(SoE) School of Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

A mechanistic model has been developed to predict corrosion rates in multiphase (water/oil/CO2) flow conditions. The model takes into account electrochemistry, reaction kinetics, and, mass transport effects. This paper describes the equations used to determine pH and bulk concentrations of various ions, which are then used to calculate the mass transfer rates to the corrosion surface. The result includes the determination of the mass transfer coefficients of various ionic species and corrosion rates. Details of relations used for determination of mass transfer coefficients for multiphase flows, and rates of electrochemical reaction kinetics are discussed and predicted results are compared with experimental observations. Agreement between model results and experimental data is good. © 1997 by NACE International.

World University root node

Electrical Engineering

NACE - International Corrosion Conference Series

Development of a mechanistic model for predicting corrosion rate in multiphase oil/water/gas flows

This paper presents a comparative performance evaluation of SVS auxiliary controllers for transient stability enhancement of long transmission lines. A new controller, namely, the Combined Reactive Power and Frequency (C.R.P.F.) auxiliary controller, has been developed for the SVS located at the middle of the transmission line. The application of auxiliary controllers considerably enhances the system damping, and unstable system modes are stabilized. A digital computer simulation study has been performed to illustrate the comparative effectiveness of the auxiliary controllers under disturbance conditions, and the superiority of the C.R.P.F. auxiliary controller has been established.

International Journal of Power and Energy Systems

Application of SVS auxiliary controllers for power system control

In this paper we present a truly practical and provably optimal O(n log h) time output-sensitive algorithm for the planar convex hull problem. The basic algorithm is similar to the algorithm presented by Chan, Snoeyink, and Yap (in "Proceedings of the 6th ACM-SIAM Symposium on Discrete Algorithms, 1995," pp. 282-291), where the median-finding step is replaced by an approximate median. We analyze two such schemes and show that for both methods, the algorithm runs in expected O(n log h) time. We further show that the probability of deviation from expected running time approaches 0 rapidly with increasing values of n and h for any input. Our experiments suggest that this algorithm is a practical alternative to the worst-case O(n log n) algorithms such as Graham's and is especially faster for small output-sizes. Our approach bears some resemblance to a recent algorithm of Wenger (Algorithmica, to appear), but our analysis is substantially different. © 1997 Academic Press.

Journal of Algorithms

On a Simple, Practical, Optimal, Output-Sensitive Randomized Planar Convex Hull Algorithm

In this paper we focus on the problem of designing very fast parallel algorithms for the planar convex hull problem that achieve the optimal O(n log H) work-bound for input size n and output size H. Our algorithms are designed for the arbitrary CRCW PRAM model. We first describe a very simple O(log n log H) time optimal deterministic algorithm for the planar hulls which is an improvement over the previously known Ω(log2 n) time algorithm for small outputs. For larger values of H, we can achieve a running time of O(log n log log n) steps with optimal work. We also present a fast randomized algorithm that runs in expected time O(log H · log log n) and does optimal O(n log H) work. For log H = Ω(log log n), we can achieve the optimal running time of O(log H) while simultaneously keeping the work optimal. When log H is o(log n), our results improve upon the previously best known Θ(log n) expected time randomized algorithm of Ghouse and Goodrich. The randomized algorithms do not assume any input distribution and the running times hold with high probability. © 1997 Elsevier Science B.V.

Computational Geometry: Theory and Applications

Optimal, output-sensitive algorithms for constructing planar hulls in parallel

A model has been developed that can predict the corrosion rate in horizontal slug flows. The effect of the slug frequency and oil type on corrosion rate have been included. The model has been compared to experimental data, and, to the model and field data of Gunaltun (1996). For all conditions, the corrosion rate increased with increase in slug frequency until a maximum in corrosion rate is reached at approximately 35 slugs/minute. At 60 C, the model compares well with that of Gunaltun (1996) if a slug frequency of 10 to 12 is used. For SO C, the Gunaltun model is in good agreement if a frequency of 1 slug/minute is used. His model does include a term that predicts a maximum in the corrosion rate between 60 and 80 C. This has not been noticed in this laboratory for slug flows. For horizontal pipelines, field data suggests that, the slug frequency is usually in the range of 1 to 20 slugs/minute, depending on the liquid velocity. When the pipe is inclined, the slug frequency can increase to values much greater than these and this may lead to higher levels of corrosion. The oil type is accounted for using the suggestion of Efird (1989) based on the product of oil acid number and % nitrogen. When this relation is used, the results compare very well with those of Efird for the oils he studied. © 1997 by NACE International.

Model for sweet corrosion in horizontal multiphase slug flow

We present lower bounds on the number of rounds required to solve a decision problem in the parallel algebraic decision tree model. More specifically, we show that any parallel algorithm in the fixed degree algebraic decision tree model that answers membership queries in W ⊆ Rn using p processors, requires Ω(log |W|/n log(p/n)) rounds where |W| is the number of connected components of W. This implies non-trivial lower bounds for parallel algorithms that use a superlinear number of processors, namely, that the speed-up obtainable in such cases is not proportional to the number of processors. We further prove a similar result for the average case complexity. We give applications of this result to various fundamental problems in computational geometry like convex-hull construction and trapezoidal decomposition and also present algorithms with matching upper bounds. The algorithms extend Reif and Sen's work in parallel computational geometry to the sublogarithmic time range based on recent progress in padded-sorting. A corollary of our result strengthens the known lower-bound of parallel sorting from the parallel comparison tree model to the more powerful bounded-degree decision tree.

Theoretical Computer Science

Lower bounds for parallel algebraic decision trees, parallel complexity of convex hulls and related problems

DOW's Fire and Explosion Index Hazard Classification Guide (DOW Index) has become a standard document in many countries, including several developing countries. The DOW Index, first issued by the DOW Chemical Co. in 1964, was based upon the Factory Mutual's Chemical Occupancy Classification Guide. The latter as well as subsequent revisions of the DOW Index are based primarily on the experiences in the U.S. which, while applicable to other developed countries, will not be applicable to developing countries in toto, due to differences in costs, training, attitudes and regulations. (This is not intended as a criticism of the situation in the developing countries, since many of the developed countries had themselves passed through similar situations in previous years.) Application of the DOW Index as is, therefore, results in lower than realistic values, thus giving a false sense of security than is actually warranted by the situation in hand. Suggested below are changes that should be and can easily be made to take into account the ground realities. Mentioned in separate sections are also a few comments on the recently issued DOW Index, 7th edition, to make it more internationally user-friendly, as well as suggestions to include some more items to make the DOW Index more dynamic. DOW's Fire and Explosion Index Hazard Classification Guide (DOW Index) has become a standard document in many countries, including several developing countries. The DOW Index, first issued by the DOW Chemical Co. in 1964, was based upon the Factory Mutual's Chemical Occupancy Classification Guide. The latter as well as subsequent revisions of the DOW Index are based primarily on the experiences in the U.S. which, while applicable to other developed countries, will not be applicable to developing countries in toto, due to differences in costs, training, attitudes and regulations. (This is not intended as a criticism of the situation in the developing countries, since many of the developed countries had themselves passed through similar situations in previous years.) Application of the DOW Index as is, therefore, results in lower than realistic values, thus giving a false sense of security than is actually warranted by the situation in hand. Suggested below are changes that should be and can easily be made to take into account the ground realities. Mentioned in separate sections are also a few comments on the recently issued DOW Index, 7th edition, to make it more internationally user-friendly, as well as suggestions to include some more items to make the DOW Index more dynamic.

Chemical Engineering

Journal of Loss Prevention in the Process Industries

Application of DOW's fire and explosion index hazard classification guide to process plants in the developing countries

Owing to frequent black-outs world wide, voltage collapse has received much attention in the electric utilities industry. This paper presents an artificial neural network based method for on-line voltage collapse margin estimation. Homotopy Continuation based Newton-Raphson method is used to drive system operating point to knee of nose curve. The distance of operating point from critical point, measured in terms of system loading may be regarded as margin to voltage collapse. This paper utilizes Kohonen classifier to estimate margin so that computational efforts are reduced compared to conventional methods. Also there is ample of saving in training time compared to error back propagation for parameter estimation. Kohonen neural network classifier transforms input patterns into neurons on the 2-dimensional grid. Power system conditions are assigned to neurons on the grid based on self-organized feature mapping. Finally these neurons are allocated voltage collapse margins corresponding to their system conditions representation. The effectiveness of the proposed technique is tested on sample systems. © 1997 Taylor & Francis.

Electric Machines and Power Systems

Kohonen neural network classifier for voltage collapse margin estimation

With the growing stress on today's power system, it is operated much closer to its stability limit. Under such circumstances it is highly desirable that one must be able to assess the security and stability of the electric power system when exposed to disturbances/faults. In the post-fault transient analysis of interconnected systems, the transient energy margin which is a complex function of prefault system conditions, structure of fault (type and location) and network topology at the specified fault clearing time gives a quantitative idea about the stability of the system. High adaptation capabilities of artificial neural networks make them capable of synthesizing the complex mapping that transform the input features in to a single-valued space of energy margin. Appropriate input feature selection has a direct bearing on the consistency and accuracy of mapping. This issue has been addressed in the present paper by comparing the prediction results based on approaches (Sobajic and Pao, 1989), (Sobajic and Pao 1992), (Jeyasurya, 1993) in the time domain, energy domain and its corresponding time domain calibration. Subsequent to the above comparison, the much haunting question of whether to train the network in the energy or time domain has been answered satisfactorily. It has been observed that fault clearing time is a key parameter that anticipates the success of possible calibration of energy margin results into the time domain. Test cases for prediction have been collected from many different operating conditions in power systems. Multilayer perceptron model with ADAPTIVE LEARNING ALGORITHM is used to carry out the present studies. © 1997 Taylor & Francis.

Input-features based comparative study of intelligent transient stability assessment

In the present work, an artificial neural network technique is applied to determine the distance from system operating point to voltage collapse. The distance is measured in terms of the existing loading/generating scenario. The critical/bifurcation point is approached in steps while moving along the nose curve. Generation resheduling is done at each step to ensure economical operation of the system. The homotopy continuation-based Newton Raphson load flow method takes care of numerical instabilities associated with the singularity of Jacobian while approaching critical point. Proximity to critical loading of present operating point is a complex function of operating point attributes and loading/generating pattern followed to approach voltage collapse. The high adaptation capabilities of artificial neural networks make it feasible to synthesize the function that maps system state attributes (bus power injections and tap settings of transformers) to distance from, voltage collapse for uniform dispatch strategy. A three-layer (one hidden layer) feed-forward artificial neural network (ANN) is trained to predict the nearness of current operating point to voltage collapse in terms of existing loading condition. It has been demonstrated here that the predicted values are well in tune with their actual ones. The technique is tested on a Ward-Hale 6-bus system and an IEEE 14-bus system.