Retrofitting of Reinforced Concrete Beam with Externally Bonded CFRP

Title:- Retrofitting of Reinforced Concrete Beam with Externally Bonded CFRP

Author Name:- J. Gopi Krishna

Abstract:- In our country many of the existing reinforced concrete structures are in need of repair or reconstruction, rehabilitation, because of deterioration due to various factors like corrosion, lack of detailing, failure of bonding between beam-column joints, increase in service loads, improper design and unexpected external lateral loads such as wind or seismic forces acting on a structure, environment and accident events etc., leading to cracking, spalling, loss of strength, deflection, etc. Strengthening of existing reinforced concrete structures is necessary to obtain an expected life span and achieve specific requirements. The need for efficient rehabilitation and strengthening techniques of existing concrete structures has resulted in research and development of composite strengthening systems. Recent experimental and analytical research have demonstrated that the use of composite materials for retrofitting existing structural components is more cost-effective and requires less effort and time than the traditional means. Fiber Reinforced Polymer (FRP) composite has been accepted in the construction industry as a capable substitute for repairing and strengthening of RCC structures. The superior properties of (FRP) polymer composite materials like high corrosion resistance, high strength, high stiffness, excellent fatigue performance and good resistance to chemical attack etc., has motivated the researchers and practicing engineers to use the polymer composites in the field of rehabilitation of structures. During past two decades, much research has been carried out on shear and flexural strengthening of reinforced concrete beams using different types of fiber reinforced polymers and adhesives. A detailed Literature review based on the previous experimental and analytical research on retrofitting of reinforced concrete beams is presented. Proposed method of strengthening the RC beam is decided based on the previous experimental and analytical research. Behaviors of retrofitted reinforced concrete beams with externally bonded CFRP with various types of resins (Epoxy, Orthophthalic Resin (GP), ISO resin) after initial load (60 % control beam) is investigated. Static load responses of all the beams under two point load method had evaluated in terms of flexural strength, crack observation, compositeness between CFRP fabric and concrete, and the associated failure modes.  

Keywords: Fiber Reinforced Polymer (FRP), CFRP fabric, reinforced concrete structures

I.   Introduction

Concrete is the most widely used man-made construction material in world. It is obtained by mixing cementing materials, water and aggregates, and sometimes admixtures is required proportions. Concrete has high compressive strength, low cost and abundant raw material, but its tensile strength is very low. Reinforced concrete, which is concrete with steel bars embedded in it. Concrete is an affordable material, which is extensively used throughout in the infrastructure of nation’s construction, industry, transportation, defense, utility, and residential sector. The flexibility and mould ability of this material, its high compressive strength, and the discovery of the reinforcing and prestressing techniques which helped to make up for its low tensile strength have contributed largely to its widespread use.

     Reinforced concrete structures often have to face modification and improvement of their performance during their service life. In such circumstances there are two possible solutions. The first is replacement and the other is retrofitting. Full structure replacement might have determinate disadvantages such as high costs for material and labour, a stronger environmental impact and inconvenience due to interruption of the function of the structure e.g. traffic problems. So if possible, it is often better to repair or upgrade the structure by retrofitting. Retrofitting methods is shown in figure 2.1.1. In recent years repair and retrofit of existing structures such as buildings, bridges, etc., have been quite prevalent among the most important challenges in Civil Engineering. 

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Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Paper Title:- Development of ANN and AFIS Models for Age Predictionof in-Service Transformer Oil Samples

Author Name:- Mohammad Aslam Ansari 

Department of Electrical Engineering 

Abstract:- Power transformer is one of the most important and expensive equipment in electrical network. The transformer oil is a very important component of power transformers. It has twin functions of cooling as well as insulation. The oil properties like viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant suffer a change with respect to time. Hence it is necessary that the oil condition be monitored regularly to predict, if possible, the remaining lifetime of the transformer oil, from time to time. Six properties such as moisture content, resistivity, tan delta, interfacial tension and flash point have been considered. The data for the six properties with respect to age, in days, has been taken from literature, whereby samples of ten working power transformers of 16 to 20 MVA installed at different substations in Punjab, India have been considered. This paper aims at developing ANN and ANFIS models for predicting the age of in-service transformer oil samples. Both the the models use the six properties as inputs and age as target. ANN (Artificial Neural Network) model uses a multi-layer feedforward network employing back propagation algorithm, and ANFIS (Adaptive Neuro Fuzzy Inference System) model is based on Sugeno model. The two models have been simulated for estimating the age of unknown transformer oil samples taken from generator transformers of Anpara Thermal Power Project in state of U.P. India. A comparative analysis of the two models has been made whereby ANFIS model has been found to yield better results than ANN model.     

Keywords: ANN, ANFIS, Power Transformer, Regression, Performance, Backpropagation Algorithm   

I.         Introduction

Power transformer is one of the most important constituent of electrical power system. The transformer oil, a very important ingredient of power transformers, acts as a heat transfer fluid and also serves the purpose of electrical insulation. Its insulating property is subjected to the degradation because of the ageing, high temperature, electrical stress and other chemical reactions. Hence it is necessary that the oil condition be monitored regularly. This will help to predict, if possible, the in-service period or remaining lifetime of the transformer oil, from time to time.

       There are several characteristics which can be measured to assess the present condition of the oil. The main oil characteristics are broadly classified as physical, chemical and electrical characteristics; some of these are viscosity, specific gravity, flash point, oxidation stability, total acid number, breakdown voltage, dissipation factor, volume resistivity and dielectric constant. There exists a co-relation among some of the oil properties and suffer a change in their values with respect to time [2]. This variation of oil properties with respect to time has been utilised to develop the two models as said earlier

      The training data for the proposed work have been obtained from literature, whereby ten working transforms of 16 to 20 MVA, 66/11 KV installed at different substations in the state of Punjab, India have been considered. The six properties of transformer oil such as breakdown voltage (BDV), moisture, resistivity, tan delta, interfacial tension and flash point have been considered as inputs and age as target. Test data have been taken from generator transformers of 250 MVA, 15.75kV/400kV from Anpara Thermal Power Project in state of U. P., India.

II.     “Ann” and “Anfis” methods

It is known that classical models need linear data for their processing, therefore models like ANN and ANFIS that are based on soft computing techniques, play an important role for solving these kinds of non-linear problems.

        Neural networks exhibit characteristics such as mapping capabilities or pattern association, generalization, robustness, fault tolerance, parallel and high speed processing. Neural networks can be trained with known examples of a problem to acquire knowledge about it. Once trained successfully, the network can be put to effective use in solving unknown or untrained instances of the problem. ANN model which uses multilayer feed forward network is based on back propagation (BP) learning algorithm of neural network. Backpropagation gives very good answers when presented with inputs never seen before. This property of generalization makes it possible to train a network on giving set of input-target pairs and get good output.

          ANFIS stands for Adaptive Neural Fuzzy Inference System. Using a given input/output data set, the toolbox function ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone, or in combination with a least squares type of method. This allows the fuzzy systems to learn from the data they are modelling. These techniques provide a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data. This learning method works similarly to that of neural networks.

III.       Development of ann model

The proposed ANN model uses “Levenburg-Marquardt (trainlm) algorithm which is independent of learning rate, hence by simply changing the number of neurons in hidden layer, training and testing error could be reduced. A total of 700 data sets obtained from literature [2] were arranged in tabular form and used for training the neural network. The model uses a simple two layer network, one hidden layer and one output layer. Input layer comprises of six neurons, one for the each input, while the output layer has a single neuron for a single output, the age of oil sample.

          It has been found that network architecture that uses 20 neurons in hidden layer gave the best performance with a regression of 0.999 and mean square error (MSE) of 83.0 ( data is non –normalized, so error looks large ) . The training continued for 184 iterations with training functions logsig in hidden layer and purelin in output layer respectively.

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A Time Domain Reference-Algorithm for Shunt Active Power Filters

A Time Domain Reference-Algorithm for Shunt Active Power Filters

Author Name:- Prof. Sumita D Chakrabortty

Abstract:- The aim of this paper is to identify an optimum control strategy of three-phase shunt active filters to minimize the total harmonic distortion factor of the supply current Power Quality (PQ) is an important measure of an electrical power system. The term PQ means to maintain purely sinusoidal current wave form in phase with a purely sinusoidal voltage wave form. The power generated at the generating station is purely sinusoidal in nature. The deteriorating quality of electric power is mainly because of current and voltage harmonics due to wide spread application of static power electronics converters, zero and negative sequence components originated by the use of single phase and unbalanced loads, reactive power, voltage sag, voltage swell, flicker, voltage interruption etc. The simulation and the experimental results of the shunt active filter, along with the estimated value of reduction in rating, show that the shunt filtering system is quite effective in compensating for the harmonics and reactive power, in addition to being cost-effective.   

Keywords: Shunt voltage inverter APF, Time domain, instantaneous active power, carrier based PWM, Control strategy etc.

I.     Introduction

The wide use of power devices (based on semi-conductor switches) in power electronic appliances (diode and thyristor rectifiers, electronic starters, UPS and HVDC systems, arc furnaces, etc…) induces the appearance of the dangerous phenomenon of harmonic currents flow in the electrical feeder networks, producing distortions in the current/voltage waveforms. As a result, harmful consequences occur: equipment overheating, malfunction of solid-state material, interferences with telecommunication systems, etc... Damping harmonics devices must be investigated when the distortion rate exceeds the thresholds fixed by the ICE 61000 and IEEE 519 standards. For a long time, tuned LC and high pass shunt passive filters were adopted as a viable harmonics cancellation solution.

II.    Shunt active filtering algorithms

The control algorithm used to generate the reference compensation signals for the active power filter determines its effectiveness. The control scheme derives the compensation signals using voltage and/or current signals sensed from the system. The control algorithm may be based on frequency domain techniques or time domain techniques. In frequency domain, the compensation signals are computed using Fourier analysis of the input voltage/current signals. In time domain, the instantaneous values of the compensation voltages/currents are derived from the sensed values of input signals. There are a large number of control algorithms in time domain such as the instantaneous PQ algorithm, synchronous detection algorithm, synchronous reference frame algorithm and DC bus voltage algorithm. The instantaneous PQ algorithm by Akagi  is based on Park’s transformation of input voltage and current signals from which instantaneous active and reactive powers are calculated to arrive at the compensation signals. This scheme is most widely used because of its fast dynamic response but gives inaccurate results under distorted and asymmetrical source conditions.

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Performance of WRF (ARW) over River Basins in Odisha, India During Flood Season 2014

Paper Title:- Performanceof WRF (ARW) over River Basins in Odisha, India During Flood Season 2014

Author Name:- Sumant Kr. Diwakar

India Meteorological Department, New Delhi, India

Abstract:- Operational Weather Research & Forecasting – Advanced Research WRF in short WRF (ARW) 9 km x 9 km Model (IMD) based rainfall forecast of India Meteorological Department (IMD) is utilized to compute rainfall forecast over River basins in Odisha during Flood season 2014. The performance of the WRF Model at the sub-basin level is studied in detail. It is observed that the IMD’s WRF (ARW) day1, day2, day3 correct forecast range lies in between 31-47 %, 37-43%, and 28-47% respectively during the flood season 2014.

Keywords: GIS; WRF (ARW); IMD; Flood 2014; Odisha     

I.      Introduction

Forecast during the monsoon season river sub-basin wise in India is difficult task for meteorologist to give rainfall forecast where the country have large spatial and temporal variations. India Meteorological Department (IMD) through its Flood Meteorological Offices (FMO) is issuing Quantitative Precipitation Forecast (QPF) sub-basin wise for all Flood prone river basins in India (IMD, 1994). There are 10 FMOs all over India spread in the flood prone river basins and FMO Bhubaneswar, Odisha is one of them. The Categories in which QPF are issued are as follows

Rainfall (in mm)

0

1-10

11-25

26-50

51-100

>100

    

    Odisha is an Indian state on the subcontinent’s east coast, by the Bay of Bengal. It is located between the parallels of 17.49’ N and 22.34’ N Latitudes and meridians of 81.27’ E and 87.29’ E Longitudes. It is surrounded by the Indian states of West Bengal to the north-east and in the east, Jharkhand to the north, Chhattisgarh to the west and north-west and Andhra Pradesh to the south. Bhubaneswar is the capital of Odisha.

     Odisha is the 9^th largest state by area in India and the 11^th largest by population. Odisha has a coastline about 480 km long. The narrow, level coastal strip including the Mahanadi river delta supports the bulk of the population. On the basis of homogeneity, continuity and physiographical characteristics, Odisha has been divided into five major morphological regions. The Odisha Coastal Plain in the east, the Middle Mountainous and Highlands Region, the Central Plateaus, the western rolling uplands and the major flood plains.     

A.      River System

The river system of Odisha comprises the Mahanadi, Brahmani, Baitarani, Subarnarekha, Vamasadhara, Burhabalanga, Rushikulya, Nagavali, Indravati, Kolab, Bahuda, Jambhira and other tributaries and distributaries.

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Evaluation of Response Reduction Factor using Nonlinear Analysis #IJIRST Journal

Paper Title:- Evaluation of Response Reduction Factor using Nonlinear Analysis 

Author Name:- Tia Toby

Department of Civil Engineering

Abstract:- The main objective of the study is to evaluate the response reduction factor of RC frames. We know that the actual earthquake force is considerably higher than what the structures are designed for. The structures can't be designed for the actual value of earthquake intensity as the cost of construction will be too high. The actual intensity of earthquake is reduced by a factor called response reduction factor R. The value of R depends on ductility factor, strength factor, structural redundancy and damping. The concept of R factor is based on the observations that well detailed seismic framing systems can sustain large inelastic deformation without collapse and have excess of lateral strength over design strength. Here the nonlinear static analysis is conducted on regular and irregular RC frames considering OMRF and SMRF to calculate the response reduction factor and the codal provisions for the same is critically evaluated. 

Keywords: Response Reduction Factor, Ductility Factor, Strength Factor, Nonlinear Analysis, Regular and Irregular Frames, OMRF, SMRF

I.    Introduction

The devastating potential of an earthquake can have major consequences on infrastructures and lifelines. In the past few years, the earthquake engineering community has been reassessing its procedures, in the wake of devastating earthquakes which have caused extensive damage, loss of life and property. These procedures involve assessment of seismic force demands on the structure and then developing design procedures for the structure to withstand the applied actions Seismic design follows the same procedure, except for the fact that inelastic deformations may be utilized to absorb certain levels of energy leading to reduction in the forces for which structures are designed. This leads to the creation of the Response Modification Factor (R factor); the all-important parameter that accounts for over-strength, energy absorption and dissipation as well as structural capacity to redistribute forces from inelastic highly stressed regions to other less stressed locations in the structure. This factor is unique and different for different type of structures and materials used. The objective of this paper is to evaluate the response reduction factor of a RC frame designed and detailed as per Indian standards IS 456, IS 1893 and IS 13920.The codal provisions for the same will be critically evaluated. Moreover parametric studies will be done on both regular and irregular buildings and finally a comparison of R value between OMRF and SMRF is also done.

II.  Definition of r factor and its components

During an earthquake, the structures may experience certain inelasticity, the R factor defines the levels of inelasticity. The R factor is allowed to reflect a structures capability of dissipating energy via inelastic behavior. The statically determinate structures response to stress will be linear until yielding takes place. But the behavioral change in structure from elastic to inelastic occurs as the yielding prevails and linear elastic structural analysis can no longer be applied. The seismic energy exerted by the structure is too high which makes the cost of designing a structure based on elastic spectrum too high. To reduce the seismic loads, IS 1893 introduces a “response reduction factor” R. So in order to obtain the exact response, it is recommended to perform Nonlinear analysis. In actual speaking R factor is a measure of overstrength and redundancy. It may be defined as a function of various parameters of the structural system, such as strength, ductility, damping and redundancy.

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Performance Assessment for Students using Different Defuzzification Techniques

Paper Title:- Performance Assessment for Students using Different Defuzzification Techniques

Author Name:- Anjana Pradeep, Jeena Thomas

Department of Computer Science & Engineering

Abstract:- The aim of this study is to evaluate the performance of students using a fuzzy expert system. The fuzzy process is based solely on the principle of taking non-precise inputs on the factors affecting the performance of students and subjecting them to fuzzy arithmetic to obtain a crisp value of the performance. The system classifies each student's performance by considering various factors using fuzzy logic. Aimed at improving the performance of fuzzy system, several defuzzification methods other than the built methods in MATLAB have been devised in this system for producing more accurate and quantifiable result.  This study provides comparison and in depth examination of various defuzzification techniques like Weighted Average Formula (WAF), WAF-max method and Quality Method (QM). A new defuzzification method named as Max-QM which is extended from Quality method that falls within the general framework is also given and commented upon in this study.      

Keywords: Fuzzy logic, Fuzzy Expert System, Defuzzification, Weighted Average Formula, Quality Method 

I.   Introduction

An expert system is a software program that can be used to solve complex reasoning tasks that usually require a (human) expert. In other words, an expert system should help a novice, or partly experienced, problem solver, to match acknowledged experts in the particular domain of problem solving that the system is designed to assist. To be more specific, expert systems are generally conceptualized as shown in Fig 1. The user makes an interaction through the interface system and the system questions the user through the same interface in order to obtain the vital information upon which a decision is to be made. Behind this interface, there are two other sub-systems viz. the knowledge base, which is made up of all the domain-specific knowledge that human experts use when solving that category of problems and the inference engine, a system that performs the necessary reasoning and uses knowledge from the knowledge base in order to come to a judgment with respect to the problem modelled [1].

     Expert system has been playing a major role in many disciplines such as in medicines, assist physician in diagnosis of diseases, in agriculture for crop management, insect control, in space technology and  in power systems for fault diagnosis[5]. Some expert systems have been developed to replace human experts and to aid humans. The use of an expert system is increasing day by day in today’s world [40]. Expert systems are becoming an integral part of engineering education and even other courses like accounting and management are also accepting them as a better way of teaching[4].Another feature that makes expert system more demanding for students is its ability to adaptively adjust the training for each particular student on the bases of individual students learning pace. This feature can be used more effectively in teaching engineering students. It should be able to monitor student’s progress and make a decision about the next step in training.

Fig. 1: Expert system structure

        The few expert systems available in the market present a lot of opportunities for the students who desire more spotlight and time to learn the subjects. Some expert systems present an interactive and friendly environment for students which encourage them to study and adopt a more practical approach towards learning. The expert systems can also act as an assistor or substitute for the teacher. Expert systems focus on each student individually and also keep track of their learning pace. This behavior of an expert system provides autonomous learning procedure for both student and teacher, where teachers act as mentor and students can judge their own performance. Expert system is not only beneficial for the students but also for the teachers which help them guiding students in a better way.

        The integration of fuzzy logic with an expert system enhances its capability and is called a fuzzy expert system, as it is useful for solving real world problems which do not require a precise solution. So, there is a need to develop a fuzzy expert system as it can handle imprecise data efficiently and reduces the manual working while enhancing the use of expert system[40].

      There are various factors inside and outside college that results in poor quality of academic performance of students[2,3]. To determine all the influencing factors in a single effort is a complex and difficult task. It necessitates a lot of resources and time for an educator to identify all these factors first and then plan the classroom activities and approaches of teaching and learning. It also requires appropriate training, organizational planning and skills to conduct such studies for determining the contributing factors inside and outside college. This process of identification of determinants must be given full attention and priority so that the teachers may be able to develop instructional strategies for making sure that all the students be provided with the opportunities to attain at their fullest potential in learning and performance.  By using suitable statistical package it was found that communication, learning facilities, proper guidance and family stress were the factors that affect the student performance. Communication, learning facilities and proper guidance showed a positive impact on student performance and family stress showed a negative impact on student performance. It is indicated that communication is more important factor that affect the student performance than learning facilities and proper guidance [3].

      In this research article seven most important factors are included which affect the students’ performance. These are personal factors, college environment, family factors, and university factors, teaching factors, attendance and marks obtained by students. All these factors are scaled and ranked based on the various sub-factors that are further divided from the base factors. In this study the students’ marks have been focused and not solely on social, economic, and cultural features.  To evaluate students’ performance, fuzzy expert system has been developed by considering all the seven factors as inputs to the system. This system has been developed by taking the data of students collected from St. Josephs College of Engineering and Technology, Palai affiliated to M.G University.

II.   Literature review

In recent years, many researchers worked on the applications of fuzzy logic and fuzzy sets in educational assessments and grading systems. Biswas[25] presented two methods for evaluating  students’ answer scripts using fuzzy sets and a matching function: a fuzzy evaluation method (FEM) and a generalized fuzzy evaluation method. He used fuzzy set theory in student evaluation and found that it is potentially finer than awarding grades or numbers when evaluating answer scripts. He also highlighted that the importance of education system should be to provide students with the evaluation reports regarding their test/examination as sufficient as possible with unavoidable error as small as possible so as to make evaluation system more transparent and fairer to students.

                Chen and Lee [26] presented two methods for applying fuzzy sets to overcome the problem of giving two different fuzzy marks to students with the same total score which could arise from Biswas’ method. Their methods perform calculations much faster and complicated matching operations were not required. Echauz and Vachtsevanos [27] proposed a fuzzy logic system for translating traditional scores into letter-grades. Law [28] built a fuzzy structure model with its algorithm to aggregate different test scores in order to produce a single score for individual students in an educational grading system. A method to build the membership functions (MFs) of several linguistic values with different weights was also proposed in this paper. 

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#IJIRST Journal : A Review on Thermal Insulation and Its Optimum Thickness to Reduce Heat Loss

Title:- A Review on Thermal Insulation and Its Optimum Thickness to Reduce Heat Loss

Author Name: Dinesh Kumar Sahu, Prakash Kumar Sen, Gopal Sahu, Ritesh Sharma, Shailendra Bohidar

Department of Mechanical Engineering

Abstract:- An understanding of the mechanisms of heat transfer is becoming increasingly important in today’s world. Conduction and convection heat transfer phenomena are found throughout virtually all of the physical world and the industrial domain. A thermal insulator is a poor conductor of heat and has a low thermal conductivity. In this paper we studied that Insulation is used in buildings and in manufacturing processes to prevent heat loss or heat gain. Although its primary purpose is an economic one, it also provides more accurate control of process temperatures and protection of personnel. It prevents condensation on cold surfaces and the resulting corrosion. We also studied that critical radius of insulation is a radius at which the heat loss is maximum and above this radius the heat loss reduces with increase in radius. We also gave the concept of selection of economical insulation material and optimum thickness of insulation that give minimum total cost.       

Keywords: Heat, Conduction, Convection, Heat Loss, Insulation

I.    Introduction

Heat flow is an inevitable consequence of contact between objects of differing temperature. Thermal insulation provides a region for insulation in which thermal conduction is reduced or thermal radiation is reflected rather than absorbed by the lower temperature body. To change the temperature of an object, energy is required in the form of heat generation to increase the temperature, or heat extraction to reduce the temperature. Once the heat generation or heat extraction is terminated a reverse flow of heat occurs to reverse the temperature back to ambient. To maintain a given temperature considerable continuous energy is required. Insulation will reduce this energy loss.

     Heat may be transferred in three mechanisms: conduction, convection and radiation. Thermal conduction is the molecular transport of heat under the effect of temperature gradient. Convection mechanism of heat occurs in liquids and gases, whereby the flow processes transfer heat. Free convection is flow caused by the differences in density as a result of temperature differences. Forced convection is flow caused by external influences (wind, ventilators, etc.). Thermal radiation mechanism occurs when thermal energy is emitted similar to light radiation.

      Heat transfers through insulation material occur by means of conduction, while heat loss to or heat gain from atmosphere occurs by means of convection and radiation. Materials, which have a low thermal conductivity, are those, which have a high proportion of small voids containing air or gases. These voids are not big enough to transmit heat by convection or radiation, and therefore reduce the flow of heat. Thermal insulation materials come into the latter category. Thermal insulation materials may be natural substances or man-made.

II.   The Need for Insulation

A thermal insulator is a poor conductor of heat and has a low thermal conductivity. Insulationis used in buildings and in manufacturing processes to prevent heat loss or heat gain. Although its primary purpose is an economic one, it also provides more accurate control of process temperatures and protection of personnel. It prevents condensation on cold surfaces and the resulting corrosion. Such materials are porous, containing large number of dormant air cells. Thermal insulation delivers the following benefits: [1][2]

A.      Energy Conservation

Conserving energy by reducing the rate of heat flow (fig 1) is the primary reason for insulating surfaces. Insulation materials that will perform satisfactorily in the temperature range of -268°C to 1000°C are widely available.

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