Stochastic Computational Fluid Mechanics
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Citations
Fast numerical methods for stochastic computations: A review
Flame acceleration and DDT in channels with obstacles: Effect of obstacle spacing
Electric field standing wave artefacts in FTIR micro-spectroscopy of biological materials
Stochastic reduced order models for random vectors: Application to random eigenvalue problems
Survey and Evaluate Uncertainty Quantification Methodologies
References
Stochastic Finite Elements: A Spectral Approach
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
Statistical Mechanics of Dissipative Particle Dynamics.
Flow-Induced Vibration
High-Order Collocation Methods for Differential Equations with Random Inputs
Related Papers (5)
Frequently Asked Questions (10)
Q2. What is the role of stochastic simulation in flow design?
In addition, stochasticsimulated responses can serve as a form of sensitivity analysis that could potentially guide experimental work and dynamic instrumentation and make the simulation–experiment interaction more meaningful.
Q3. What are the main issues that still exist?
Several outstanding issues related to long-time integration, stochastic discontinuities, adaptivity, and high dimensionality still exist.
Q4. What is the corresponding stochastic forcing term?
(11)The parameter c is the constant correlation coefficient, a gives the measure of the stochastic forcing’s strength, and i are independent random variables with zero mean and unit variance.
Q5. How do the authors obtain the perturbed shock path?
Using stochastic perturbation analysis, the authors obtain the perturbed shock path z(x; ) as, (16)where h(x) is the perturbation on the wedge and,,= 1 – s/m, and.
Q6. What is the corresponding spatial covariance kernel?
By solving the following stochastic Helmholtz equation, the authors can obtain the corresponding spatial covariance kernel Rhh(v(x1, ), v(x2, )),15v – k2v = f(x), (9)where the random forcing term f (x) is a whitenoise process, a function of the spatial vector x that satisfies[f(x1)f(x2)] = (x1 – x2).
Q7. What are the key elements to establishing multi-element gPC?
All of them are key elements to establishing multi-element gPC (with Galerkin or collocation projections) as a “mainstream” stochastic simulation approach and a powerful alternative to Monte Carlo simulation.
Q8. How do the authors approximate the desired random field u( )?
The authors first approximate the desired random field u( ) locally via gPC, where the degree of perturbation is effectively decreased by Equation 2 from O(1) to O((bk – ak)/2)).
Q9. what is the mean and variance of the perturbed shock path?
The mean and variance of the perturbed shock path are thenz(x; ) = 0,z x x s MH ( ; ) ( ) ( ) ξ χ = +Δ 1 2 10v cv v ai i i i= + ++ −2 1 1 ( ) ξq c= −1 2c e t A= −ΔR v t v tv t v t ehh ( ( , ), ( , ))[ ( , ) ( , )]1 21 2ω ωω ω= = − −| |t t A 1 2corresponding to random inflow velocity or random oscillations of the wedge around its apex.
Q10. How big is the RMS of lift coefficient?
The RMS approaches approximately the same value, 0.258, for both uniform and beta noise, which is 20 percent bigger than the time-averaged deterministic RMS, or 0.215, without noise.