Program Goal 1: The graduates will acquire contemporary knowledge and concepts of mechanical equipment design.
Program Learning Outcome 1a: Understanding of conventional and advanced stress analysis.
Program Learning Outcome 1b: Analytical ability to critically evaluate the design performance of various basic and advanced mechanical equipment designs.
Program Goal 2: The graduates will possess the concepts of materials deformation and their mechanisms.
Program Learning Outcome 2a: Understanding the dynamics of mechanical components.
Program Learning Outcome 2b: Identify and critically evaluate the dynamic performance of mechanical equipment.
Program Goal 3: The graduates will possess state-of-the-art in the knowledge of advanced materials failure and failure theories.
Program Learning Outcome 3: Ability to analyse the cause and prevention of failure in mechanical components
Program Goal 4: The graduates will possess the state-of-the-art practical and numerical approach for the analysis of mechanical design.
Program Learning Outcome 4a: Acquire the appropriate engineering skill and knowledge pertaining to design processes.
Program Learning Outcome 4b: Possess the computational and analytical expertise required for the analysis of mechanical equipment design.
Post Processing Techniques: Viewing of results, Average and unaverage stress, Interpretation of results.
Limitations with FEM: Introduction of Meshfree Methods, XFEM (eXtended Finite Element Method), Phase Field Modelling, Applications of advanced methods.
5.
ME5104
Design Lab - I ▼
0
0
3
1.5
Design Lab - I - Detailed Syllabus
Unit 1: Data Acquisition and Signal Processing
Introduction to Data Acquisition (DAQ) systems and their components (sensors, transducers, signal conditioning, A/D converters).
Feedback motion control of DC motor: Understanding control principles and implementing basic feedback loops.
Introduction to filters: Low pass and high pass filters in signal processing.
Spectrum analysis: Understanding frequency domain representation of signals (FFT).
Unit 2: Fault Detection in Rotating Machinery
Fundamentals of rotating machinery and common faults (unbalance, misalignment, bearing defects, gear faults).
Fault Detection in Rotating Machinery: Theoretical background and experimental methods.
Experimental investigation of Oil whirl-Oil whip in Machine Fault Simulator.
Dynamic Balancing (on MFS) and Field balancing of Rotating machinery.
Unit 3: Electrical Motor Current Signature Analysis and Air Bearing
Electrical motor current signature analysis (MCSA) on Machine Fault Simulator for fault diagnosis.
Study of Air Bearing apparatus: Principles of operation and characteristics.
Experimental investigation of Air Bearing onset whirl.
Unit 4: Vibration and Acoustics Experiments
Experimental investigation of Rider's comfort through Active mass suspension systems.
To determine the frequency response function of a Cantilever Beam using experimental modal analysis techniques.
To measure the sound pressure level of shop floor/machine with different weighting scale (A, B, C) and validation of inverse proportionality law for sound intensity.
Unit 5: Project-Based Learning and Standards
Experimental setup built by students themselves / a precursor to M-Tech. project: Students will undertake a mini-project involving design, fabrication (if applicable), and experimental validation.
Use of standards for experiments: Introduction to relevant industry standards (e.g., ISO for vibration, acoustics) for experimental procedures and reporting.
Safety procedures in laboratory experiments involving dynamic machinery.
6.
ME61XX
DE-I ▼
3
0
0
3
DE-I - Detailed Syllabus
Content for DE-I will vary based on the specific elective chosen by the student. Common topics for Mechanical Engineering electives in a first semester might include advanced topics in:
Unit 1: Advanced Thermodynamics / Heat Transfer
Review of fundamental thermodynamic laws and concepts.
Exergy analysis and its applications.
Combustion thermodynamics.
Advanced heat transfer modes: Conduction, convection, radiation (e.g., transient heat conduction, boiling and condensation).
Heat exchangers design and analysis.
Unit 2: Advanced Fluid Mechanics
Governing equations of fluid flow (Navier-Stokes equations).
Potential flow theory.
Boundary layer theory.
Turbulence modeling (introduction to RANS, LES, DNS).
Material characterization techniques (e.g., SEM, TEM, XRD).
Fracture mechanics and fatigue of materials.
Corrosion and its prevention.
Material selection and design.
7.
ME61XX
DE-II ▼
3
0
0
3
DE-II - Detailed Syllabus
Content for DE-II will also vary based on the specific elective chosen by the student. This might be another advanced topic or a specialized area complementing DE-I.
Unit 1: Computational Fluid Dynamics (CFD)
Introduction to CFD: Governing equations, discretization methods (FDM, FVM).
Gridding and meshing techniques.
Solving incompressible and compressible flows.
Turbulence models in CFD.
CFD software (e.g., ANSYS Fluent, OpenFOAM).
Unit 2: Advanced Thermodynamics / Energy Systems
Advanced power cycles (e.g., combined cycles, cogeneration).
Renewable energy technologies (solar, wind, geothermal).
Energy efficiency and conservation.
Fuel cells and alternative energy sources.
Energy system modeling and analysis.
Unit 3: Vibrations and Acoustics
Multi-degree-of-freedom systems: Equations of motion, natural frequencies, mode shapes.
Vibration control: Passive and active vibration isolation.
Longitudinal, lateral, and vertical vehicle dynamics.
Suspension systems and ride comfort.
Steering systems and handling.
Braking systems and stability.
Unit 5: Smart Manufacturing / Industry 4.0
Introduction to Industry 4.0: Pillars and concepts (Cyber-Physical Systems, IoT, Big Data).
Smart factories and digital twins.
Artificial intelligence and machine learning in manufacturing.
Cloud manufacturing and blockchain in supply chain.
Cybersecurity in industrial systems.
8.
XX61PQ
IDE ▼
3
0
0
3
IDE - Detailed Syllabus
The "IDE" course likely refers to an Interdisciplinary Elective or a general "Integrated Development Environment" course, depending on the program context. Given the ME (Mechanical Engineering) subjects, it's more probable to be an interdisciplinary elective or a course focused on software tools relevant to engineering analysis and design.
Unit 1: Introduction to Engineering Software Environments
Overview of common engineering software: CAD (e.g., SolidWorks, AutoCAD), CAE (e.g., ANSYS, ABAQUS), CAM, MATLAB/Octave, Python for engineering.
Understanding the workflow in engineering design and analysis using integrated tools.
Basic interface navigation and customization of a selected IDE/software.
Unit 2: Programming Fundamentals for Engineers
Introduction to programming logic: Variables, data types, control structures (loops, conditionals), functions.
Basic scripting for automation within engineering software (e.g., MATLAB scripting, Python scripting for CAD/CAE).
Debugging techniques within the IDE/scripting environment.
Unit 3: Data Analysis and Visualization
Importing and exporting engineering data.
Data manipulation and processing using built-in functions or libraries (e.g., NumPy, Pandas in Python).
Creating engineering plots and visualizations: 2D/3D plots, contours, animations.
Statistical analysis of experimental or simulation data.
Unit 4: Simulation and Model Building
Introduction to simulation concepts: Discrete event simulation, continuous simulation.
Building simple engineering models within the IDE (e.g., block diagrams in Simulink, basic FEA models).
Running simulations and interpreting results.
Parameter studies and optimization using integrated tools.
Unit 5: Project-Based Application and Collaboration
Applying the learned tools to solve a small interdisciplinary engineering problem.
Version control systems (e.g., Git) for collaborative project development.
Documentation practices for engineering projects and code.
Presentation of project results using visual aids and technical reports.
Different discretization techniques: Upwind, central differencing, higher-order schemes.
Processor (Solver):
Solution schemes: Implicit, explicit.
Different solvers: Pressure-based, density-based.
Post-processing:
Analysis of results: Contour plots, vector plots, streamlines.
Validation against experimental data or analytical solutions.
Grid independent studies: Ensuring solution accuracy with mesh refinement.
Developing codes/using commercial/open-source software (e.g., ANSYS Fluent, OpenFOAM) for solving problems of laminar and turbulent flow with heat transfer applications.
Unit 4: Laboratory Assignments and Software Usage
Hands-on laboratory assignments using engineering software related to:
FEM software (e.g., ANSYS, ABAQUS, Nastran): For structural, thermal, and vibration analysis.
CFD software (e.g., ANSYS Fluent, OpenFOAM): For fluid flow and heat transfer simulations.
Working with both Graphical User Interface (GUI) and script-like languages (e.g., APDL in ANSYS, Python scripting in Abaqus) for automation and advanced analysis.
Troubleshooting common issues in simulations.
Interpretation and presentation of simulation results.
2.
ME5202
Advanced Dynamics & Vibration ▼
3
1
0
4
Advanced Dynamics and Vibration - Detailed Syllabus
Unit 1: Rigid Body Dynamics - Kinematics and Kinetics
Review of Newtonian mechanics for rigid bodies: Translation, rotation, general plane motion.
Review of system of rigid bodies: Degrees of freedom, constraints.
Coordinate transformation between two sets of axes in relative motion.
Euler angles: Definition, sequence, and representation of rigid body orientation.
Angular velocity, angular acceleration, angular momentum etc. in terms of Euler angle parameters.
Newton-Euler equations of motion for rigid bodies: Derivation and application.
Unit 2: Analytical Dynamics - Lagrangian Mechanics
Elementary Lagrangian mechanics: Advantages over Newtonian approach.
Generalized coordinates: Definition, selection for multi-body systems.
Constraints: Holonomic and non-holonomic, scleronomic and rheonomic.
Principle of virtual work: For static and dynamic systems.
Hamilton’s principle: Derivation and application.
Lagrange’s equation of motion: Derivation, application to various mechanical systems.
Generalized forces: Definition and calculation.
Lagrange’s equation with constraints: Using Lagrange’s multiplier method.
Introduction to nonlinear effects in Dynamics: Geometric and material non-linearity (conceptual).
Unit 3: Vibration of Discrete Systems
Review of single Degree of Freedom (DOF) systems: Free vibration (undamped and damped), forced vibration (harmonic excitation, impulse response, general forcing).
Simple Multi-DOF lumped parameter systems:
Equations of motion: Matrix formulation.
Eigenvalue problem: Natural frequencies and mode shapes.
Modal analysis for multi-DOF systems: Decoupling of equations.
Forced vibration response using modal superposition.
Unit 4: Vibration of Distributed Parameter Systems
Equations of motion for free and forced vibration of continuous systems:
Axial vibration of a bar: Wave equation derivation, boundary conditions, natural frequencies.
Transverse vibration of a string: Wave equation, boundary conditions, natural frequencies and mode shapes.
Torsional vibration of a shaft: Torsional wave equation, boundary conditions, natural frequencies.
Transverse vibration of beams: Euler-Bernoulli beam theory, boundary conditions, natural frequencies and mode shapes.
Boundary-value problem and boundary conditions for continuous systems.
Differential eigenvalue problem, eigen-function and natural modes for continuous systems.
Orthogonality of eigen-functions and expansion theorem.
Rayleigh quotient: For estimating natural frequencies.
Response to initial conditions and external excitations for continuous systems.
Unit 5: Discretization and Advanced Topics
Discretization of distributed parameter system: Lumped mass method, consistent mass matrix, finite element method (basic concept).
Algebraic eigenvalue problem: Solving for eigenvalues and eigenvectors from discretized systems.
Introduction to Modal analysis for both discrete and continuous systems.
Vibration control strategies: Passive and active control (brief introduction).
Introduction to random vibrations (conceptual).
3.
ME5203
Measurement and Instrumentation ▼
3
0
0
3
Measurement and Instrumentation - Detailed Syllabus
Module 1: Basic Concepts of Measurement Systems
Basic concepts of measurement: Definition, purpose, units, standards.
Generalized configuration of measuring systems: Input, transducer, signal conditioner, indicator/recorder.
Functional elements of instruments.
Classification of measuring instruments: Active vs. passive, analog vs. digital, indicating vs. recording.
Methods of correction for interfering and modifying inputs.
Visualizing mode shapes (e.g., using Chladni patterns).
Unit 4: Crack Detection and Microstructural Examination
Detection of location and size of the crack in a cracked beam using deflection measurement method:
Understanding how cracks affect beam stiffness and deflection.
Using deflection measurements to infer crack presence and severity.
Scanning Electron Microscopy (SEM) examination of fracture surfaces of specimens fractured in experiment:
Introduction to SEM principles and operation.
Analysis of fracture features (e.g., dimples for ductile fracture, cleavage facets for brittle fracture, fatigue striations).
Correlation of microstructure with mechanical properties.
Use of relevant standards for experiments (e.g., ASTM standards for fracture and fatigue testing).
5.
ME62XX
DE-III
3
0
0
3
6.
ME62XX
DE-IV
3
0
0
3
7.
RM6201
Research Methodology
3
1
0
4
8.
IK6201
IKS
3
0
0
3
TOTAL
19
2
7
24.5
Sl. No.
Subject Code
SEMESTER III
L
T
P
C
1.
ME6198
Summer Internship/ Mini Project ▼
0
0
12
3
2.
ME6199
Project I ▼
0
0
30
15
TOTAL
0
0
42
18
Sl. No.
Subject Code
SEMESTER IV
L
T
P
C
1.
ME6299
Project II ▼
0
0
42
21
TOTAL
0
0
42
21
Sl. No.
Subject Code
Subject
L
T
P
C
ME6113
Soft Computing Application in Engineering ▼
3
0
0
3
Soft Computing Application in Engineering - Detailed Syllabus
Chapter-I: Fuzzy Modeling
Fuzzy Sets:
Basic Definition and Terminology: Fuzzy sets vs. crisp sets, membership functions, support, core, crossover points, fuzzy singleton.
Set-theoretic Operations: Union, intersection, complement of fuzzy sets.
Member Function Formulation and Parameterization: Different shapes (triangular, trapezoidal, Gaussian, S-function, Pi-function), methods for constructing membership functions.
Fuzzy Rules and Fuzzy Reasoning:
Fuzzy If-Then Rules: Antecedent and consequent.
Fuzzy Inference Systems (FIS):
Mamdani Fuzzy Model: Fuzzification, rule evaluation, aggregation, defuzzification methods (centroid, bisector, mean of max).
Sugeno Fuzzy Model: Rule consequent as linear or constant function, advantages.
Tsukamoto Fuzzy Models (brief overview).
Input Space Partitioning and Fuzzy Modeling: Grid partitioning, fuzzy C-means clustering for rule generation.
Applications of Fuzzy Logic: Control systems (e.g., washing machines, air conditioners), decision making, pattern recognition.
Digital Twin: Fundamental concepts, architecture, benefits, applications in manufacturing (e.g., predictive maintenance, process optimization, product design).
Cybersecurity in Industry 4.0: Threats, vulnerabilities, security measures for industrial control systems.
Case studies and practical implementation issues for cyber physical systems development.
Programming and simulation exercises for digital manufacturing concepts.
Industry 4.0 readiness assessment and implementation strategies.
4.
ME6108
Wear & Lubrication of Machine Components ▼
3
0
0
3
Wear & Lubrication of Machine Components - Detailed Syllabus
Unit 1: Introduction to Tribology and Surface Mechanics
Definition of Tribology: Study of friction, wear, and lubrication.
Significance for Maintenance and Reliability of Machines: Economic and performance aspects.
Short crack growth: Deviations from LEFM, microstructural effects.
Concept of Kth (threshold stress intensity factor).
Unit 5: Creep, High Rate Deformation, and Sheet Metal Forming
Creep: Stages of creep (primary, secondary, tertiary), creep mechanisms.
Creep crack growth: Phenomenon and measurement.
Stress relaxation tests.
Creep-fatigue interaction: Combined effects.
High Rate Deformation:
Strain rate sensitivity: Effect on flow stress and ductility.
Crash testing: Experimental setups and data analysis.
Crashworthiness of engineering components: Design considerations for energy absorption.
Sheet Metal Forming:
Concept of planar anisotropy and texture: R-value.
Forming limit diagram (FLD): Construction and interpretation for formability assessment.
Wrinkling limit, fracture limit curve.
Hole expansion ratio.
Bauschinger effect: Yield strength anisotropy after reverse loading.
Spring back: Elastic recovery after forming.
r-ratio and deep drawing ratio.
TOTAL
12
0
0
12
Sl. No.
Subject Code
Subject
L
T
P
C
1.
ME6207
Rotor Dynamics ▼
3
0
0
3
Rotor Dynamics - Detailed Syllabus
Unit 1: Fundamentals of Rotor-Bearing Systems
Rotor-Bearing Interaction: Introduction to the dynamic interplay between rotating shafts and their support systems.
Flexural Vibration of Rotors: Understanding bending vibrations in rotating machinery.
Critical Speeds of Shafts: Definition, calculation, and significance.
Jeffcott Rotor Model: A simplified single-disk rotor model for understanding critical speeds and unbalance response.
Unbalance Response: Dynamic behavior of a rotor due to mass unbalance.
Effect of Damping: Influence of various damping mechanisms on rotor vibration.
Campbell Diagram: Plotting natural frequencies as a function of spin speed for identifying critical speeds and resonances.
Effects of Anisotropic Bearings: Impact of directional stiffness/damping on rotor dynamics.
Unbalanced Response of an Asymmetric Shaft: Dynamics of rotors with non-uniform stiffness properties.
Parametric Excitation: Vibration induced by time-varying system parameters.
Unit 2: Gyroscopic Effects and Advanced Rotor Models
Gyroscopic Effects: Influence of gyroscopic moments on rotor behavior, especially during precession.
Rotor with Non-central Disc: Dynamics of rotors with off-center disks.
Rigid-rotor of Flexible Bearings: Modeling and analysis of rigid rotors supported by flexible bearings.
Stodola Model: An early model for rotor stability analysis.
Effect of Spin Speed on Natural Frequency.
Forward and Backward Whirling Motion: Understanding the types of precession in rotors.
Aerodynamic Effects: Influence of fluid forces on rotor dynamics (e.g., in turbomachinery).
Instability:
Rub: Instability caused by contact between rotor and stator.
Tangential forces: Forces leading to self-excited vibrations.
Oil-Whirl/Oil-Whip (covered in Unit 4).
Unit 3: Continuous and Discrete Rotor Models
Rotor-shaft Continuum: Treating the shaft as a continuous system.
Effect of Rotary Inertia and Shear-Deformation within the Shaft: Incorporating Timoshenko beam theory effects.
Equivalent Discrete System: Discretization techniques for complex rotor systems.
Finite Element model for Flexural Vibration: Applying FEM to analyze rotor bending vibrations.
Torsional Vibration: Analysis of twisting vibrations in shafts.
Geared and Branched Systems: Dynamics of interconnected rotating systems.
Transfer Matrix Model: A method for analyzing multi-span and multi-disk rotor systems.
Unit 4: Bearings, Balancing, and Diagnostics
Fluid Film Bearings:
Steady State Characteristics of Bearings: Load capacity, stiffness, damping coefficients.
Reynolds’s Equation: Governing equation for fluid film lubrication.
Oil-Whirl/Oil-Whip: Instability phenomena in fluid film bearings.
Rigid and Flexible Rotor Balancing: Techniques for reducing unbalance.
Active Vibration Control of Rotor-Bearing System:
Active Magnetic Bearing (AMB): Principles and applications.
Other active control strategies.
Condition Monitoring of Rotating Machinery: Techniques for assessing health and predicting failures.
Measurement Techniques: Sensors and instrumentation used in rotor dynamics (e.g., proximity probes, accelerometers).
Rolling element bearings: Characteristics and modeling.
Fault diagnosis in rotating machinery: Identification of common rotor system faults.
2.
ME6208
Robot Motion Planning ▼
3
0
0
3
Robot Motion Planning - Detailed Syllabus
Unit 1: Configuration Space and Topology
Introduction to Configuration Space (C-space): Definition, importance in motion planning.
Topology of C-space:
Homeomorphism and diffeomorphism: Understanding continuous transformations and geometric equivalences.
Differential manifolds: Concepts of smooth spaces relevant to robot kinematics.
Connectedness and compactness of C-space.
Parameterization of SO(3) (Special Orthogonal group 3): Representing orientations in 3D space (e.g., Euler angles, quaternions, rotation matrices) and their implications for motion planning.
Unit 2: Potential Functions for Motion Planning
Potential Field Method: Basic concept of attractive and repulsive forces guiding robot motion.
Additive attractive/repulsive potential functions: Formulation and characteristics.
Distance computation:
Using Brushfire algorithm for computing distances to obstacles.
Other distance metrics.
Local minima problem in potential fields and strategies to overcome it.
Wave-front planner: A grid-based planning algorithm using potential fields.
Navigation potential functions: Designing global potential fields without local minima.
Sphere-space and star-space concepts in path planning.
Potential function in non-Euclidean spaces: Extension to complex C-spaces.
Unit 3: Roadmaps and Graph-based Planning
Roadmap Approaches: Constructing a graph of feasible paths in C-space.
Visibility maps: Connecting configurations that can "see" each other.
Generalized Voronoi Diagram (GVD): Generating paths that maintain maximal clearance from obstacles.
Retract-like Structures: Concepts for creating skeletal representations of C-space.
Canny’s Roadmap algorithm: A classic algorithm for constructing roadmaps.
Opportunistic path planner: Strategies for finding paths efficiently.
Unit 4: Cell Decomposition and Sampling-based Algorithms
Cell Decomposition Methods: Dividing C-space into simple, non-overlapping regions.
Trapezoidal decomposition: A method for decomposing 2D C-spaces.
Morse cell decompositions: Generalization for higher dimensions.
Visibility-based decompositions for Pursuit/Evasion problems.
Sampling-based algorithms: Probabilistic approaches for high-dimensional C-spaces.
Probabilistic Roadmaps (PRM): Constructing a roadmap by sampling random configurations and connecting them.
Learning Objectives: The objective of the course is to train students about the modeling of scalar and multi-objective nonlinear programming problems and various classical and numerical optimization techniques and algorithms to solve these problems.
Course Description: Advanced Optimization Techniques, as a subject for postgraduate and PhD students, provides the knowledge of various models of nonlinear optimization problems and different algorithms to solve such problems with its applications in various problems arising in economics, science and engineering.
Course Content:
Module I: Research Method Fundamentals (6 lecture hours)
Definition, characteristics, and types of research.
Basic research terminology.
An overview of research method concepts.
Research methods vs. method methodology.
Role of information and communication technology (ICT) in research.
Nature and scope of research, information-based decision-making and source of knowledge.
The research process: Basic approaches and terminologies used in research.
Defining research problem and hypotheses framing to prepare a research plan.
Module II: Research Problem Visualization and Conceptualization (5 lecture hours)
Significance of literature survey in identification of a research problem from reliable sources and critical review.
Identifying technical gaps and contemporary challenges from literature review and research databases.
Development of working hypothesis.
Defining and formulating the research problems.
Problem selection, necessity of defining the problem and conceiving the solution approach and methods.
Module III: Research Design and Data Analysis (5 lecture hours)
Research design: Basic principles, need of research design.
Data classification: Primary and secondary data.
Features of good design, important concepts relating to research design.
Observation and facts, validation methods.
Observation and collection of data, methods of data collection.
Sampling methods.
Data processing and analysis.
Hypothesis testing, generalization.
Analysis, reliability, interpretation, and presentation of results.
Module IV: Qualitative and Quantitative Analysis (16 lecture hours)
Qualitative Research Plan and designs.
Meaning and types of Sampling.
Tools of qualitative data collection: observation, depth interview, focus group discussion.
Data editing, processing & categorization.
Qualitative data analysis.
Fundamentals of statistical methods: parametric and nonparametric techniques.
Test of significance, variables, conjecture, hypothesis, measurement, types of data and scales.
Sample and sampling techniques, probability and distributions.
Hypothesis testing, level of significance and confidence interval.
Statistical tests: t-test, ANOVA (Analysis of Variance), correlation, regression analysis.
Error analysis.
Research data analysis and evaluation using software tools (e.g.: MS Excel, SPSS, Statistical, R, etc.).
Module V: Principled Research (10 lecture hours)
Ethics in research and Ethical dilemma.
Affiliation and conflict of interest.
Publishing and sharing research.
Plagiarism and its fallout (case studies).
Internet research ethics, data protection.
Intellectual Property Rights (IPR): patent survey, patentability, patent laws and IPR filing process.
Learning Outcome: On successful completion of the course, students should be able to:
Understand the terminology and basic concepts of various kinds of nonlinear optimization problems.
Develop the understanding about different solution methods to solve nonlinear Programming problems.
Apply and differentiate the need and importance of various algorithms to solve scalar and multi-objective optimization problems.
Employ programming languages like MATLAB/Python to solve nonlinear programming problems.
Model and solve several problems arising in science and engineering as a nonlinear optimization problem.
Assessment Method: Mid Semester Examination (30%), End Semester examination (50%), Class test & quiz (10%), Assignment (10%)
Suggested Readings:
Text Books:
Jordan, D. W. and Smith, P.: Nonlinear Ordinary Differential Equations, 3rd Edition, Clarendon Press, Oxford, 1999 ed.
Nayfeh, A. H. and Mook, D. T.: Nonlinear Oscillations, Wiley Interscience, New York., 1979 ed.
Nayfeh, A. H and Balachandran, B.: Applied Nonlinear Dynamics: Analytical, Computational and Experimental Methods, Wiley, 2008 ed.
Strogatz, S. H.: Nonlinear Dynamics And Chaos: With Applications To Physics, Biology, Chemistry, And Engineering, Westview Press, 2001 ed.
Ogorzalek Maciej J.: Chaos and Complexity in Nonlinear Electronic Circuits, World Scientific Series on Nonlinear Science Series A, 1997 ed.