Numerical Study of Fiber Bragg Grating Sensor: Longitudinal and Transverse Detection of Temperature and Strain

Fiber Bragg Grating (FBG) structure is an periodically modulated optical fiber. It acts as a selective filter of wavelength whose reflected peak is called Bragg wavelength and it depends on the period of the fiber and the refractive index. The simulation of FBG is based on solving the Coupled Mode Theory equation by using the Transfer Matrix Method which is carried out using MATLAB. It is found that spectral reflectivity is shifted when the change of temperature and strain is uniform. Under non-uniform temperature or strain perturbation, the spectrum is both shifted and destroyed. In case of transverse loading, reflectivity spectrum is split into two peaks, the first is specific to X axis, and the second belongs to Y axis. FBGs are used in civil engineering to detect perturbations applied to buildings.

Numerical and Infrared Mapping of Temperature in Heat Affected Zone during Plasma Arc Cutting of Mild Steel

During welding or flame cutting of metals, the prediction of heat affected zone (HAZ) is critical. There is need to develop a simple mathematical model to calculate the temperature variation in HAZ and derivative analysis can be used for this purpose. This study presents analytical solution for heat transfer through conduction in mild steel plate. The homogeneous and nonhomogeneous boundary conditions are single variables. The full field analytical solutions of temperature measurement, subjected to local heating source, are derived first by method of separation of variables followed with the experimental visualization using infrared imaging. Based on the present work, it is suggested that appropriate heat input characteristics controls the temperature distribution in and around HAZ.

Designs of Temperature Measuring Device for a Re-Configured Milling Machine

The design of temperature measuring approach for a re-configured milling machine to produce friction stir welds is reported in this paper. The product design specifications for the redesigning of a milling machine were first outlined and the ranking criteria were determined. Three different concepts were generated for the temperature measurement on the reconfigured system and the preferred or the best concept was selected based on the set design ranking criteria. Further simulation and performance analysis was then conducted on the concept. The Infrared Thermography (IRT) concept was selected for the temperature measurement among other concepts generated because it is an ideal and most effective system of measurement in this regard.

Application of Smart Temperature Information Material for The Evaluation of Heat Storage Capacity and Insulation Capacity of Exterior Walls

The heat storage capacity of concrete in building shells is a major reason for excessively large electricity consumption induced by indoor air conditioning. In this research, the previously developed Smart Temperature Information Material (STIM) is embedded in two groups of exterior wall specimens (the control group contains reinforced concrete exterior walls and the experimental group consists of tiled exterior walls). Long term temperature measurements within the concrete are taken by the embedded STIM. Temperature differences between the control group and the experimental group in walls facing the four cardinal directions (east, west, south, and north) are evaluated. This study aims to provide a basic reference for the design of exterior walls and the selection of heat insulation materials.

Verified Experiment: Intelligent Fuzzy Weighted Input Estimation Method to Inverse Heat Conduction Problem

In this paper, the innovative intelligent fuzzy weighted input estimation method (FWIEM) can be applied to the inverse heat transfer conduction problem (IHCP) to estimate the unknown time-varying heat flux efficiently as presented. The feasibility of this method can be verified by adopting the temperature measurement experiment. We would like to focus attention on the heat flux estimation to three kinds of samples (Copper, Iron and Steel/AISI 304) with the same 3mm thickness. The temperature measurements are then regarded as the inputs into the FWIEM to estimate the heat flux. The experiment results show that the proposed algorithm can estimate the unknown time-varying heat flux on-line.

Modelling Indoor Air Carbon Dioxide (CO2)Concentration using Neural Network

The use of neural networks is popular in various building applications such as prediction of heating load, ventilation rate and indoor temperature. Significant is, that only few papers deal with indoor carbon dioxide (CO2) prediction which is a very good indicator of indoor air quality (IAQ). In this study, a data-driven modelling method based on multilayer perceptron network for indoor air carbon dioxide in an apartment building is developed. Temperature and humidity measurements are used as input variables to the network. Motivation for this study derives from the following issues. First, measuring carbon dioxide is expensive and sensors power consumptions is high and secondly, this leads to short operating times of battery-powered sensors. The results show that predicting CO2 concentration based on relative humidity and temperature measurements, is difficult. Therefore, more additional information is needed.

Holografic Interferometry used for Measurement of Temperature Field in Fluid

The presented paper shows the possibility of using holographic interferometry for measurement of temperature field in moving fluids. There are a few methods for identification of velocity fields in fluids, such us LDA, PIV, hot wire anemometry. It is very difficult to measure the temperature field in moving fluids. One of the often used methods is Constant Current Anemometry (CCA), which is a point temperature measurement method. Data are possibly acquired at frequencies up to 1000Hz. This frequency should be limiting factor for using of CCA in fluid when fast change of temperature occurs. This shortcoming of CCA measurements should be overcome by using of optical methods such as holographic interferometry. It is necessary to employ a special holographic setup with double sensitivity instead of the commonly used Mach-Zehnder type of holographic interferometer in order to attain the parameters sufficient for the studied case. This setup is not light efficient like the Mach-Zehnder type but has double sensitivity. The special technique of acquiring and phase averaging of results from holographic interferometry is also presented. The results from the holographic interferometry experiments will be compared with the temperature field achieved by methods CCA method.

Analysis of Tool-Chip Interface Temperature with FEM and Empirical Verification

Reliable information about tool temperature distribution is of central importance in metal cutting. In this study, tool-chip interface temperature was determined in cutting of ST37 steel workpiece by applying HSS as the cutting tool in dry turning. Two different approaches were implemented for temperature measuring: an embedded thermocouple (RTD) in to the cutting tool and infrared (IR) camera. Comparisons are made between experimental data and results of MSC.SuperForm and FLUENT software. An investigation of heat generation in cutting tool was performed by varying cutting parameters at the stable cutting tool geometry and results were saved in a computer; then the diagrams of tool temperature vs. various cutting parameters were obtained. The experimental results reveal that the main factors of the increasing cutting temperature are cutting speed (V ), feed rate ( S ) and depth of cut ( h ), respectively. It was also determined that simultaneously change in cutting speed and feed rate has the maximum effect on increasing cutting temperature.

Application of Computational Intelligence for Sensor Fault Detection and Isolation

The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, application of new probabilistic neural network features for data approximation and data classification are considered for plausibility check in temperature measurement. For this purpose, two-phase FDI mechanism was considered for residual generation and evaluation.

The Study of the Intelligent Fuzzy Weighted Input Estimation Method Combined with the Experiment Verification for the Multilayer Materials

The innovative intelligent fuzzy weighted input estimation method (FWIEM) can be applied to the inverse heat transfer conduction problem (IHCP) to estimate the unknown time-varying heat flux of the multilayer materials as presented in this paper. The feasibility of this method can be verified by adopting the temperature measurement experiment. The experiment modular may be designed by using the copper sample which is stacked up 4 aluminum samples with different thicknesses. Furthermore, the bottoms of copper samples are heated by applying the standard heat source, and the temperatures on the tops of aluminum are measured by using the thermocouples. The temperature measurements are then regarded as the inputs into the presented method to estimate the heat flux in the bottoms of copper samples. The influence on the estimation caused by the temperature measurement of the sample with different thickness, the processing noise covariance Q, the weighting factor γ , the sampling time interval Δt , and the space discrete interval Δx , will be investigated by utilizing the experiment verification. The results show that this method is efficient and robust to estimate the unknown time-varying heat input of the multilayer materials.