Statistical analysis of measured wind speed data’s appealing spreadsheet applications

Once the wind data is measured, the values are processed, based on statistic approach, as accurately as possible, to provide a clear over-view of the locations wind potential, being the basis of any wind farm project, representing the go or no-go in further subsequent design steps. The probability density distributions are derived from time-series data, identifying the associated distributional parameters. The wind energy potential of the locations is studied based on the Rayleigh and Weibull models, implemented with the help of Excel computations, and representing tools, to understand the wind characteristics. Based on the statistical analysis of wind conditions presented here, the results of current study can be used to make a sustainable energy yield for any location.

Study on Full-scale Measurements of Wind Field Characteristics of Coastal Complex Mountainous Terrains

Based on full-scale wind field measurements of coastal complex mountainous terrains, data of fluctuating wind velocities at the height of 30m for four sites, including mountaintop and hillsides, are obtained. The wind load characteristics of mean wind velocities, wind directions, turbulence intensities and speed-up ratios of wind velocities are comprehensively examined. Results show that the maximum mean wind velocity at the mountaintop site is 12.4 m/s. The probability density distributions of mean wind velocities for the four measurement sites can be well represented by the Weibull probability model. The predominant wind directions are around the northeast and southwest. The longitudinal, lateral and vertical turbulence intensities decrease with the increase of mean wind velocities. The turbulence intensities for the mountaintop site are as many as 0.13,0.12 and 0.089 under maximum wind velocities, respectively for the previously mentioned three directions. The speed-up ratios of wind velocities between mountaintop and hillside sites are reduced, as the wind velocities increase. However, in cases of intensive wind with mean wind velocities larger than 8 m/s, the speed-up effects of wind velocities can also appear. The maximum speed-up ratio can reach 1.17.

Assessment of Ecological Risk of Heavy Metals Using Probabilistic Risk Assessment Model (AQUARISK) in Surface Sediments from Wami Estuary, Tanzania

Total concentrations of As, Cd, Cr, Cu, Pb, and Zn in sediment samples obtained from Wami Estuary in Tanzania were used to generate contaminant probability density distributions and species sensitivity distributions using the AQUARISK model. Results of tier 1 assessment showed that As, Cd, Cr, Pb, and Zn were not of concern as their measured values and the 99th percentile of the fitted distributions were lower than the SQG low-trigger values. However, Cu was identified as of concern in this estuary. According to the Bur III distributional analysis of the exotoxicological data, the estimated percentage of species likely to be affected is 3.4, 79.4, 79.8, 99.9, 98.4, and 98.0 for As, Cd, Cr, Cu, Pb, and Zn, respectively. Lowering of the current median concentrations of metals (Cd, Cr, Cu, Pb, and Zn) is recommended as they exceeded modeled median target sediment concentration to achieve 95% or higher for species protection. With the ongoing increase in anthropogenic activities in the Wami River catchment, the environmental regulatory bodies may use the findings of the present study and augmented with AQUARISK to set discharge standards for various contaminants in order to minimize impacts to the receiving ecosystems.

Constraining a land cover map with satellite-based aboveground biomass estimates over Africa

Most land surface models can either calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFT) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing based assessments of above-ground biomass can be used to refine discretizing real-world vegetation in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous, and bare soil fractions for the 15 land cover classes, according to the UN-LCCS typology, present in Africa. Subsequently, the 2.5 and 97.5 percentile of the probability density distributions were used to create 2.5 % and 97.5 % confidence interval PFT maps. Finally the original and refined PFT maps were used to drive biomass and albedo simulations with the ORCHIDEE model. This study demonstrates that remotely sensed biomass data can be used to better constrain PFT maps. Among the advantages of using remotely sensed biomass data were the reduced dependency on expert knowledge and the ability to report the confident interval of the PFT maps. Applying this approach at the global scale, would increase confidence in the PFT maps underlying assessments of present day biomass stocks.

Are basic actors brainbound agents? Narrowing down solutions to the problem of probabilistic content for predictive perceivers

Clark (Journal of Consciousness Studies, 25(3–4), 71–87, 2018) worries that predictive processing (PP) accounts of perception introduce a puzzling disconnect between the content of personal-level perceptual states and their underlying subpersonal representations. According to PP, in perception, the brain encodes information about the environment in conditional probability density distributions over causes of sensory input. But it seems perceptual experience only presents us with one way the world is at a time. If perception is at bottom probabilistic, shouldn’t this aspect of subpersonally represented content show up in consciousness? To address this worry, Clark argues that representations underlying personal-level content are constrained by the need to provide a single action-guiding take on the environment. However, this proposal rests a conception of the nature of agency, famously articulated by Davidson (1980a, b), that is inconsistent with a view of the mind as embodied-extended. Since Clark and other enactivist PP theorists present the extended mind as an important consequence of the predictive framework, the proposal is in tension with his complete view. I claim that this inconsistency could be resolved either by retaining the Davidsonian view of action and abandoning the extended-embodied approach, or by adopting a more processual, world-involving account of agency and perceptual experience than Clark currently endorses. To solve the puzzle he raises, Clark must become a radical enactivist or a consistent internalist.

Numerical Prediction of Turbulent Spray Flame Characteristics Using the Filtered Eulerian Stochastic Field Approach Coupled to Tabulated Chemistry

The Eulerian stochastic fields (ESF) method, which is based on the transport equation of the joint subgrid scalar probability density function, is applied to Large Eddy Simulation of a turbulent dilute spray flame. The approach is coupled with a tabulated chemistry approach to represent the subgrid turbulence–chemistry interaction. Following a two-way coupled Eulerian–Lagrangian procedure, the spray is treated as a multitude of computational parcels described in a Lagrangian manner, each representing a heap of real spray droplets. The present contribution has two objectives: First, the predictive capabilities of the modeling framework are evaluated by comparing simulation results using 8, 16, and 32 stochastic fields with available experimental data. At the same time, the results are compared to previous studies, where the artificially thickened flame (ATF) model was applied to the investigated configuration. The results suggest that the ESF method can reproduce the experimental measurements reasonably well. Comparisons with the ATF approach indicate that the ESF results better describe the flame entrainment into the cold spray core of the flame. Secondly, the dynamics of the subgrid scalar contributions are investigated and the reconstructed probability density distributions are compared to common presumed shapes qualitatively and quantitatively in the context of spray combustion. It is demonstrated that the ESF method can be a valuable tool to evaluate approaches relying on a pre-integration of the thermochemical lookup-table.

3-D Kernel Density Imaging Based on the Euler Deconvolution of Tensor Gravity Data

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

Variability of distributions of wave set-up heights along a shoreline with complicated geometry

The phenomenon of wave set-up may substantially contribute to the formation of devastating coastal flooding in certain coastal areas. We study the appearance and properties of empirical probability density distributions of the occurrence of different set-up heights on an approximately 80 km long section of coastline near Tallinn in the Gulf of Finland, eastern Baltic Sea. The study area is often attacked by high waves propagating from various directions, and the typical approach angle of high waves varies considerably along the shore. The distributions in question are approximated by an exponential distribution with a quadratic polynomial as the exponent. Even though different segments of the study area have substantially different wave regimes, the leading term of this polynomial is usually small (between −0.005 and 0.005) and varies insignificantly along the study area. Consequently, the distribution of wave set-up heights substantially deviates from a Rayleigh or Weibull distribution (that usually reflect the distribution of different wave heights). In about three-quarters of the occasions, it is fairly well approximated by a standard exponential distribution. In about 25 % of the coastal segments, it qualitatively matches a Wald (inverse Gaussian) distribution. The Kolmogorov–Smirnov test (D value) indicates that the inverse Gaussian distribution systematically better matches the empirical probability distributions of set-up heights than the Weibull, exponential, or Gaussian distributions.

A Random Method for Calculation of Hoisting Drag

In reality, downhole conditions are highly unpredictable due to many uncertain and inconsistent factors, such as the uncertainty of the friction and contact between drillstring and bore-hole. As friction and contact are crucial components in torque and drag calculation, it is meaningful and practical to consider their uncertainty. This paper presents a random method for calculation of hoisting drag. Firstly, the finite element method (FEM) is used for hoisting drag calculation of a directional drilling well using Adanoy’s method in the deterministic case. Then two strategies are taken to model the random component in the downhole. The first strategy considers the randomness of the downhole friction. Instead of being a deterministic value, the friction coefficient is considered as Gaussian. The second strategy considers the randomness of contact between drillstring and wellbore. As a result, the drillstring is no longer continuously contacting with the wellbore in the curved section of well profile, which can help avoid overestimating torque and drag. Parametric studies on both strategies are conducted. Monte Carlo (MC) simulation is employed for statistical analysis. The probability density distributions and mean values of drag will be studied. The methodology can be extended into torque or drag calculation in lowering, ream in and ream out drilling conditions. Results from this paper indicate that surface hoisting drag is nearly Gaussian when the friction coefficient is Gaussian. The contact loss leads to considerable reduction in the surface hoisting drag when contact uncertainty is considered. The work of this paper will help estimate the range of surface drag and torque, which allows the well planner to develop a risk assessment for a challenging well trajectory.