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Reduced-order modeling of turbulent forced convection with parametric conditions

机译:参数条件下湍流强迫对流的降阶建模

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Accurate reduced-order models of turbulent flows have been traditionally constructed with the proper orthogonal decomposition (POD), however the method has been limited to prototypical flows over a narrow parameter range. An orthogonal complement subspace method is developed here to treat inhomogeneous boundary conditions while implicitly coupling the velocity and temperature fields of turbulent convection. A new flux matching procedure is formulated as a state space residual series expansion to efficiently model parameter dependent convection, greatly extending the utility of the reduced-order modeling framework. An illustrative test case of turbulent channel flow over heated blocks shows flow and thermal models with 95% accuracy over the domain can be produced, while simultaneously reducing the number of degrees of freedom by a factor of 103. Error bounds are formulated and provide a posteriori error estimates for the reduced-order model.
机译:传统上已经通过适当的正交分解(POD)构建了精确的湍流降阶模型,但是该方法仅限于在狭窄参数范围内的原型流。在此开发了正交补子空间方法来处理非均匀边界条件,同时隐式耦合湍流对流的速度和温度场。一种新的通量匹配程序被公式化为状态空间残差级数展开,以有效地建模依赖于参数的对流,从而大大扩展了降阶建模框架的实用性。加热块上湍流通道流动的说明性测试案例表明,可以生成在整个区域内具有95%精度的流动和热模型,同时将自由度的数量减少103倍。确定了误差范围并提供了后验降阶模型的误差估计。

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