Reacting flow simulations for combustion applications require extensive computing capabilities. Leveraging the AMReX library, the Pele suite of combustion simulation tools targets the largest supercomputers available and future exascale machines. We introduce PeleC, the compressible solver in the Pele suite, and detail its capabilities, including complex geometry representation, chemistry integration, and discretization. We present a comparison of development efforts using both OpenACC and AMReX’s C++ performance portability framework for execution on multiple GPU architectures. We discuss relevant details that have allowed PeleC to achieve high performance and scalability. PeleC’s performance characteristics are measured through relevant simulations on multiple supercomputers. The success of PeleC’s design for exascale is exhibited through demonstration of a 160 billion cell simulation and weak scaling onto 100% of Summit, an NVIDIA-based GPU supercomputer at Oak Ridge National Laboratory. Our results provide confidence that PeleC will enable future combustion science simulations with unprecedented fidelity.

PeleC: An adaptive mesh refinement solver for compressible reacting flows

Prospects for scramjet engines in reusable launch applications: A review

A Mach 1.5 non-reactive flow in a cavity-stabilized combustor of a model scramjet is studied via a direct-numerical simulation approach, and the analysis is focused on the interaction among boundary layer, free shear-layer above the cavity and shock wave. It is found that the impingement of the free shear-layer on the aft wall of the cavity leads to strong turbulence kinetic energy, high local pressure, and a fan of compression waves. The compression waves evolve into an oblique shock, which reflects between the upper and lower walls and interacts with the boundary layers attached to the two walls. The analysis of the turbulence production reveals that the amplification of turbulence in the core of the shear-layer and around the reattachment point is mainly due to the shear production, but the deceleration production mechanism presents a significant impact in the regions above the aft wall of the cavity and around the shock interaction points. The very low frequency commonly observed in shock wave/boundary layer interactions is not observed in the present research, which might be due to the low Reynolds number of the studied case.

Direct numerical simulation of supersonic internal flow in a model scramjet combustor under a non-reactive condition

The spatiotemporal flow field in a wind farm determines the wind turbines' energy production and structural fatigue. However, it is not obtainable by the current measurement, modeling, and prediction tools in wind industry. Here we propose a novel data and knowledge fusion approach to create the first digital twin for onshore/offshore wind farm flow system, which can predict the in situ spatiotemporal wind field covering the entire wind farm. The digital twin is developed by integrating the Lidar measurements, the Navier–Stokes equations, and the turbine modeling using actuator disk method, via physics-informed neural networks. The design enables the seamless integration of Lidar measurements and turbine operating data for real-time flow characterization, and the fusion of flow physics for retrieving unmeasured wind field information. It thus addresses the limitations of existing wind prediction approaches based on supervised machine learning, which cannot achieve such prediction because the training targets are not available. Case studies of a wind farm under typical operating scenarios (i.e. a greedy case, a wake-steering case, and a partially-operating case) are carried out using high-fidelity numerical experiments, and the results show that the developed digital twin achieves very accurate mirroring of the physical wind farm, capturing detailed flow features such as wake interaction and wake meandering. The prediction error for the flow fields, on average, is just 4.7% of the value range. With the accurate flow field information predicted, the digital twin is expected to enable brand new research across wind farm lifecycle including monitoring, control, and load assessment. 

Digital twin of wind farms via physics-informed deep learning

Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows

Adaptive mesh based combustion simulations of direct fuel injection effects in a supersonic cavity flame-holder

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations

This paper is associated with a video winner of a 2020 American Physical Society's Division of Fluid Dynamics (DFD) Gallery of Fluid Motion Award for work presented at the DFD Gallery of Fluid Motion. The original video is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2020.GFM.V0026.

Visualizations of direct fuel injection effects in a supersonic cavity flameholder

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Adaptive mesh based combustion simulations of direct fuel injection effects in a supersonic cavity flame-holder

Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations

Visualizations of direct fuel injection effects in a supersonic cavity flameholder