Search

The temporal trend of wind speed near the surface over land has been investigated over recent decades. A prevailing trend of decline was found in several studies for most areas in the Northern hemisphere, denoted as terrestrial stilling, with multiple causes being discussed. In this study, we focus on the impact of changes of surface roughness due to changes in land-use and vegetation cover on this variable for western Europe using the regional climate model RACMO driven by ERA5 reanalysis. We conduct two simulations at climate scale, with and without changes in land-use and vegetation on a yearly basis. We find that upper level large-scale circulation slowing down results in declining near surface mean wind speed, and that increases in surface roughness due to changes in land use and vegetation cover play an important role in intensifying this declining trend from our simulations with RACMO. However, the trends inferred from the model simulations and ERA5 are not consistent with trends derived from gridded observations database E-OBS. This could come from the complexity of integrating in-situ measurements with uncorrectable inhomogeneity to derive E-OBS.

We present a numerical study on the uncertainty quantification (UQ) of aerodynamic forces acting on a hypersonic trailing-edge flap model, as a result of input uncertainties in the experimental boundary conditions. The complex fluid–thermal-structural interaction on aerodynamic surfaces of a hypersonic flight vehicle and fluctuations in flow conditions result in uncertainties in their aerodynamic characteristics. We run the numerical simulations in US3D to quantify these uncertainties. Altogether four input uncertain parameters—inlet flow velocity, density, temperature, and the model wall temperature—are considered. We obtain the aerodynamic forces from the primal solve, as well as gradient information from a dedicated sensitivity solver. We compare the surrogate-based UQ analysis using kriging as well as gradient-enhanced kriging (GEK), accounting for significant observation errors in the gradients, and quantify the accuracy of the output probability density function (PDF). The accuracy of the predicted output PDF converges faster for GEK than for kriging, implying the importance of the gradient information in order to reduce the computational cost—in the present case, the computational cost is reduced by a median speed-up of roughly 3.0 by exploiting the gradient information available from the sensitivity solver.

With uncertainty quantification, we aim to efficiently propagate the uncertainties in the input parameters of a computer simulation, in order to obtain a probability distribution of its output. In this work, we use multi-fidelity sparse grid interpolation to propagate the uncertainty in the shape of the incoming wave for the Okushiri test-case, which is a wave tank model of a part of the 1993 Hokkaido Nansei-oki tsunami. An important issue with many uncertainty quantification approaches is the ‘curse of dimensionality’: the overall computational cost of the uncertainty propagation increases rapidly when we increase the number of uncertain input parameters. We aim to mitigate the curse of dimensionality by using a multifidelity approach. In the multifidelity approach, we combine results from a small number of accurate and expensive high-fidelity simulations with a large number of less accurate but also less expensive low-fidelity simulations. For the Okushiri test-case, we find an improved scaling when we increase the number of uncertain input parameters. This results in a significant reduction of the overall computational cost. For example, for four uncertain input parameters, accurate uncertainty quantification based on only high-fidelity simulations comes at a normalised cost of 219 high-fidelity simulations; when we use a multifidelity approach, this is reduced to a normalised cost of only 10 high-fidelity simulations.

After a jet engine is shut down, hot air rising inside the compressor disc cavities, secondary air systems, and the gas path annulus will result in a vertical temperature gradient. As the compressor rotor cools and contracts in the presence of this thermal gradient, it will bend, in a phenomenon known as thermal rotor bow. Starting an engine under bowed conditions can result in rubbing of the rotor and stator seals, adding heat to the rotor, exacerbating the rotor bow. This causal sequence of rubbing and bending is called the Newkirk Effect. In this study, 30 simulations of simplified compressor geometries have been run in three-dimensional unsteady conjugate heat transfer computational fluid dynamics coupled with finite element modelling, using a Sobol’ quasi-random sequence coupled with kriging interpolation to study the effects of three important geometric parameters on the thermal bow response of an engine. The three parameters, rotor length, rotor diameter, and compressor case wall thickness, were selected based on a similar screening test analysis performed by authors in a previous study. The results include response maps of each parameter with respect to rotor bow and clearance reduction onset time, duration, and severity, and show that length and case wall thickness exhibit linear responses due to their effect on stiffness, whereas diameter exhibits a non-linear response, due to the conflicting and competing effects on stiffness and vertical temperature difference.

One of the few acoustic travelling wave systems (TWS) in the world has been developed by Defence Science and Technology Group Australia (DST). This system uses acoustic excitation of blisks by means of a high-powered speaker cluster. The response to this acoustic excitation is measured by a laser doppler vibrometer which shows the natural frequencies and mode shapes of the blisk. To date, this system has been verified and validated on a simple, twelve-bladed research blisk with a maximum error of 3.78% from FEM modal and harmonic analysis. Furthermore, the system has been refined and tested on a current in-service blisk with an accuracy of approximately 1%. This study outlines how the TWS has been used to investigate the effects of mistuning on blisks. Promising results have been achieved with the system able to accurately identify changes in resonant behaviour due to mistuning with correlation to finite element modelling predictions. The limitations are also outlined, along with suggested areas of research and improvement. This may ultimately result in the ability to verify manufacturer blending limits and assess the effect of foreign object damage and repairs on the dynamic response characteristics of a blisk without a complex and labour intensive mechanical spin rig investigation.

Adjoint-based gradient information has been successfully incorporated to create surrogate models of the output of expensive computer codes. Exploitation of these surrogates offers the possibility of uncertainty quantification, optimization and parameter estimation at reduced computational cost. Presently, when we look for a surrogate method to include gradient information, the most common choice is gradient-enhanced Kriging (GEK). As a competitor, we develop a novel method: gradient-enhanced sparse grid interpolation. Results for two test functions, the Rosenbrock function and a test function based on the drag of a transonic airfoil with random shape deformations, show that the gradient-enhanced sparse grid interpolation is a reliable surrogate that can incorporate the gradient information efficiently for high-dimensional problems.

Cokriging is a flexible tool for constructing surrogate models on the outputs of computer models. It can readily incorporate gradient information, in which form it is named gradient-enhanced Kriging (GEK), and promises accurate surrogate models in >10 dimensions with a moderate number of sample locations for sufficiently smooth responses. However, GEK suffers from several problems: poor robustness and ill-conditionedness of the surface. Furthermore it is unclear how to account for errors in gradients, which are typically larger than errors in values. In this work we derive GEK using Bayes’ Theorem, which gives an useful interpretation of the method, allowing construction of a gradienterror contribution. The Bayesian interpretation suggests the “observation error” as a proxy for errors in the output of the computer model. From this point we derive analytic estimates of robustness of the method, which can easily be used to compute upper bounds on the correlation range and lower bounds on the observation error. We thus see that by including the observation error, treatment of errors and robustness go hand in hand. The resulting GEK method is applied to uncertainty quantification for two test problems.

Uncertainty quantification (UQ) for CFD-based ship design can require a large number of simulations, resulting in significant overall computational cost. Presently, we use an existing method, multi-fidelity Kriging, to reduce the number of simulations required for the UQ analysis of the performance of a sailing yacht hull, considering uncertainties in the tank blockage, mass and centre of gravity. We compare the UQ results with experimental values.

The objective of the method described in this work is to provide an improved reconstruction of an original flow field from experimental velocity data obtained with particle image velocimetry (PIV) technique, by incorporating the local accuracy of the PIV data. The postprocessing method we propose is Kriging regression using a local error estimate (Kriging LE). In Kriging LE, each velocity vector must be accompanied by an estimated measurement uncertainty. The performance of Kriging LE is first tested on synthetically generated PIV images of a two-dimensional flow of four counter-rotating vortices with various seeding and illumination conditions. Kriging LE is found to increase the accuracy of interpolation to a finer grid dramatically at severe reflection and low seeding conditions. We subsequently apply Kriging LE for spatial regression of stereo-PIV data to reconstruct the three-dimensional wake of a flapping-wing micro air vehicle. By qualitatively comparing the large-scale vortical structures, we show that Kriging LE performs better than cubic spline interpolation. By quantitatively comparing the interpolated vorticity to unused measurement data at intermediate planes, we show that Kriging LE outperforms conventional Kriging as well as cubic spline interpolation.