Design and optimization of supersonic turbines for detonation combustors

Detonation-based engines offer a potential surge in efficiency for compact thermal power systems. However, these cycles require ad-hoc components adapted to the high outlet velocity from the detonation combustors. This paper presents the design methodology of turbine stages suitable for supersonic inlet conditions and provides a detailed analysis of optimized turbine geometries. A reduced-order solver examines the supersonic blade rows’ functional design space, quantifies the turbine’s non-isentropic performance, and budgets the turbine loss for different optimized leading-edge designs and chord to pitch ratios. The shock-wave interactions were identified as the predominant contributor to turbine losses, and optimal pitch-chord ratios were determined for various inlet Mach numbers. Finally, with this tool, the specific-power output for a wide range of design configurations was computed; and the metal angle that ensures flow starting and maximizes power extraction was calculated. The detailed numerical study describes the flow interactions in a supersonic turbine and offers new correlations to guide the design of future supersonic turbines.     

可以恢复Navier-Stokes方程的3阶格子Boltzmann作用力模型

为了给出能够恢复适用于低黏性流动的Navier-Stokes方程的3阶格子Boltzmann作用力模型，修正了Shan等人给出的3阶格子Boltzmann作用力模型，并重新定义了受作用力影响的流体速度和总能。使用修正后的Shan模型,通过Chapman-Enskog展开，可以将lattice Bhatnagar-Gross-Krook(LBGK)方程恢复到Navier-Stokes方程（含能量方程）, 且没有产生任何误差项。      

A novel robust aerodynamic optimization technique coupled with adjoint solvers and polynomial chaos expansion

Uncertainty is common in the life cycle of an aircraft, and Robust Aerodynamic Optimization (RAO) that considers uncertainty is important in aircraft design. To avoid the curse of dimensionality in surrogate-based optimization, this study proposes an adjoint RAO technique called “R-Opt”. Polynomial Chaos Expansion (PCE) is coupled with the R-Opt technique to quantify uncertainty in the responses of the target (including its mean and standard deviation). Only one process of PCE model construction is required in each iteration, and the gradients of uncertainty can be inferred via chain rules. The proposed method is more efficient than prevalent methods, and avoids the problem of a disagreement over the best PCE basis from among a number of PCE models (especially in case of sparse PCE). It also supports the application of sparse PCE. Two benchmark tests and two airfoil cases were used to verify R-Opt, and the optimal solutions were deemed to be robust. It improved the mean aerodynamic performance and reduced the standard deviation of the target.