3. Book Chapters

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/8

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Accelerating MCMC using model reduction for the estimation of boundary properties within Bayesian framework
(2019) Gnanasekaran N.; Harsha Kumar M.K.
In this work, Artificial Neural Network (ANN) and Approximation Error Model (AEM) are proposed as model reduction methods for the simultaneous estimation of the convective heat transfer coefficient and the heat flux from a mild steel fin subject to natural convection heat transfer. The complete model comprises of a three-dimensional conjugate heat transfer from fin whereas the reduced model is simplified to a pure conduction model. On the other hand, the complete model is then replaced with ANN model that acts as a fast forward model. The modeling error that arises due to reduced model is statistically compensated using Approximation Error Model. The estimation of the unknown parameters is then accomplished using the Bayesian framework with Gaussian prior. The sampling space for both the parameters is successfully explored based on Markov chain Monte Carlo method. In addition, the convergence of the Markov chain is ensured using Metropolis–Hastings algorithm. Simulated measurements are used to demonstrate the proposed concept for proving the robustness; finally, the measured temperatures based on in-house experimental setup are then used in the inverse estimation of the heat flux and the heat transfer coefficient for the purpose of validation. © Springer Nature Singapore Pte Ltd. 2019.
Adapting to climate change: Water management strategy
(2016) Goyal M.K.; Ojha C.S.P.; Surampalli R.Y.; Choudhury A.
Impacts of climate change and climatic variability are evident in many parts of the world and will result in significant effects on water resources. Climate change studies inherently have to consider the significance of natural variability, future emissions, and downscaling methodology. Climate change threatens water management through changes in precipitation patterns and agricultural production through variable temperatures, and increased occurrences of extreme events, such as droughts and floods. This chapter presents a case study of Pichola Lake Basin. The objective of the case study is to assess the impact of climate change on a lake basin in an arid region in India for the various Intergovernmental Panel on Climate Change (IPCC) SRES scenarios. Adaptation and mitigation techniques are of immense importance when tackling and eliminating or reducing the adverse effects of climate change to life and property. © 2016 American Society of Civil Engineers.
Additive Manufacturing of Lattice Structures for Heat Transfer Enhancement in Pipe Flow
(2021) Koneri R.; Mulye S.; Ananthakrishna K.; Hota R.; Khatei B.; Bontha S.
Additive manufacturing has added a new dimension to manufacturing technology. The Design for Additive Manufacturing (DFAM) principles provide guidelines for successful 3D printing. Several industrial applications utilize the cellular structures in AM for design improvement by light weighting, topology optimization, etc. Self-supporting behavior is the most desired characteristic for DFAM of cellular structures. In the present work, gyroid, star kagome and BCC cellular structures are evaluated for self-supporting behavior using Materialize Magics software. The lattice designs of different sizes are 3D printed and visually examined for defects. The lattice designs are introduced into a smooth circular pipe. Conjugate heat transfer analysis is done for different Reynolds numbers (1193–10736) using FloEFD to study heat transfer and pressure drop characteristics. All the lattice designs show heat transfer enhancement and higher pressure drop with respect to smooth pipe. Among all lattice designs, gyroid shows the highest heat transfer enhancement and highest pressure drop. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Analysis of forced convection heat transfer through graded PPI metal foams
(2019) Kotresha B.; Gnanasekaran N.
A forced convection heat transfer through high porosity graded Pores per inch (PPI) metal foam heat exchanger is numerically solved in this paper. The physical domain of the problem consists of a heat exchanger system attached to the bottom of a horizontal channel to absorb heat from the exhaust gas leaving the system. Two different pore densities of the metal foam 20 and 40 along with two different metal foam materials are considered for the enhancement of heat transfer in the present numerical investigation. The metal foam heat exchanger is considered as a homogeneous porous medium and is modeled using Darcy Extended Forchheirmer model. The heat transfer through the metal foam porous media is solved by using local thermal equilibrium (LTE) model. The effect of graded pore density and graded thermal conductivity is investigated and compared with the nongraded PPI metal foam. The heat exchanger system is simulated over a velocity range of 6–30 m/s. The pressure drop decreases for the graded pore density metal foams compared to the higher PPI metal foam and also increases with increase in the fluid inlet velocity. The results of temperature and velocity distribution for the graded and nongraded metal foams are compared and discussed elaborately. © Springer Nature Singapore Pte Ltd. 2019.
Anomalous event detection methodologies for surveillance application: An insight
(2017) Rao T.J.N.; Tahiliani, M.P.; Girish G.N.; Rajan J.
Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system. © 2018 IGI Global. All rights reserved.
Anomalous event detection methodologies for surveillance application: An insight
(2018) Rao T.J.N.; Girish G.N.; Tahiliani, M.P.; Rajan J.
Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system. © 2019 by IGI Global.
Application of Andreassen and Modified Andreassen Model on Cementitious Mixture Design: A Review
(2021) Snehal K.; Das B.B.
Cement is a widely used construction material and its consumption on large-scale causes environmental degradation; thus, more emphasis is being given on industrial by-products as alternative materials to cement for their sustainable usage. It is necessary that varying particle size of supplementary cementitious particles is to be used for filling the voids to form a dense particle-packed concrete. The selection of right combination of material is tedious job by trials involving different replacement materials and the resultant concrete may show unexpected results; thus, a more suitable method is the selection of materials based on optimum packing of particles. To select the optimum size of replacement materials particle packing models are essential, so that a low-cement concrete can be prepared which will be ecological as well as economical with improved density, low porosity and high compressive strength. It is found that there are different models have been developed to achieve optimal packing. However, application of Andreassen and modified Andreassen models for the particle packing of multiple ingredients of cementitious matrix found to be largely being accepted by the researchers. This paper reviews the application of Andreassen and modified Andreassen models for the effective particle packing investigations on cementitious particles. It also reviews the software’s employed for designing various cementitious mixtures based on Andreassen and modified Andreassen models. © 2021, Springer Nature Singapore Pte Ltd.
Application of green’s function to establish a technique in predicting jet impingement convective heat transfer rate from transient temperature measurements
(2019) Parida R.K.; Kadam A.R.; Hindasageri V.; Vasudeva M.
Jet impingement heat transfer has gained attention of the researchers due to its very high rate of convective heat transfer. The objective of this study is to establish an analytical technique to predict the convective heat transfer coefficient and the reference temperature over a surface being impinged. This technique is based on the fundamental mathematical concept of Green’s function. A code in MATLAB is developed to predict both local convective heat transfer coefficient and reference temperature over the impinging surface, which requires the transient temperature data at both faces of the impinging plate as input. Radiation correction is also considered to incorporate radiation losses in high-temperature applications. This code works on the principle of one-dimensional heat transfer across the impinging plate, for known dimensions, thermal diffusivity, and surface emissivity. A numerical simulation of hot jet at Reynolds number equal to 1000, over a cold plate of thickness 10 mm, is carried out for a given set of spatially varying convective heat transfer coefficient and reference temperature values, along the impinging surface. The impinging plate is considered to be orthotropic to ensure one-dimensional heat conduction across the plate thickness. Transient temperature at both the faces for a duration of 10 s with an interval of one second was recorded and used as input to the code to validate the proposed technique. Local heat transfer coefficient and the reference temperature predicted are in good agreement with those input values for numerical analysis using ANSYS, having a maximum deviation of 2 and 10%, respectively. Further, it is observed that estimated values of convective heat flux at a given location on the impinging surface varies linearly with temperature at the same location, which confirms Newton’s law of cooling. © Springer Nature Singapore Pte Ltd. 2019.
Arguing formally about flight control laws using SLDV and NUSMV
(2017) Jeppu N.; Jeppu Y.
Software systems have failed in the recent past. This is most often attributed to wrong requirements often caught very late in the program or escapes from the rigorous process leading to failures. There is a necessity to ensure that the requirements are correct up front before the design and verification process start. Formal methods have become popular these days and a lot of impetus is there in the industry to apply these techniques to safety critical projects especially in flight controls. This paper looks at two tools NuSMV, an open source model checker, and Simulink Design Verifier, a commercial model checker. It is seen that these can be practically applied to projects and design. These are very successful in finding defects in design and requirements as demonstrated on a set of mutants. © Springer Nature Singapore Pte Ltd. 2018. All rights reserved.
Assessment of ferrous slag with relevance to physico-chemical properties
(2020) Anjali M.S.; Poorani M.; Shrihari S.; Sunil B.M.
Blast furnace slag is generated as a by-product in the production of iron. Large quantities of slag are visible in the industrial premises that can have adverse effects on the environment. To mitigate such problems, proper environmental management of slag is of great concern. In this regard, a qualitative and quantitative evaluation of ferrous slags such as crystallinity, surface morphology, and elemental composition were done using X-Ray Diffraction and Field Emission Scanning Electron Microscope with EDS (Energy Dispersive X-Ray Spectrometer), respectively. It is also characterized to determine heavy metals and functional groups using Atomic Absorption Spectroscopy and Fourier Transform Infrared Spectroscopy techniques for various geo-environmental applications. The nonplastic slag material showed 85–92% sand-size particles and 8–15% silt-size particles. The SiO2 and CaO values were found to be high followed by Al2O3, MgO, and other compounds. Since slag performed similarly to sand, it could be used as an alternative source of sand. © Springer Nature Singapore Pte Ltd. 2020.
Asymmetric phase-transfer catalysis as a powerful tool in the synthesis of biologically active chiral complex natural products
(2014) Gururaja G.N.; Waser M.
[No abstract available]
Autonomic computing: A fuzzy control approach towards application development
(2011) Venkatarama H.S.; Chandra Sekaran K.
Autonomic computing (Salehie & Tahvildari, 2005) is a new paradigm to design, develop, deploy, and manage systems by taking inspiration from strategies used by biological systems. An autonomic system has four major characteristics: self-configure, self-heal, self-optimize, and self-protect. The autonomic computing architecture provides a blueprint for developing feedback control loops for self-managing systems. This observation suggests that control theory might provide guidance as to the structure of and requirements for autonomic managers. E-commerce is an area where an Autonomic Computing system could be very effectively deployed. E-commerce has created demand for high quality information technology services, and businesses seek ways to improve the quality of service in a cost-effective way. Properly adjusting tuning parameters for best values is time-consuming and skills-intensive. This chapter describes simulation environments to implement approaches to automate the tuning of MaxClients parameter of Apache web server using fuzzy controllers. These are illustrations of the self-optimizing characteristic of an autonomic computing system. © 2012, IGI Global.
Ball convergence theorem for a fifth-order method in banach spaces
(2019) Argyros I.K.; George S.
We present a local convergence analysis for a fifth-order method in order to approximate a solution of a nonlinear equation in a Banach space. Our sufficient convergence conditions involve only hypotheses on the first Fréchet-derivative of the operator involved. Earlier studies use hypotheses up to the fourth Fréchet-derivative [1]. Hence, the applicability of these methods is expanded under weaker hypotheses and less computational cost for the constants involved in the convergence analysis. Numerical examples are also provided in this study. © 2020 by Nova Science Publishers, Inc. All rights reserved.
Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater
(2019) Kundapura S.; Hegde A.V.; Wazerkar A.V.
Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.
Big data computation model for landslide risk analysis using remote sensing data
(2018) Venkatesan M.; Prabhavathy P.
Effective and efficient strategies to acquire, manage, and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era brings up challenges to traditional data mining algorithms. The processing capacity, architecture, and algorithms of traditional database systems are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences, and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this chapter is to propose a multi-ranking decision tree big data approach to handle complex spatial landslide data. The proposed classifier performance is validated with massive real-time dataset. The results indicate that the classifier exhibits both time efficiency and scalability. © 2018, IGI Global. All rights reserved.
Biomass Estimation Using Synergy of ALOS-PALSAR and Landsat Data in Tropical Forests of Brazil
(2020) Huggannavar V.; Shetty A.
Satellite remote sensing technologies are currently tested and suggested as a tool in REDD+ (MRV, Measurement Reporting, and Verification). SAR (Synthetic Aperture Radar) has got an extensive application in the estimation of biomass due to its all-weather capabilities. L band radar signals penetrate the canopy more efficiently when compared to C band. Scientific biomass study using SAR has not been conducted in Para in spite of extensive field datasets being freely available under CMS (Carbon Monitoring System) project. This study aims in using various polarization combinations like HH + HV, HH − HV, HH + HV/HH − HV and vegetation index such as NDVI from the optical data. ALOS-PALSAR and Landsat 7 data acquired over Paragominas in Brazil, where field samples were collected in the form of transects. Regression analysis was performed using backscatter coefficients and field collected Above Ground Biomass (AGB). Semi-empirical model was developed to model AGB using various polarization combinations and NDVI as predictor variables. Combination gave higher R2 value of 0.657 for biomass prediction. Multiple linear regression using NDVI and HH + HV as variables yielded R2 of 0.73 during calibration and 0.363 during validation. There is future scope to use other vegetation indices such as RVI, EVI, etc., along with increased number of samples, which may yield more robust models with acceptable level of accuracy for practical application. © Springer Nature Singapore Pte Ltd. 2020.
Carbon Nanotubes and Graphene in Energy Storage and Catalysis
(Studium Press LLC, USA, 2013) Rajarao, Ravindra; Bhat, Badekai Ramachandra
Case-based reasoning and some typical applications
(2013) Roy D.P.; Chakraborty B.
Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977-1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R and D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit. © 2014, IGI Global. All rights reserved.
Case-based reasoning and some typical applications
(2016) Roy D.P.; Chakraborty B.
Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977-1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit. © 2016 by IGI Global. All rights reserved.
Chalcones: Possible new materials for third-order nonlinear optics
(2011) Kiran A.J.; Chandrasekharan K.; Kalluraya B.; Shashikala H.D.
'Search for potential materials' for third-order nonlinear optics has been of continuing interest in recent years. In this context, organic molecules are increasingly being recognized as the materials of the future because their molecular nature combined with the versatility of synthetic chemistry can be used to alter and optimize molecular structure to maximize third order nonlinear response. Chalcones have received considerable interest as materials for second-order nonlinear optical applications due to their ability to crystallize in noncentrosymmetric structure and their blue light transmittance. Being charge transfer compounds, chalcones can also possess large thirdorder nonlinearities due to their π-conjugated structure. In this article, we discuss the structure-nonlinear response relationship among a few chalcones and the possibility of using them for third-order applications. A meager or no work, to our knowledge, has been done so far on these molecules in this regard. Z-scan and degenerate four-wave mixing techniques were employed to investigate third-order optical nonlinearities of chalcone derivatives. Some of these molecules possess large χ (3) of magnitude as high as 10-12 esu and exhibit strong optical limiting properties. Possible mechanisms that are responsible for optical limiting property of these molecules have been discussed. . © 2011 by Nova Science Publishers, Inc. All rights reserved.