress] and 33.5 ±1.5 cP [consistency] at 25 °C.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Soot Formation and Ignition Characteristics of Ethanol/Gasoline Blends in a Rapid Compression Machine

With the ever-increasing demand for sustainable energy, alcohol fuels have garnered interest for use in heavy duty engines. The significant infrastructure for ethanol production and blending of ethanol with gasoline make these fuels/fuel blends desirable candidates. However, development of heavy duty engine technology that is capable of burning alcohol fuels while retaining the advantages of traditional diesel combustion requires an improved understanding of the soot formation for these fuels under conditions relevant to mixing-controlled combustion. This work uses an extinction diagnostic to study the sooting tendency of ethanol and gasoline/ethanol blends ranging from E10 to E98 during ignition in a homogeneous environment. Experiments were conducted in a rapid compression machine (RCM) for compressed conditions of 20 ± 1 bar and an approximately constant temperature (± 10K) which was unique for each fuel. For a given soot volume fraction, a linear relationship was observed between ethanol content and the equivalence ratio in which that soot volume fraction was formed. Accounting for the oxygenated nature of ethanol, E85 and E98 fuels produced similar amounts of soot at a given Φ ox, , suggesting other factors outside of fuel oxygen content, such as fuel morphology, impact soot formation. Ignition delay data is reported for compressed pressures of 20 ± 1 bar and compressed temperatures ranging from 633 – 670 K for E10 and 771 – 789 K for E98. Varying pressures for E10 and E98 at conditions producing similar soot volume fractions demonstrated a linear dependence of soot formation on pressure, regardless of if the pressure considered was at top dead center or peak combustion pressure. Furthermore, the data gleaned from this work will be used to select soot models and chemical kinetic mechanisms for RCM simulations to ultimately model heavy duty engine technology with the studied fuels.

33 ADVANCED PROPULSION SYSTEMS↗