Societal and environmental pressures are forcing thermal power plant operators to deviate greatly from the generation strategies of the past. The application of high frequency start up/shut down/partial load cycles to components that may well be outside their design life makes research into the long-term integrity of at risk assets paramount. Decoupled thermal/mechanical analyses have been used in the literature to estimate anisothermal fatigue in header components, with convective boundary conditions typically assumed for internal surfaces in order to determine heat fluxes and hence a temperature field. In reality, convective coefficients are heavily dependent upon local velocity profiles. In the present work, computational fluid dynamics is used in order to better approximate the steam flow in a real power plant header, leading to a convection coefficient field that is used to solve the thermal problem. Anisothermal fatigue analysis is finally conducted using a Chaboche type model. The results of computational fluid dynamics have illustrated that heat transfer coefficient values can vary (spatially) by a factor of 5.49 over the internal header wall, with noticeable hot spots in the wake of the stub penetrations. Peak differences of 6.47% in accumulated plastic strain levels have been observed between simulations conducted with constant (simplified) and variable (computational fluid dynamics derived) thermal boundary conditions.
The flow and heat transfer over a three-dimensional axisymmetric hill and rectangular ribbed duct is computed in order to evaluate the Shear Stress Transport - Scale Adaptive Simulation (SST-SAS) turbulence model. The study presented here is relevant to turbine blade internal cooling passages and the aim is to establish whether SAS-SST is a viable alternative to other turbulence models for computations of such flows. The model investigated is based on Menter’s modification to Rotta’s k-kL model and comparison is made against experimental data as well as other models including some with scale resolving capability, such as LES, DES & hybrid LES-RANS. For the hill case the SAS model dramatically overpredicts the size of the separation bubble. The LES on the other hand proved to be more accurate even though the mesh is courser by LES standards. There is little improvement of SST-SAS compared with RANS. Broadly speaking all models predict streamwise velocity profiles for the ribbed channel with reasonable accuracy. The cross-stream velocity is underpredicted by all models. Heat transfer prediction is more accurately predicted by LES than RANS, DES & SST-SAS on a mesh that is slightly coarser than required by LES standard, however it still exhibits significant error. It is concluded that more investigation of the SST-SAS model is required to more broadly assess its viability for industrial computation.
Flow as well as geometry inside turbomachinery components such as turbine blades is complex and difficult to handle accurately. Computationally affordable Reynolds Averaged Navier Stokes (RANS) simulations are often not suitable and partly resolving simulations such as Large Eddy Simulation (LES) or hybrid RANS-LES are needed for sufficient accuracy in the area. Within industrial turbine design, these are not deployed routinely, if at all, due to their presently unaffordable computational cost and time-consuming grid generation for complex geometries. General Purpose Graphic Processing Units (GPGPUs) and other modern heterogeneous hardware offer much cheaper computational power, however, so far remain mostly unharnessed in the field of CFD due to difficulty of creating structured datasets required to utilise the GPUs effectively. While unstructured or hybrid grids can be used on massively parallel platforms, the typically irregular memory access patterns they demand usually prohibits effective scaling and GPU remains mostly idle, negating the benefits. Within CFD, structured datasets with ordered memory access patterns are most easily obtained with structured multiblock grids and such grids are an excellent candidate for GPU platforms. This is not without challenges as creating high quality structured grids over complex geometries is known to be a highly time consuming and difficult process. Another limitation of GPUs is difficulty of solving tridiagonal systems of equations efficiently on those platforms. Solution of such systems of equations is typically required for implicit time advancement or convergence acceleration techniques such as AMG and it is well established that implicit numerical schemes provide significant computational savings due to their efficiency. In the present work a novel Alternating Direction Implicit (ADI) library is integrated into the CFD system to enable scalable solution of tridiagonal systems on GPUs. In the current paper a GPU-accelerated Immersed Boundary Method (IBM) code is presented and validated for turbo-machinery applications. It is shown that the combination of IBM, a high-level Oxford Parallel library for Structured applications (OPS) and an ADI solver provide the geometric as well as computational flexibility unmatched by traditional unstructured solvers. A single source code exists for major hardware platforms and the parallel implementation is decoupled from the scientific codebase, making the code scalable and easily adaptable to any emerging, future architectures.
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