107 research outputs found
Uncertainty quantification of leakages in a multistage simulation and comparison with experiments
The present paper presents a numerical study of the impact of tip gap uncertainties in a multistage turbine. It is well known that the rotor gap can change the gas turbine efficiency but the impact of the random variation of the clearance height has not been investigated before. In this paper the radial seals clearance of a datum shroud geometry, representative of steam turbine industrial practice, was systematically varied and numerically tested. By using a Non-Intrusive Uncertainty Quantification simulation based on a Sparse Arbitrary Moment Based Approach, it is possible to predict the radial distribution of uncertainty in stagnation pressure and yaw angle at the exit of the turbine blades. This work shows that the impact of gap uncertainties propagates radially from the tip towards the hub of the turbine and the complete span is affected by a variation of the rotor tip gap. This amplification of the uncertainty is mainly due to the low aspect ratio of the turbine and a similar behavior is expected in high pressure turbines
Uncertainty Quantification for Linear Hyperbolic Equations with Stochastic Process or Random Field Coefficients
In this paper hyperbolic partial differential equations with random
coefficients are discussed. Such random partial differential equations appear
for instance in traffic flow problems as well as in many physical processes in
random media. Two types of models are presented: The first has a time-dependent
coefficient modeled by the Ornstein--Uhlenbeck process. The second has a random
field coefficient with a given covariance in space. For the former a formula
for the exact solution in terms of moments is derived. In both cases stable
numerical schemes are introduced to solve these random partial differential
equations. Simulation results including convergence studies conclude the
theoretical findings
Numerical approximation of statistical solutions of scalar conservation laws
We propose efficient numerical algorithms for approximating statistical
solutions of scalar conservation laws. The proposed algorithms combine finite
volume spatio-temporal approximations with Monte Carlo and multi-level Monte
Carlo discretizations of the probability space. Both sets of methods are proved
to converge to the entropy statistical solution. We also prove that there is a
considerable gain in efficiency resulting from the multi-level Monte Carlo
method over the standard Monte Carlo method. Numerical experiments illustrating
the ability of both methods to accurately compute multi-point statistical
quantities of interest are also presented
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