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

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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#IJIRST Journal: Evaluation of Response Reduction Factor using Nonlinear Analysis

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 over strength 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. 

         Wilson, Karr, and Freeman [29] presented an automatic grading system based on fuzzy rules and genetic algorithms. To assess the outcomes of student-centered learning using the evaluation of their peers and lecturer, a new fuzzy set approach was proposed by Ma and Zhou[30] .Wang and Chen [31] proposed a method for evaluating students answer scripts using fuzzy numbers associated with degree of confidence. They have considered degree of confidence of evaluator when awarding satisfaction level to questions of student answer scripts. Weon and Kim [32] developed an evaluation strategy based on fuzzy MFs. They pointed out that the system for students’ achievement evaluation should consider the three important factors of the questions: the difficulty, the importance, and the complexity. They used singleton functions to describe the factors of each question reflecting only the individual effect of the three factors and not the collective effect.

            Bai and Chen [34] pointed out that the difficulty factor is a very subjective parameter and may cause an argument concerning fairness in evaluation. Bai and Chen [33] proposed a method for automatically constructing grade membership functions of fuzzy rules for students’ evaluation. Bai and Chen [34] proposed a method for applying fuzzy MFs and fuzzy rules for the same purpose. To solve the subjectivity of the difficulty factor in Weon and Kim’s method [32], they obtained the level of difficulty as a function of the accuracy of the student’s answer script and the time consumed to answer the questions. However, their method still has the subjectivity problem, since the results in scores and ranks are heavily dependent on the values of several weights which are determined by the subjective knowledge of domain experts.

             In paper[35], a fuzzy logic evaluation system considering the importance, the difficulty, and the complexity of questions based on Mamdani’s[36] fuzzy inference and center of gravity (COG) defuzzification is proposed as an alternative to Bai and Chen’s method [34]. The transparency and objective nature of the fuzzy logic system makes it easy to understand and explain the results of evaluation, and thus to persuade students who are skeptical or not satisfied with the evaluation results. Li and Chen [37] proposed a method for automatically generating the weights for several attributes with fuzzy reasoning capability. Chiang and Lin [38] presented a method for applying the fuzzy set theory to teaching assessment. Chang and Sun [39] presented a method for fuzzy assessment of learning performance of junior high school students. From the previous studies, it can be found that fuzzy numbers, fuzzy sets, fuzzy rules, and fuzzy logic systems have been used for various educational grading systems. 

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

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

Author Name: Dinesh Kumar Sahu

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.

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