The 2nd Law of Modeling & Simulation, Inequality Statements, Engineering UQ, and Credibility Assessment
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Romero, Vicente Jose [Sandia National Laboratories↗
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Numerical Investigation of Ammonia/n-Heptane Dual-Fuel Spray Flames Using Large Eddy Simulations
Diesel engines are extensively used in heavy-duty transportation, power generation, and marine vehicles due to their superior thermal efficiency and extended high-load operability compared to spark ignition (SI) engines. However, combustion in diesel engines is generally characterized by locally rich fuel–air mixtures and high combustion temperatures, causing significant amounts of soot and NO x emissions from these engines. Utilizing carbon-free alternative fuels and enhancing fuel efficiency represent promising strategies to mitigate greenhouse gas (GHG) and other emissions in the heavy-duty transportation sector. In this context, ammonia (NH 3 ), as a hydrogen carrier, has received significant attention as a viable substitute for hydrocarbon fuels due to its carbon-free composition, relatively high energy density, and well-established infrastructure. Many previous studies have considered combustion and emission characteristics of ammonia-hydrocarbon fuel blends in engines and simplified flames. But, detailed investigations on the effects of ammonia on the performance of hydrocarbon fuels under engine conditions are lacking. In the present study, we perform large eddy simulations (LES) of the ignition and flame processes in a constant-volume combustion reactor, where n-heptane is injected in an ammonia/air ambient mixture in a diesel-like environment. A detailed and validated reaction mechanism containing 302 species and 1981 reactions is employed. The Engine Combustion Network Spray H experimental data is used to validate the spray model under both non-reacting and reacting conditions. Dual-fuel combustion is simulated using the well-stirred reactor (WSR) approach. Results are presented for two spray cases: (1) single fuel (SF) with n-heptane injected into a mixture of air and combustion products and (2) dual-fuel (DF) with the injection of n-heptane in a mixture of air, ammonia, and combustion products. It is observed that the presence of ammonia has a significant effect on the ignition and flame development processes. With ammonia addition, both the first- and second-stage ignition delay times increase, but the effect of ammonia on the second-stage ignition is significantly more prominent. In addition, the ignition kernel size and growth rate decrease noticeably. For SF spray, the main ignition is characterized by multiple ignition kernels near the spray tip, whereas for DF spray, a single relatively small ignition kernel forms and grows slowly in the downstream direction. The flame development and the final quasi-steady flame structure are also modified due to ammonia. Here, the outcome of this research would enable a better understanding of ammonia–diesel dual-fuel spray flame behavior and guide the development of associated engine combustion strategies.
ammonia↗
Crack nucleation from non-metallic inclusions in aluminum alloys described by peridynamics simulations
Conventional engineering methods oftentimes have challenges in the quantification of crack nucleation processes from manufacturing defects that are relevant for engineering component lifing. In this work, we present peridynamic simulation framework for the description of the crack nucleation process from cluster of non-metallic inclusions in aluminum alloys. Our non-local simulation framework characterizes crack nucleation process as multiple micro-crack nucleation events from individual inclusions, and eventually one micro-crack dominates. We define individual stages of the crack nucleation process, i.e., nucleation, micro-crack, technical, and crack initiation, that allow a quantification and meta model development of both the individual stages and the entire crack nucleation process.
36 MATERIALS SCIENCE↗
Impact of multimode range and location on urban fuel economy on a light-duty spark-ignition based powertrain using vehicle system simulations
Multimode engine operation uses two or more combustion modes to maximize engine efficiency across the operational range of a vehicle to achieve higher overall vehicle fuel economy than is possible with a single combustion mode. More specifically for this study, multimode solutions are explored that make use of boosted SI under high load operation and other advanced combustion modes such as advanced compression ignition (ACI) under part-load conditions to enable additional engine efficiency improvements across a broader range of the engine operating map. ACI combustion has well-documented potential to improve efficiency and emissions under part-load operation but poses challenges that limit full engine speed-load range. This study investigates the potential impact of ACI operational range on simulated fuel economy to help focus research on areas with the most opportunity for improving fuel economy. These simulations make use of a vehicle model, discretized engine data, and employ a systematic exploration of ACI operational range to estimate multimode fuel economy for a mid-size passenger vehicle over U.S. Environmental Protection Agency’s Urban Dynamometer Driving Schedule. The results of this study highlight operational ranges with the highest potential fuel economy and correspondingly areas of focus for multimode research and ACI engine operation1.
Curran, Scott↗
Simulation of Internal Combustion Engines with High-Performance Computing Tools
Traditional Lagrangian spray modeling approaches for internal combustion engines are highly griddependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was developed and implemented by Senecal et al. (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against nonevaporating, evaporating, and reacting data from the literature. The current numerical study focusses on further demonstration of the grid-convergent modeling approach for simulating a single-cylinder Cat® compression ignition engine. The simulated injector is characterized with a nominal nozzle exit diameter of 259 μm. Simulations using various minimum grid sizes (ranging from 125 μm to 1000 μm) are compared for engine performance and emissions parameters of interest such as pressure, heat release rate, ignition delay, NOx, HC, and soot emissions. The peak cell-count for the highest resolution simulation was on the order of 34 million. These computationally expensive simulations were facilitated at a high-performance computing facility at Argonne National Laboratory. METIS load-balancing algorithm was developed and implemented in Converge code for the simulations. Scaling studies were also performed. The validity of previously recommended grid settings (ASME-ICEF2012-92043) for accuracy/runtime trade-off is further assessed. Efficacy of a simplified combustion model is also compared against a detailed chemical kinetics based combustion model.
33 ADVANCED PROPULSION SYSTEMS↗
Investigating the Origins of Cyclic Variability in Internal Combustion Engines Using Wall-Resolved Large Eddy Simulations
Modern internal combustion engines (ICE) operate at the ragged edge of stable operation characterized by high cycle-to-cycle variations (CCV). A key scientific challenge for ICE is the understanding, modeling, and control of CCV in engine performance, which can contribute to partial burns, misfire, and knock. The objective of this study is to use high-fidelity numerical simulations to improve the understanding of the causes of CCV. Nek5000, a leading high-order spectral element, open source code, is used to simulate the turbulent flow in the engine combustion chamber. Multicycle, wall-resolved large-eddy simulations (LESs) are performed for the General Motors (GM), Transparent Combustion Chamber (TCC-III) optical engine under motored operating conditions. The mean and root-mean-square (rms) of the in-cylinder flow fields at various piston positions are validated using particle image velocimetry (PIV) measurements during the intake and compression strokes. The large-scale flow structures, including the swirl and tumble flow patterns, are analyzed in detail and the causes for cyclic variabilities in these flow features are explained. The energy distribution across the different scales of the flow are quantified using one-dimensional (1D) energy spectra, and the effect of the tumble breakdown process on the energy distribution is examined. Finally, the insights from this study can help us develop improved engine designs with reduced cyclic variabilities in the in-cylinder flow leading to enhanced engine performance.
33 ADVANCED PROPULSION SYSTEMS↗
Numerical and boundary condition effects on the prediction of detonation engine behavior using detailed numerical simulations
High-fidelity numerical simulations of an experimental rotating detonation engine with discrete fuel/air injection were conducted. A series of configurations with different feed-plenum pressures but with constant equivalence ratio were studied. Detailed chemical kinetics for the hydrogen/air system is used. A resolution study for the full rotating detonation engine (RDE) system simulation is also conducted. Two kinds of boundary conditions, a total pressure boundary and a constant mass flow rate boundary, are used to assess the effects of the inlet boundary. As mass flow rate is increased, the total pressure boundary causes more error in the axial pressure distribution while the constant mass flow rate gives a better solution for all cases ran. The simulations confirm experimental findings, and reproduce qualitative as well as some of the quantitative trends. These results demonstrate that a) fuel-air mixing is highly non-uniform within the detonation chamber, leading to variations in local equivalence ratio, b) the fuel and oxidizer injectors experience significant backflow as the detonation wave passes over, but recover at different rates which further augments the inefficiencies in mixing, and c) parasitic combustion in the mixing region makes the detonation wave weak by extending the reaction zone across the wave.
33 ADVANCED PROPULSION SYSTEMS↗
Transforming Energy through Computational Excellence. Exascale Computing: Combustion; Simulating Effects of Fuel Injection Location in Supersonic Jet Engines
Computational tractable simulations using an adaptive-mesh-refinement solverfor compressible reacting flows help researchers understand how variations in fuel injection location within the supersonic flow cavity impacts combustion efficiency. By identifying the important physical determinants of the combustion processes, this study shows a promising pathway to improving flame stability and combustion efficiency, as well as reducing emissions.
adaptive mesh refinement↗
KIVA Software
KIVA is a family of Fortran-based Computational Fluid Dynamics (CFD) software that predicts complex multi-species multi-phase turbulent fuel and air flows. KIVA models all engine combustion processes with models for spray, soot formation, spark ignition, and reactive chemistry (including pollutantformation processes). Employing KIVA software helps to optimize internal combustion engine processes, including diesel engines, for higher efficiency and lower emissions. KIVA engine modeling routines such as spray and core CFD Arbitrary Lagrangian-Eulerian (ALE) algorithms are now ubiquitous in the industry of engine code simulators. In addition, many engineers and researchers have made modifications to or enhanced our routines to address specific problems in engine designs.
20 FOSSIL-FUELED POWER PLANTS↗
Development of Kinetic Mechanisms for Next-Generation Fuels and CFD Simulation of Advanced Combustion Engines
Predictive chemical kinetic models are needed to represent next-generation fuel components and their mixtures with conventional gasoline and diesel fuels. These kinetic models will allow the prediction of the effect of alternative fuel blends in CFD simulations of advanced spark-ignition and compression-ignition engines. Enabled by kinetic models, CFD simulations can be used to optimize fuel formulations for advanced combustion engines so that maximum engine efficiency, fossil fuel displacement goals, and low pollutant emission goals can be achieved.
30 DIRECT ENERGY CONVERSION↗
Engineering Dynamically Decoupled Quantum Simulations with Trapped Ions
An external drive can improve the coherence of a quantum many-body system by averaging out noise sources. It can also be used to realize models that are inaccessible in the static limit, through Floquet Hamiltonian engineering. The full possibilities for combining these tools remain unexplored. We develop the requirements needed for a pulse sequence to decouple a quantum many-body system from an external field without altering the intended dynamics. Demonstrating this technique experimentally in an ion-trap platform, we show that it can provide a large improvement to coherence in real-world applications. Finally, we engineer an approximate quantum simulation of the Haldane-Shastry model, an exactly solvable paradigm for long-range interacting spins. Our results expand and unify the quantum simulation toolbox.
97 MATHEMATICS AND COMPUTING↗
Support Vector Machines for Estimating Decision Boundaries with Numerical Simulations
Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.
25 ENERGY STORAGE↗
Quantum simulation of charge and exciton transfer in multi-mode models using engineered reservoirs
Quantum simulation enables studies of open-system dynamics in non-perturbative regimes by programming electronic, vibrational, and environmental interactions on comparable energy scales. Trapped ions offer this capability, combining spins, phonons, and tunable dissipation on one platform. We demonstrate an open-system quantum simulation of charge and exciton transfer in a multi-mode linear vibronic coupling model. Using tailored spin-phonon interactions with reservoir engineering, we emulate a system with two dissipative vibrational modes coupled to donor and acceptor sites and track its non-equilibrium dynamics. We continuously tune the system from the charge transfer regime to the vibrationally assisted exciton transfer regime and find that degenerate modes enhance transfer rates at large energy gaps, while non-degenerate modes activate pathways that reduce the energy-gap dependence. Thus, the presence of one additional vibration introduces interfering pathways and reshapes non-perturbative excitation transfer. Our results establish a scalable, hardware-efficient route to simulate vibronic processes with engineered environments.
74 ATOMIC AND MOLECULAR PHYSICS↗
Innovative Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems
Numerical simulation of non-isothermal multiphase porous flow combined with reactive transport is used in a wide range of applications, which include nuclear waste repositories, enhanced recovery of petroleum reservoirs, contaminant remediation, geothermal engineering, and carbon sequestration. Understanding and predicting underground phenomena can have enormous impact on how to deal with world issues like climate change, clean water, and renewable energy. The main motivation for this proposal arises from safety assessment of future nuclear waste repositories using the U.S. Department of Energy (DOE) Geologic Disposal Safety Assessment (GDSA) Framework, and performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the nation's only active nuclear waste repository in Carlsbad, New Mexico. I am a technical staff member at Sandia National Laboratories involved in both projects.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest, and the results are compared with those predicted by the engine simulator. Our results show that a deepneural-network-based surrogate model achieves high accuracy: Pearson product-moment correlation values larger than 0.99 and mean absolute percentage error within 1.07% for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 mu s for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.
deep learning↗
Capturing Cyclic Variability in EGR Dilute SI Combustion using Multi-Cycle RANS
Dilute combustion is an effective approach to increase the thermal efficiency of spark-ignition (SI) internal combustion engines (ICEs). However, high dilution levels typically result in large cycle-to-cycle variations (CCV) and poor combustion stability, therefore limiting the efficiency improvement. In order to extend the dilution tolerance of SI engines, advanced ignition systems are the subject of extensive research. When simulating the effect of the ignition characteristics on CCV, providing a numerical result matching the measured average in-cylinder pressure trace does not deliver useful information regarding combustion stability. Typically Large Eddy Simulations (LES) are performed to simulate cyclic engine variations, since Reynold-Averaged Navier-Stokes (RANS) modeling is expected to deliver an ensemble-averaged result. In this paper it is shown that, when using RANS, the cyclic perturbations coming from different initial conditions at each cycle are not damped out even after many simulated cycles. As a result, multi-cycle RANS results feature cyclic variability. This allows evaluating the effect of advanced ignition sources on combustion stability but requires validation against the entire cycle-resolved experimental dataset. A single-cylinder GDI research engine is simulated using RANS and the numerical results for 20 consecutive engine cycles are evaluated for several operating conditions, including stoichiometric as well as EGR dilute operation. The effect of the ignition characteristics on CCV is also evaluated. Results show not only that multi-cycle RANS simulations can capture cyclic variability and deliver similar trends as the experimental data, but more importantly that RANS might be an effective, lower-cost alternative to LES for the evaluation of ignition strategies for combustion systems that operate close to the stability limit.
Scarcelli, Riccardo↗
Examining the role of flame topologies and in-cylinder flow fields on cyclic variability in spark-ignited engines using large-eddy simulation
In this work we have studied cycle-to-cycle variation (CCV) in a spark-ignited (SI) engine using large-eddy simulation (LES) in conjunction with the G-equation combustion model. A single cylinder of a four-cylinder port-fueled SI engine was simulated. 49 consecutive full cycles were computed. The operating condition studied in this work is stoichiometric and stable, and represents a load of 16 bar brake mean effective pressure (BMEP), and an engine speed of 2500 revolutions per minute (r/min). The CFD simulation shows good agreement in terms of in-cylinder pressure prediction with respect to the experiments, and is also able to capture the range of CCV observed in experiments. Further, neither the simulation, nor the experiments show any distinguishable pattern in the sequence of high and low cycles. We numerically decoupled the effects of variations in equivalence ratio fields and velocity fields to isolate the effects of differences in the velocity field and differences in the equivalence ratio field on flame development and propagation. Based on this study we inferred that for this engine, under the operating conditions studied, the differences in burn rates can be attributed to the differences in the velocity flow-field in the region around the spark gap during ignition. We then performed an analysis to identify the correlation between peak cylinder pressure (PCP) and flame topologies over all the simulated cycles. We found that high cycles (higher PCP values) are strongly correlated to flatter flame volume shapes (flattened in the piston-to-head direction), and volumes that are more symmetric about the ignition axis. In addition, these kinds of flame volumes were found to correlate well with lower values of prior-to-ignition velocity going from the intake to the exhaust side (mean flow caused by tumble) at the spark, and also higher values of prior-to-ignition velocity in the piston-to-head direction.
CCV↗