Today, the study of surface properties has become widely used in industry and research, so many technologies are used to observe and study surface properties in a wide range of measurements (from millimeters to nanometers. One of the most important items in the study of surface properties is surface roughness. Surface roughness is a one of property of the material surface texture that is identified by comparing of its topography with an ideal smooth surface. A rough surface contains roughness, peaks, and Valleys that are very close to each other. There are several methods for measuring surface roughness that can be divided into contact and non-contact techniques. In the contact method, which is the basis of profillometrs stylus and scanning probe microscopes, the surface roughness is measured by moving a probe along the surface. Interferometry techniques and confocal microscopes are also common examples of non-contact roughness measuring devices. Of course, the type of surface and its physical properties also have a significant impact on choosing the right tool. In this article, some of common methods in roughness measuring and their principles are investigated and these techniques compared with the atomic force microscopy method.

Abstract Introduction: Investigation of the internal surface roughness of fluid transmission systems pipes is very important in the amount of energy loss. Different concepts and methods have been used to examine surface roughness. Some of these methods are based on roughness measurement devices and equipment in this field. Significant researches are done in surface roughness measuring in steel, copper, plastic, or coated pipes. Methods: In this study, the inner surface roughness of fiberglass pipes, has been evaluated using laboratory methods and roughness measuring devices in several diameters and two different types. Calibration and verification results of the surface roughness tester machine on sandpaper and U-PVC pipe wall surfaces are evaluated. In addition, the effect of roughness parameters and their calculated surface roughness and time using of fiberglass pipe have been investigated. Findings: According to the results, the roughness parameters Rz and Ra in the cut length of 0.8 and 2.5 respectively are suitable parameters to estimate the roughness of the inner surface of the fiberglass tube. Also, the roughness of the inner surface of biaxial tubes is lower than uniaxial tubes. In addition, in comparing the roughness of newly produced and old fiberglass pipes, the surface roughness parameters decrease due to the passage of time and the use in projects. Whereas, the roughness parameters related to the type of pipe have not changed. Conclusion: Based on the results of the Surftest SJ-210 device has best results with accuracy of the roughness height reported for fiberglass pipes is equal to 98.84%. In U-PVC, similar to fiberglass pipe, the average roughness values has been estimated with high accuracy using the Ra with a cutting length of 2.5.

The sensitivity of the microwave waves to the physical and geometrical parameters of the soil has caused the radar remote sensing to have a wide range of applications in various fields. In agricultural and environmental issues, the soil parameters, including its moisture and roughness, have been among the most important issues to the experts, the first of which is related to its physical characteristic and the second one is related to geometrical characteristic. The roughness parameter plays an important role in the soil erosion. And in order to estimate the soil moisture, the roughness must also be studied. The role of each of them in the backscattering of radar waves must be analyzed as well. There are various models to estimate these two parameters from the radar images, among which the most important ones are the IEM and SPM models. As the evaluation of the accuracy of these models in estimating the surface roughness is based on the real ground data, the calculation of the ground roughness is important. In order to measure the ground roughness, there are two methods: Euclidean Geometry and Fractal Geometry. There are many studies that have demonstrated the higher accuracy of the fractal methods in estimating the surface roughness. In order to measure the surface roughness to enter the physical models or to measure the accuracy of the roughness using the inversion of the physical models, various fractal methods have been proposed, which are based on the profile length, while In this study, a new equation for measuring the roughness by fractal method is proposed, which is based on the sampling intervals. This equation has been obtained based on the simulation of different fractal surfaces in a wide range of dimensions. The accuracy of the obtained model is RMSE =0. 12 and R2 =0. 9988. The evaluation results of this equation for the ranges outside the simulation have shown that it is a reliable method with high accuracy for measuring the surface roughness using the fractal method.

Stereolithography has the most portions through rapid prototyping techniques in injection molding and vacuum casting manufacturing because of simultaneously possessing dimensional accuracy and strength. However, low surface finish and appearance of stair-step phenomenon restrict extensive of use of this process. In this research, the influence of process parameters and part orientation on surface finish was studied. For this purpose parts were built in various conditions of surface angle, hatch space and post curing time. Surface roughness was measured by contact and non-contact method. Surface angle was in 0-180 degree range by 2 degree step. Considered post curing time was 20, 50 and 80 minutes and hatch spacing was 50, 75, 100 and 125 micrometer. In non-contact method using digital microscope, surface profile was obtained and then surface roughness calculated using MATLAB software. Finally, using Analysis of Variance (ANOVA) a mathematical model has been developed among parameters and responses. Results showed that surface finish has reverse relation with hatch space and is almost independent of post curing time. Surface roughness in up- facing increases swiftly with surface angle enhancement and then drops. In down- facing surface roughness increases slowly with surface angle enhancement and then falls. Comparison of real dates with estimated values showed that estimation average error is less than 14 percent.

Introduction Wind is known as an intermittent event because of its rapid change in direction and value. Various effects of storm on civil aviation, besides of its danger to the urban, industrial and agricultural areas, make it very important to forecast wind in appreciate lead time. Direct effect of wind on many industries, specially its role in energy generation and increasing share of wind energy in the market, made it very important. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Material and methods Different kind of observation systems including in-situ devices and remote sensing devices are useful to measure wind, and different methods are useful to detect and estimate probability of extreme events as well as forecast the wind speed. Different methods for detection and forecasting of wind have been invented and several works were done for comparing and improving them. In-situ measuring devices include, cup anemometer, ultrasonic anemometer and hotwire anemometer, while remote sensing measuring devices include, SODAR, LiDAR and radar. SODAR, LiDAR and radar operate in a similar manner except that they use different kind of pulses for transition. Generally, both the intensity and the Doppler frequency shift of the return signal are analyzed to determine wind speed, wind direction and turbulence. In spite of in-situ measuring instruments which measure the wind at a single point, remote sensing devices measure the wind in several points or a limited area. Each measuring device has its advantage and limitations witch has been listed in the paper. Wind farm deployment is moving from flat to complex terrains because of the availability of stronger winds there. The cost of site assessment through local sensing techniques is also growing due to the increasing height of meteorological masts. The maintenance required after installing the setup makes this approach even more expensive. On the other hand, remote sensing technologies are cheaper solutions, but their accuracy in complex terrains is still questionable. Turbulence also needs to be considered when measuring the wind. Turbulence is caused by (i) friction with the earth’ s surface, that is flow disturbances caused by the topographical features and (ii) thermal effects that can cause air masses to move vertically as a result of variations in temperature. Turbulent flow is chaotic with a variable pattern over a short time frame but it has a relatively constant average over longer time periods. Wind turbulence is the rapid disturbances or irregularities in the wind speed, direction, and vertical component. The most common indicator of turbulence is the standard deviation (σ ) of wind speed. σ normalized with the average wind speed gives the Turbulence Intensity (TI) of a site. Results and discussion Various methods classified according to time-scales or methodology, are available for wind forecasting. According to the time-scales, wind forecasting methods can be divided into 4 categories. (i) ultra-short-term forecasting: from few minutes to 1 hour ahead, (ii) Short-term forecasting: from 1 hour to several hours ahead, (iii) medium-term forecasting: from several hours to 1 week ahead and (iv)long-term forecasting: from 1 week to 1 year or more ahead. Each category has its own application in industry. The rapid increase in numbers of connectable devices, the expansion of networks, the implementation of new networks, and the requirement for field workers to be completely mobile but always connected (with laptops, smart tablets, smart phones), makes even more imperative the implementation of some form of Unified Communications. Otherwise it takes too long to adapt to changes. Under this paradigm the communications medium from the central server to a remote station, and around the remote station may still be varied (fibre, cable, cellular, satellite, ADSL, Radio, Microwave, WiFi, Ethernet etc. ). However the interconnection method between the different medium link modules is all the same-Ethernet, with Power over Ethernet (PoE) where practicable. New frameworks in observation systems like IOT (Internet Of Things), make a revolution in measuring methods along with data transfer. In IOT, all of the data sources (sensors), end user devices (displays, databases), and even a data source and sink (an actuator, smart phone) are connected to the Internet and have two ways communication. Conclusion This paper review the wind measuring devices along with the new frameworks of measuring methods like IOT and then presents a comparison between different wind forecasting methods. Spatial correlation method has been depicted by use of measured data of two ultrasonic wind sensors of IKIA (Imam Khomeini International Airport) in March 31st 2015. Results show strong dependencies of the observed data of two sites, and wind speed and direction in second site, follow the first site with a delay. Comparison between wind measurement by radiosonde and VVP and CAPPI products of S-band weather radar in Ahwaz shows good consistency at higher at elevation.

The objective of this research work is to evaluate the surface roughness of polyacrylonitrile (PAN) nanowebs. For this purpose the nanowebs have been prepared in different concentrations of PAN solution from 11 to 15% (by wt). Surface roughness of nanowebs was evaluated by entropy algorithm (ENT) as well as atomic force microscopy (AFM) and then the results of two methods have been compared. To evaluate surface roughness using AFM, four roughness parameters such as maximum height, ten point height, arithmetic mean of roughness (AMR), and root mean square were measured and AMR parameter was used as surface roughness. Based on the results obtained, the increase in concentration of PAN solution from 11 to 15% (by wt) would increase nanofiber diameter from 195 to 524 nm. The results obtained from two methods show that increasing the fiber diameters of nanowebs lead to the enhancement of surface roughness of samples. The correlation coefficient of surface roughness obtained from these two methods and nanofibers diameter is more than 0.90. Statistical analysis shows that there is a good agreement of surface roughness between the two methods.

Grinding is a surface finishing process, and surface roughness is one of the most important factors in evaluating the performance of the finished component. The development of a comprehensive model that can predict surface roughness over a wide range of operating conditions is still a key issue for the grinding process. In this paper, a new predictive surface roughness model for the surface grinding process is developed, based on maximum undeformed chip thickness modeling. By considering the random nature of grit distribution and gritgeometry and, thus, variations in the depth of grain penetration, the concept of a probability density function (PDF) has been utilized. Gamma PDF has been determined to be the best function, by comparing the main distribution functions in a histogram graph of chip thickness, and, based on this PDF, maximum undeformed chip thickness modeling has been carried out. The representation of chip thickness in the proposed model is a function of grinding parameters, the wheel microstructure and process kinematic conditions. The developed model for surface roughness prediction is based on the geometrical analysis of the grooves left on the surface due to the grit-work piece interaction, and has been resulted using maximum undeformed chip thickness modeling. The surface roughness model has been validated by the experimental results of the surface grinding of a thermally sprayed WC-10Co-4Cr coating. Reasonable agreement has been observed between predictions from the proposed model and the experimentally measured surface roughness. This is also supported by the values of the average percentage of error between predicted and experimental results. The average value of relative error between predicted and measured values of surface roughness was 8.53%. According to these results, it can be concluded that the proposed surface roughness model is an effective prediction technique.