A flux splitting scheme is proposed for the general nonequilibrium flow equations with an aim at removing numerical dissipation of Van-Leer-type flux-vector splittings on a contact discontinuity. The scheme obtained is also recognized as an improved Advection Upwind Splitting Method (AUSM) where a slight numerical overshoot immediately behind the shock is eliminated. The proposed scheme has favorable properties: high-resolution for contact discontinuities; conservation of enthalpy for steady flows; numerical efficiency; applicability to chemically reacting flows. In fact, for a single contact discontinuity, even if it is moving, this scheme gives the numerical flux of the exact solution of the Riemann problem. Various numerical experiments including that of a thermo-chemical nonequilibrium flow were performed, which indicate no oscillation and robustness of the scheme for shock/expansion waves. A cure for carbuncle phenomenon is discussed as well.

A Flux Splitting Scheme with High-Resolution and Robustness for Discontinuities(Proceedings of the 12th NAL Symposium on Aircraft Computational Aerodynamics)

Bending analysis of FGM plates under hygro-thermo-mechanical loading using a four variable refined plate theory

Results from the Fourth AIAA Drag Prediction Workshop (DPW-IV) are summarized. The workshop focused on the prediction of both absolute and differential drag levels for wing-body and wing-body-horizontal-tail configurations that are representative of transonic transport air- craft. Numerical calculations are performed using industry-relevant test cases that include lift- specific flight conditions, trimmed drag polars, downwash variations, dragrises and Reynolds- number effects. Drag, lift and pitching moment predictions from numerous Reynolds-Averaged Navier-Stokes computational fluid dynamics methods are presented. Solutions are performed on structured, unstructured and hybrid grid systems. The structured-grid sets include point- matched multi-block meshes and over-set grid systems. The unstructured and hybrid grid sets are comprised of tetrahedral, pyramid, prismatic, and hexahedral elements. Effort is made to provide a high-quality and parametrically consistent family of grids for each grid type about each configuration under study. The wing-body-horizontal families are comprised of a coarse, medium and fine grid; an optional extra-fine grid augments several of the grid families. These mesh sequences are utilized to determine asymptotic grid-convergence characteristics of the solution sets, and to estimate grid-converged absolute drag levels of the wing-body-horizontal configuration using Richardson extrapolation.

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Summary of the Fourth AIAA CFD Drag Prediction Workshop

Results from the Second AIAA Drag PredictionWorkshop are summarized. The workshop focused on absolute
and configuration delta drag prediction of the DLR, German Aerospace Research Center F6 geometry, which is
representative of transport aircraft designed for transonic flight. Both wing–body and wing–body–nacelle–pylon
configurations are considered. Comparisons are made using industry relevant test cases that include single-point
conditions, drag polars, and drag-rise curves. Drag, lift, and pitching moment predictions from several different
Reynolds averagedNavier–Stokes computational fluid dynamics codes are presented and compared to experimental
data. Solutions on multiblock structured, unstructured, and overset structured grids using a variety of turbulence
models are considered. Results of a grid-refinement study and a comparison of tripped transition vs fully turbulent
boundary-layer computations are reported.

Data Summary from Second AIAA Computational Fluid Dynamics Drag Prediction Workshop

High-order methods for computational fluid dynamics: A brief review of compact differential formulations on unstructured grids

UPACS (Unified Platform for Aerospace Computational Simulation), a computational environment for CFD (Computational Fluid Dynamic) in the aerospace field, has been in development at NAL (National Aerospace Laboratory of Japan) since 1998, which includes two years of support by the ITBL (IT-Based Laboratory) project. It aims to overcome the increasing programming difficulties with recent CFD codes on parallel computers for aerospace applications. The codes mainly require applicability to complex geometries, higher accuracy, and coupling with heat conduction analysis in materials and structure analysis. UPACS successfully developed a general parallelized CFD code for aerospace applications in Japan. This paper describes in detail programming, applications, and parallel performance on the newly installed supercomputer at NAL.

The Development of the UPACS CFD Environment

OpenACC is a new accelerator programming interface that provides a set of OpenMP-like loop directives for the programming of accelerators in an implicit and portable way It allows the programmer to express the offloading of data and computations to accelerators, such that the porting process for legacy CPU-based applications can be significantly simplified This paper focuses on the performance aspects of OpenACC using two micro benchmarks and one real-world computational fluid dynamics application Both evaluations show that in general OpenACC performance is approximately 50\% lower than CUDA However, for some applications it can reach up to 98\% with careful manual optimizations The results also indicate several limitations of the OpenACC specification that hamper full use of the GPU hardware resources, resulting in a significant performance gap when compared to a fully tuned CUDA code The lack of a programming interface for the shared memory in particular results in as much as three times lower performance

CUDA vs OpenACC: performance case studies with kernel benchmarks and a memory-bound CFD application

A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm for dataset for a small number of degree of freedom (DoF). It also illustrates that EKFDMD requires significant numerical resources for many-degree-of-freedom (many-DoF) problems and that the combination with truncated proper orthogonal decomposition (trPOD) helps us to apply the EKFDMD algorithm to many-DoF problems, though it prevents the algorithm from being fully online. The numerical experiments of a noisy dataset with a small number of DoFs illustrate that EKFDMD can estimate eigenvalues better than or as well as the existing algorithms, whereas EKFDMD can also denoise the original dataset online. In particular, EKFDMD performs better than existing algorithms for the case in which system noise is present. The EKFDMD with trPOD, which unfortunately is not fully online, can be successfully applied to many-DoF problems, including a fluid-problem example, and the results reveal the superior performance of system identification and denoising.

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Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising.

A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combination with truncated proper orthogonal decomposition for many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can estimate eigenmodes more precisely compared with standard DMD or total least-squares DMD (tlsDMD) methods for the severe noise condition if the nature of the observation noise is known, though tlsDMD works better than KFDMD in the low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely even when the system matrix varies with time similar to online DMD, and this extension is naturally conducted owing to the characteristics of the Kalman filter. In summary, the KFDMD is a promising tool with strong antinoise characteristics for analyzing sequential datasets.

Ryoji Takaki

Papers

The Development of the UPACS CFD Environment

CUDA vs OpenACC: performance case studies with kernel benchmarks and a memory-bound CFD application

Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising.

Dynamic mode decomposition using a Kalman filter for parameter estimation

CFD Sensitivity of Drag Prediction on DLR-F6 Configuration by Structured Method and Unstructured Method