Tarkan Erdik

Wind Velocity Vertical Extrapolation by Extended Power Law

Wind energy gains more attention day by day as one of the clean renewable energy resources. We predicted wind speed vertical extrapolation by using extended power law. In this study, an extended vertical wind velocity extrapolation formulation is derived on the basis of perturbation theory by considering power law and Weibull wind speed probability distribution function. In the proposed methodology not only the mean values of the wind speeds at different elevations but also their standard deviations and the cross-correlation coefficient between different elevations are taken into consideration. The application of the presented methodology is performed for wind speed measurements at Karaburun/Istanbul, Turkey. At this location, hourly wind speed measurements are available for three different heights above the earth surface.

Review: Fuzzy logic approach to conventional rubble mound structures design

Stability of rock slopes are usually evaluated by empirical formulae, none of which deal with uncertainties. In fact, stability number and damage level are closely related random variables and their relationship can best be modeled by methods that explicitly take uncertainty into account as vagueness, ambiguities, and imprecision. Fuzzy logic (FL) approach is one of these methods that can deal with nonlinear, complex and uncertain systems. In this paper, the use of non-traditional FL technique is employed as a means to develop efficient predictive model for designing conventional rubble mound structures. A total of 579 experimental small and large scale test data from Van der Meer are used for calibration and verification. FL model results are compared with empirical Van der Meer model in addition to the artificial neural network (ANN) model of Mase et al. It is shown that one can forecast the stability number of conventional rubble mound structures with more significant accuracy by the FL approach than previous models.

TAKAGI-SUGENO FUZZY APPROACH IN ROCK ARMORED SLOPES FOR 2% WAVE RUNUP ESTIMATION

Runup level exceeded by 2% of the incident waves is a key parameter in rough rock armored slopes design. From the literature survey, it is seen that the two most important factors influencing runup phenomena on rock armored slopes are structure permeability (SP) and surf similarity parameter (SSP). Since the relationships between wave runup and these parameters are complex, vague and uncertain in nature, it is quite difficult to adequately examine wave runup by conventional regressional approaches. Here, an attempt is made to construct various Takagi-Sugeno [TS, 1985] fuzzy models for predicting the 2% wave runup on rock armored slopes. The developed TS fuzzy model with two inputs namely SP and SSP yielded the best result out of all constructed models and is proposed in this study. The presented model is validated by comparison with widely used empirical model of Meer and Stam [MS, 1992], recommended by the U.S. Army Corps of Engineers [2002], using the experimental data-sets of MS. The verification process is obtained through scatter diagrams and two numerical error criterias. It was found that the TS model produce better accuracy in performance than the MSs empirical model.

Discussion on “A Wavelet-neuro-fuzzy Combined Approach for Digital Relaying of Transmission Line Faults”

The use of wavelet-neuro-fuzzy intelligence methodology for depicting digital relaying of transmission line faults is a timely approach, which should be elaborated more with future research along the same line. Power system simulation work by MATLAB software is a very useful tool, but without plausible reasoning, it may lead to mechanical achievements in terms of numerical analysis but might be short of sight when linguistic bases are concerned. It seems that the authors did not consider some basic conceptions underlying the fuzzy logic (FL) principle in their proposed model. They mentioned that usually membership functions (MFs) are chosen somewhat arbitrarily and are fixed in nature, which is not quite right. None of the MFs are fixed in nature, but they are flexible depending on the degree of expert view. They misused some points, especially concerning the MFs, which are fully contrary to the philosophy of FL. We would like to present the following important subjects.

Long term 3D Hydrodynamic modelling and water surface statistics in Marmara Sea

ABSTRACT Marmara Sea (MS) lies in the strategic crossroad, accommodating one of the busiest shipping routes in the world. In general, there is a two-layer current system in the MS and Turkish Straits system; the brackish waters originating in the Black Sea (BS) (18 PSU) moving southward to the Aegean Sea (AS), and a lower layer return flow of saltier Aegean waters (38.5 PSU) back to the BS. This variability poses a challenging task within the modeling perspective. In this research, 3D hydrodynamic modeling of MS is performed in order to investigate the spatial and temporal behavior of elevations between years 2000 and 2015. During the calibration process, the grid configuration, time step, and model coefficients (Manning bed roughness coefficient, a wind drag coefficient and horizontal viscosity are coefficients) are adjusted until the computed solution produced the best match to the observed data such as water surface elevations, velocities, and net discharges. To this end, a series of simulations are made. As a result, the observed and the predicted water surface elevations follow each other very closely. The developed model could accurately estimate the net discharge as well. In order to understand the behavior of MS, elevation pattern is calculated and depicted both on annual and seasonal scales. It is demonstrated that the influence of seasonally varying strong fresh water river discharges of Danube in BS have strong influence on the water mass characteristics of the MS.

Nonlinear surface fit stability formula without any transition region for conventional breakwater design

ABSTRACT Determining the optimum weight of the armor blocks is of vital importance in the design of conventional breakwaters. The widely used formulae in the literature include the transition region from plunging to surging waves. In this paper, it is aimed to investigate a new design formula without any transition region as an alternative to widely used Van der Meer formulae. The dimensionless parameters of Van der Meer formulae as well as newly generated variables are used as inputs. Nonlinear surface fit best subset regression model is used to find the optimum input combination that keeps the nonlinear relationships. All the input parameters, their second powers, and their two-way interactions are included in the regression analyses to obtain a nonlinear surface fit. Various goodness of fit statistics are applied to check the different perspectives of the model accuracy. It is demonstrated that the proposed model gives a realistic prediction of the stability number for critical data range. Especially for high values of stability number the proposed formula outperforms the benchmark formulae of Van der Meer and Etemad-Shahidi and Bonakdar. The other advantage is that it does not contain any transition region that depends on wave conditions. Besides, there is no need to include “number of waves” and “permeability” parameters into the equation.

Discussion on “Adaptive Fuzzy System for Discrimination of Fault from Non-fault Switching in Over-current Protection”

1. On page 1373, the authors stated that there are n fuzzy rules in any neuro-fuzzy system, in which n is defined as the number of input variables. However, there should be M n rules, with M being defined as the number of membership functions (MFs) of any input variable. Since there are two inputs each with three MFs in the neuro-fuzzy model, by the authors there should be 3 D 9 fuzzy rules, not 2 rules. 2. On page 1376, the authors indicated that “Since n D 2 and M D 3 the number of parameters are 21.” This is simply not valid; in fact, there should be 2 M n premise parameters and M .nC1/ consequent parameters. In this research, there are 2 3 2 D 12 parameters for the MFs, 3.2 C 1/ D 27 parameters for the consequents, and hence, there are a total of 12 C 27 D 39 adaptive parameters, not 21. 3. It is not clear in the article as to why the Gaussian MFs prefer inputs over other alternatives such as trapezoidal, bell shaped, triangular, etc. Do the authors randomly choose it or is there any explanation for that? 4. MFs for input2 after training phase, depicted in their Figure 1, is completely against the fuzzy logic (FL) philosophy. First, in formal FL philosophy, MFs should have at least one membership degree (MD) that is equal to “1” [2]. However, the A21 MF in Figure 1 violates this principle. Second, MFs of input2 are not properly set in terms of FL principles. It is clearly seen that elements of input2 within the range of [0.15–0.85] have zero MDs. This means that none of the nine fuzzy rules in the neuro-fuzzy model in their approach are triggered. For instance, if the input2 element is within the same range, the model yields no solution.