Large-sample estimates of wind fluctuations over the ocean

The best quality wind data from the Norwegian sector of the North Sea, consisting of 3662 20-min time series measured at the top of the Statfjord A drilling derrick, are analyzed. Identification of Autoregressive wind models with Akaike's AIC and Achwarz's BIC measures appears to give rather arbitrary results. Spectral estimation with FFT- and AIC-identified AR-methods give almost identical results in the mean. At the higher frequencies (% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOzaaaa!36D7!\[f\] > 10^–2 s^–1) the spectrum is estimated to follow the usual inertial subrange law with little variability. The small-scale turbulent intensity is estimated to be very low, even in hurricane conditions. Comparatively, the low-frequency (% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOzaaaa!36D7!\[f\] ~ 10^–3 s^–1) fluctuations are more energetic than expected. None of the chosen low-frequency characteristica appear to be significantly linearly correlated to the available mean weather variables. However, some nonlinear relations appear to exist.     

A Prediction System for Local Wind Variations in Mountainous Terrain

A three-level model system for the prediction of local flows in mountainous terrain is described. The system is based upon an operational weather prediction model with a horizontal grid spacing of about 10 km. The large-scale flow is transformed to a more detailed terrain, first by a mesoscale model with grid spacing of about 1 km, and then by a local-scale model with a grid spacing of about 0.2 km. The weather prediction model is hydrostatic, while the two other models are non-hydrostatic. As a case study the model system has been applied to estimate wind and turbulence over Várnes airport, Norway, where data on turbulent flight conditions were provided near the runway. The actual case was chosen due to previous experiences, which indicate that south-easterly winds may cause severe turbulence in a region close to the airport. Local terrain induced turbulence seems to be the main reason for these effects. The predicted local flow in the actual region is characterized by narrow secondary vortices along the flow, and large turbulent intensity associated with these vortices. A similar pattern is indicated by the sparse observations, although there seems to be a difference in mean wind direction between data and predictions. Due to fairly coarse data for sea surface temperature, errors could be induced in the turbulence damping via the Richardson number. An adjustment for this data problem improved the predictions.     

Time-varying turbulent flows over bluff bodies predicted by finite volume integration of Reynolds equations

Dynamic flows over bluff bodies are simulated with standard models based upon Reynolds equations (k,ε)-turbulence closure and equilibrium boundary conditions. The equations are integrated by finite volume techniques. The model is applied to time varying, transverse flow over a cylinder at a plane boundary and the flow around a truncated cylinder in longitudinal oscillations. Well-behaved, plausible predictions are obtained. Accelerating flow tends to be attached even around sharp corners. Decellerated flow is associated with detachment. Laboratory scale force data are predicted reasonably realistically, without model adjustments. However, the lift force associated with a return wall jet over the transverse cylinder and the tiny damping force on the truncated cylinder are inaccurately predicted. Numerical diffusion is probably a main cause for these inaccuracies.     

Estimation of Geological Attributes from a Well Log: An Application of Hidden Markov Chains

Geophysical well logs used in petroleum exploration consist of measurements of physical properties (such as radioactivity, density, and acoustic velocity) that are digitally recorded at a fixed interval (typically half a foot) along the length of the exploratory well. The measurements are informative of the unobserved rock type alternations along the well, which is critical for the assessment of petroleum reservoirs. The well log data that are analyzed here are from a North Sea petroleum reservoir where two distinct strata have been identified from large scale seismic data. We apply a hidden Markov chain model to infer properties of the rock type alternations, separately for each stratum. The hidden Markov chain uses Dirichlet prior distributions for the Markov transition probabilities between rock types. The well log measurements, conditional on the unobserved rock types, are modeled using Gaussian distributions. Our analysis provides likelihood estimates of the parameters of the Dirichlet prior and the parameters of the measurement model. For fixed values of the parameter estimates we calculate the posterior distributions for the rock type transition probabilities, given the well log measurement data. We then propagate the model parameter uncertainty into the posterior distributions using resampling from the maximum likelihood model. The resulting distributions can be used to characterize the two reservoir strata and possible differences between them. We believe that our approach to modeling and analysis is novel and well suited to the problem. Our approach has elements in common with empirical Bayes methods in that unspecified parameters are estimated using marginal likelihoods. Additionally, we propagate the parameter uncertainty into the final posterior distributions.