Turbulent Reactive Flow Modeling in Engines: KIVA ...

Turbulent Reactive Flow Modeling in Engines: KIVA-hpFE a robust and accurate software for simulating engine dynamics Project Introduction Our research and development of KIVA-hpFE for turbulent reactive and multiphase flow modeling, particularly as related to engine combustion, is relevant to the LANL’s and DOE’s efforts for addressing national energy security. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42–50 mpg mark. These are conventional gasoline engines. With continued investment and research into new technical innovations, the potential exists to save another than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emissions and use of petroleum where vehicles account for approximately 20% of greenhouse gas emissions and obviously would be a very large stimulus to the U.S. economy. Better understanding of fuel injection and fuel–air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to minimize fuel use and unwanted emissions. Helping to accomplish this understanding, the KIVA development program is providing a state-of-the-art capability for accurately simulating combustion processes: to have a predictive methodology in a software helping industry and researchers not only meet national goals on fuel usage and emissions, but global goals. In addition, a predictive, robust, and accurate capability for simulating the engine combustion processes helps to minimize time and labor needed for development of new engine technology. Achieving more Predictive, Robust and Accurate Engine Combustion Simulations The new software KIVA-hpFE provides a state-of-the-art capability for accurately simulating turbulent reactive multi-phase flow with sprays and robust moving immersed parts (valves and pistons or any moving part). Engine combustion processes are becoming more predictive with the new methodology, more predictive than is currently available. KIVA-hpFE supplies industry and researchers a tool to help meet national goals on emissions and engine efficiencies. The software is a more predictive, robust, and accurate system for simulating engine combustion processes. To meet these goals, our program provides the following (although not a complete) list of salient attributes:  Develop mathematical and computer algorithms and software for the advancement of speed, accuracy, robustness, and range of applicability of KIVA, an internal engine combustion modeling software; to be a more predictive computer code. This is to be accomplished by employing higher-order, spatially accurate methods for reactive turbulent flow and more predictive spray injection, combined with a robust and accurate actuated moving parts process along with more appropriate turbulence modeling. The code combines stateof-the-art chemical reaction simulators such as Chemkin-Pro.  Provide engine modeling software that is easier to maintain and is easier to add models to than the current KIVA, reducing code development costs via more modern code architecture.  Provide a software capable of producing fast turn-around times needed by industry. The code not only functions well on small computer platforms but addresses high performance computing aspects required for high-fidelity more predictive solutions. By designing, inventing, and developing new modeling methods and code the goals are being achieved. The new design is a change of discretization to the finite element method (FEM) from which essentially every other beneficial and salient attribute of the software stems. We invented and developed the following systems to date (details are provided in the referenced publications).  Developed the FEM-predictor–corrector scheme projection method for high accuracy and all the benefits the FEM system brings to computational fluid dynamics (CFD) modeling of engines. The CFD solver is applicable to all flow regimes; from incompressible laminar flow to transitional and turbulent compressible flows to hypersonic flows. (Carrington 2009, Carrington et al. 2014, Waters et al. 2016)  Developed the ‘h-adaptive’ system for automatic grid refinement in a k-ω turbulence modeling FEM system. (Carrington and Pepper, 2010).  Developed the hp-adaptive system for higher order accuracy, where ‘p-adaptation’ is higher order polynomial basis approximation and ‘h-adaptive’ is automatic grid refinement collaboration with Dr. Xiuling Wang of Purdue University (Carrington et al. 2014)  Invented a local-arbitrary Lagrangian–Eulerian (ALE) method for moving bodies (Carrington et al. to appear)  Developed Immersed Boundary Method (IBM) for moving bodies using the invented marker system and some of the reconstruction techniques in the ALE scheme (Waters et al. 2018)

 Developed a new FEM form of the Vreman dynamic Large Eddy Simulation (LES) method capable of easily handling highly unsteady and transitional to fully turbulent wall-bounded flows encountered in engines. (Waters et al. 2016) o Self-damping turbulence at the walls negates the need for a law-of-the-wall system that, in general, is flawed or violated for unsteady turbulent flow in complex domains  Invented and developed Volume of Fluid (VOF) methods in FEM for modeling true multi-phase flow o To fully represent the spray break-up process, to have predictive spray modeling (Waters et al. 2017).  Developed parallel solution method that is super-linear (Waters and Carrington 2016) o Delivers superliner scalability o Delivers 30×speed-up over serial code given the same problem and settings  Developed implicit solutions methods for 10× speed-up over explicit parallel solver for an overall 300×speedup over the explicit serial version, based on the same test problems (Waters and Carrington 2016)  Invented a method for implementing Message Passing Interface (MPI) for today’s and future platforms (Waters and Carrington 2016). A scheme that only requires local development and integration of the functionals and not requiring the typical ghost cell or flux balancing processes use in traditional CFD.  A software that is significantly faster, robust and accurate than our previous finite volume based KIVA-4mpi. A software that is vastly easier to produce computational grids for simulations, meets the goal of going from CAD to CFD easily. Some Computational Features Our efforts push toward a comprehensive tool for the future with the accomplishment of grid generation, immersed moving part, KH injection spray model, multiphase (liquid and gas) compressible turbulent flow with a dynamic LES for unsteady wall-bounded flow, VOF for predictive initial liquid fuel jet break-up, along with high computational speed. Grid Generation In conjunction with Program Development Company who developed GridPro, we are working on providing high quality grids for the engine system with an eye toward ease of use. The overset immersed moving parts system used allows for easy grid generation of the cylinders and ports. The spark and injector modules are easily inserted into the engine topology. The final process of grid generation, the piston and valves surfaces simply are inserted by overlaying their surface representations after the high quality grid generation is performed automatically by GridPro. The overset moving parts greatly simplifies the gridding process, removing the need to work around immersed bodies, the process required using traditional gridding methods and immersed parts motion. The injector, engine manifolds and spark systems that are represented as topologies, separately, with the idea of making various types of injectors, spark plug and manifolds module topologies, that are simply connected to the engine cylinder topology. The final gridded engine system is shown in Figure 1 with four million cells. Gridding is a major component of CFD, where we provide quality grids with a minimum of labor, very quick and high quality, saving weeks and months on the gridding process over traditional methods.

Figure 1 - Computational grid using GridPro and overset valve and piston surfaces. In close-up, the valves and the injector module are easily married to the cylinder domain.

Immersed Moving Parts By developing 2nd order spatially accurate immersed boundary methods for moving bodies we achieve a robust moving boundary which floats through the computational grid while maintaining robustness of the solution process -- the grid never becomes distorted. Using the invented marker system that is based on methods used in our local-ALE system for moving bodies, the marker system tracks moving boundary interfaces (Carrington et al. to appear). By employing the FEM shape functions for interpolation an IBM method is created to calculate the fluid’s motion and thermodynamic state at the intersected grid cells or elements (Waters et al. 2018). A twovalve engine test case is functioning shown in Figure 2 using the immersed boundary methods, showing flow over the opening valve at a crank angle of 80° and 640o after top dead center (ATDC).

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Figure 2 - Two-Port engine. (a) Intake valve operating showing the magnitude of velocity and the streamline of the fluid entering at 80° ATDC and (b) Exhaust valve operating showing magnitude of velocity and vectors at 640° ADTC.

Spray Modeling To fully represent the spray break-up process; to produce a predictive spray model system, we’ve developed a VOF scheme in finite element method to produce a system that models true multiphase flow, allowing us to accurately model fuel/liquid jet break-up into ligaments or large drops. The breaking ligaments, once small enough as calculated by the FEM and VOF scheme, is handed-off to a Lagrangian particle method in FEM using either the Rayleigh–Taylor Analogy (TAB) or the new Kelvin Helmholtz (KH) secondary droplet break-up systems to completely model the atomization process. This allows for engineering modeling for the injection system that is more predictive. Figure 3 shows liquid being injected into air at 3 bar through an orifice of 0.01mm diameter early in time. The liquid inlet is slug flow and laminar. The break-up length is where the wave instabilities are large enough to cause ligamentation, and is found to be five orifice diameters downstream of inlet with our method. This corresponds to the results obtained by direct numerical simulation as reported in Waters et al. (2017) and seen in experimental data.

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Figure 3 - Multiphase flow simulation with VOF method, gasoline injected into quiescent air at 3 bar. (a) Gasoline jet primary break-up into ligaments and (b) primary break-up and w-component of velocity of air showing recirculation.

By adding the KH-RT spray model (Reitz 1987) and validating on an Engine Combustion Network (ECN) Spray A test case we extend KIVA’s modeling capability from the current Taylor Analogy Break-up (TAB) system. The KH model requires less tuning to experimental data than the TAB system, the tuning being the parameters for initial break-up. So with VOF and KH combined that predicted fluid ligaments can be feed into the KH model for good engineering calculations. The ECN Spray A case with injection of diesel into quiescent nitrogen at 2.2 MPa is shown in Figure 4. Validation continues in collaboration with Oakland University and Texas Technology University.

Figure 4 - The ECN Spray A case: injection of diesel in quiescent nitrogen at 2.2 MPa, KH-RT spray model

Computational Efficiency Continued work on the parallel solution method delivers superliner scalability (Waters and Carrington 2016) and a code considerable faster than KIVA-4mpi (Los Alamos National Laboratory’s previous unstructured grid code for engines) on the same problems with exact same settings and grid size. On that grid in fact KIVA-hpfE is more accurate. Hence, KIVA-hpFE produces better accuracy than previous codes and on coarser grids. The new FEM code is capable of being faster on the same resolution as old codes, but is more accurate on less resolved problems, providing additional advantage. Figure 5 demonstrates the ever increasing computational speed versus KIVA-4mpi; at 0.38 s of simulation time, KIVA-hpFE is 1.44× faster on a typical test case. The improvement in speed translates to 12× faster than the previous version of parallel KIVA over a single engine cycle.

Figure 5 - KIVA-hpFE’s ever increasing advantage in computational speed over KIVA-4mpi; at 0.38 s of simulation time, KIVA-hpFE is 1.44× faster (for the problem being solved by both systems with exactly the same settings)

KIVA-hpFE and the KIVA development program at Los Alamos National Laboratory The KIVA development program at Los Alamos National Laboratory is working to achieve the objective of having a robust CFD software for turbulent reactive flow that is particularly well-suited for combustion modeling in engines or machines where immersed moving boundaries are involved. We’ve kept an eye toward solutions being produced on quality grids created with a minimal amount of labor. Our current and future efforts include:    

Engine solid material modeling and conjugate heat transfer by marrying a Boundary Element Method (BEM) representing the solid material physics to the fluid dynamic FEM solver. Exascale coding because the operations are local to elements (vectorizable), and require very little crossdomain communication. We continue improving memory handling for the hp-adaptive FEM system, testing and improving the multiphase and spray modeling along with improving computational speed and linear equation solution methods. KIVA-hpFE is to be a software for industry and researchers via commercialization and collaboration.

References Carrington, D.B. “A characteristic-based split hp-adaptive finite element method for combustion modeling in KIVAhpFE.” LANL Scientific Report no. LA-UR-09-06527, 2009. Carrington, D.B. and D.W. Pepper, “An h-Adaptive Finite Element Method for Turbulent Heat Transfer,”

Computer Modeling in Engineering & Sciences, Tech Science Press, vol. 61, no 1, pp. 23-44, 2010 Carrington, D.B., X. Wang, and D.W. Pepper. “A predictor-corrector split projection method for turbulent reactive flow.” Journal of Computational Thermal Sciences, Begell House Inc., vol. 5, no. 4, pp.333–352, 2014. Carrington D.B., X. Wang, and D.W. Pepper. “An hp-adaptive Predictor-Corrector Split Projection Method for Turbulent Compressible Flow.” Proceedings of the 15th International Heat Transfer Conference, IHTC-15, Kyoto, Japan, August 10–15, 2014. Carrington, D.B., M. Mazumder, and J.C. Heinrich. “Three-Dimensional Local ALE-FEM Method for Fluid Flow in Domains Containing Moving Boundaries/Objects Interfaces.” Progress in Computational Fluid Dynamics, to appear. Waters, J., D.B. Carrington, and B. Philipbar. “'KIVA-hpFE 2018 Update: Meshing, Modeling and Methods for Injection, Spray Break-up and Engine Modeling.” Los Alamos Scientific Report no. 'KIVA-hpFE 2018 Update: Meshing, Modeling and Methods for Injection, Spray Break-up and Engine Modeling. AEC/HCCI Working Group Meeting at ANL, Chicago, IL, Jan. 29, 2018. Waters J., D.B. Carrington, and D.W. Pepper. “An Adaptive Finite Element Method with Dynamic LES for Incompressible and Compressible Flows.” Journal of Computational Thermal Sciences, Begell House Inc., Vol.8(1), pp.57–71, 2016. Waters, J., D.B. Carrington, and M.M. Francois. “Modeling Multi-phase Flow: Spray Break-up Using Volume of Fluids in a Dynamic LES FEM method.” Numerical Heat Transfer, Part B, vol. 72, no. 4, pp. 285–299, 2017. Waters, J., and D.B. Carrington. “A parallel Large Eddy Simulation in a finite element projection method for all flow regimes.” Numerical Heat Transfer, Part A, vol, 70, n0. 2, pp. 117–131, 2016. Reitz, R.D. “Modeling Atomization Processes in High-Pressure Vaporizing Sprays.” Atomization and Sprays Technology, vol 3, pp. 309–337, 1987. Waters, J., and D.B. Carrington. “A Dynamic Large Eddy Model for Simulating Turbulent Reactive, Flow in Engines: A Parallel adaptive Finite Element Method.” Proceedings of WCX™17: SAE International World Congress Experience, Detroit, MI, April 2–4, 2017. Waters, J., D.B. Carrington, X. Wang, and D.W. Pepper. “A Dynamic Large Eddy Model for Simulating Turbulent Reactive Flow with an Adaptive Finite Element Method,” Proceeding of the 2nd Thermal and Fluid Engineering Conference, TFEC2017, ASTFE, Las Vegas, NV, April 2–5, 2017. Waters, J, and D.B. Carrington. “Modeling Turbulent Reactive Flow in Internal Combustion Engines with an LES in a semi-implicit/explicit Finite Element Projection Method.” Proceedings of the ASME 2016 Internal Combustion Fall Technical Conference, ICEF2016, Greenville, SC, October 9–12, 2016. Waters J., and D.B. Carrington. “Parallel Large Eddy Simulation for Modeling 3D Turbulent Flow in Engines.” Proceedings of the ASME 2016 Fluids Engineering Division Summer Meeting, Washington, DC, July 10–14, 2016.