C. Nataraj

Diagnostics of a Nonlinear Pendulum Using Computational Intelligence

A novel method has been presented in this paper for the diagnostics of nonlinear systems using the features of the nonlinear response and capabilities of computational intelligence. Four features of the phase plane portrait have been extracted and used to characterize the nonlinear response of a nonlinear pendulum. An artificial neural network has been created and trained using the numerical data for the estimation of parameters of a defective nonlinear pendulum setup. The results show that, with appropriately selected features of the nonlinear response, the parameters of the nonlinear system can be estimated with an acceptable accuracy.Copyright

Chaotic and Hyperchaotic Dynamics of Smart Valves System Subject to a Sudden Contraction

In this paper, we focus on determining the safe operational domain of a coupled actuator–valve configuration. The so-called “smart valves” system has increasingly been used in critical applications and missions including municipal piping networks, oil and gas fields, petrochemical plants, and more importantly, the U.S. Navy ships. A comprehensive dynamic analysis is hence needed to be carried out for capturing dangerous behaviors observed repeatedly in practice. Using some powerful tools of nonlinear dynamic analysis including Lyapunov exponents and Poincar e map, a comprehensive stability map is provided in order to determine the safe operational domain of the network in addition to characterizing the responses obtained. Coupled chaotic and hyperchaotic dynamics of two coupled solenoid-actuated butterfly valves are captured by running the network for some critical values through interconnected flow loads affected by the coupled actuators’ variables. The significant effect of an unstable configuration of the valve–actuator on another set is thoroughly investigated to discuss the expected stability issues of a remote set due to others and vice versa. [DOI: 10.1115/1.4033610]

Optimal Design of Solenoid Actuators Driving Butterfly Valves

Smart valves are used in cooling applications and are responsible for regulating and supplying the coolant, which is critical for safe and effective operation of many components on naval and commercial ships. In order to be operated under local power (for various mission-critical reasons) they need to consume as little energy as possible in order to ensure continued operability. This paper focuses on optimized design of a typical system using high fidelity nonlinear dynamic models for all the subsystems with full consideration of stability constraints. A simulated annealing algorithm is applied to explore optimal design using two sets of design variables. The results indicate that substantial amount of energy can be saved by an intelligent design that helps select parameters carefully, but also uses hydrodynamic loads to augment the closing effort. [DOI: 10.1115/1.4024720]

Nonlinear dynamic model-based adaptive control of a solenoid-valve system

In this paper, a nonlinear model-based adaptive control approach is proposed for a solenoid-valve system. The challenge is that solenoids and butterfly valves have uncertainties in multiple parameters in the nonlinear model; various kinds of physical appearance such as size and stroke, dynamic parameters including inertia, damping, and torque coefficients, and operational parameters especially, pipe diameters and flow velocities. These uncertainties are making the system not only difficult to adjust to the environment, but also further complicated to develop the appropriate control approach for meeting the system objectives. The main contribution of this research is the application of adaptive control theory and Lyapunov-type stability approach to design a controller for a dynamic model of the solenoid-valve system in the presence of those uncertainties. The control objectives such as set-point regulation, parameter compensation, and stability are supposed to be simultaneously accomplished. The error signals are first formulated based on the nonlinear dynamicmodels and then the control input is developed using the Lyapunov stability-type analysis to obtain the error bounded while overcoming the uncertainties. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation results are shown to demonstrate good performance of the proposed nonlinear modelbased adaptive approach and to compare the performance of the same solenoid-valve system with a non-adaptive method as well.

A Chaotic Blue Sky Catastrophe of Butterfly Valves Driven by Solenoid Actuators

Chilled water systems used in the industry and on board ships are critical for safe and reliable operation. It is hence important to understand the fundamental physics of these systems. This paper focuses in particular on a critical part of the automation system, namely, actuators and valves that are used in so-called “smart valve” systems. The system is strongly nonlinear, and necessitates a nonlinear dynamic analysis to be able to predict all critical phenomena that affect effective operation and efficient design. The derived mathematical model includes electromagnetics, fluid mechanics, and mechanical dynamics. Nondimensionalization has been carried out in order to reduce the large number of parameters to a few critical independent sets to help carry out a broad parametric analysis. The system stability analysis is then carried out by the aid of the tools from nonlinear dynamic analysis. This reveals that the system is unstable in a certain region of the parameter space. The system is also shown to exhibit crisis and chaotic responses; this is characterized using Lyapunov exponents and power spectra. Knowledge and avoidance of these dangerous regimes is necessary for successful and safe operation.Copyright

Design Optimization of Dynamically Coupled Actuated Butterfly Valves Subject to a Sudden Contraction

In this effort, we present novel nonlinear modeling of two solenoid actuated butterfly valves subject to a sudden contraction and then develop an optimal configuration in the presence of highly coupled nonlinear dynamics. The valves are used in the so-called smart systems employed in a wide range of applications including bioengineering, medicine, and engineering fields. Typically, thousands of the actuated valves operate together to regulate the amount of flow and also to avoid probable catastrophic disasters which have been observed in practice. We focus on minimizing the amount of energy used in the system as one of the most critical design criteria to yield an efficient operation. We optimize the actuation subsystems interacting with the highly nonlinear flow loads in order to minimize the amount of energy consumed. The contribution of this work is the inclusion of coupled nonlinearities of electromechanical valve systems to optimize the actuation units. Stochastic, heuristic, and gradient based algorithms are utilized in seeking the optimal design of two sets. The results indicate that substantial amount of energy can be saved by an intelligent design that helps select parameters carefully and also uses flow torques to augment the closing efforts. [DOI: 10.1115/1.4032215]

Sequential Recurrence Analysis of Experimental Time Series of a Rotor Response with Bearing Outer Race Faults

Rolling elements bearings are one of the most common components used in expensive, high precision and critical machines such as gas turbines, rolling mills and gyroscopes. They can be subjected to various defects which could lead to catastrophic results. This includes inner and outer race defects and hence it is of interest to analyze the system response under such defects. A better understanding of the system performance under such defects can be beneficial when performing system diagnostics and system design. In this paper we are focused on the outer race defect and perform a comparative nonlinear time series analysis of a healthy system and a defective system. We consider various levels of outer race defects. The analysis is based on the recurrence properties of the system in its reconstructed state space. After determining the appropriate time lags through the average mutual information technique and the corresponding embedding dimensions through the false neighbor technique, we perform a sequential analysis of the system by subdividing the time series into bins and investigating the system response through recurrence quantification analysis parameters along with the entropy. This contributes to the enhancement of the science of diagnostics of outer race defects by analyzing the signature of various recurrence quantification analysis parameters as the system goes from a healthy state to a severely defective state.

Design Optimization of Solenoid Actuated Butterfly Valves Dynamically Coupled in Series

In this effort, we present novel nonlinear modeling of two solenoid actuated butterfly valves operating in series and then develop an optimal configuration in the presence of highly coupled nonlinear dynamics. The valves are used in the so-called “Smart Systems” to be employed in a wide range of applications including bioengineering, medicine, and engineering fields. Typically, tens of the actuated valves are instantaneously operating to regulate the amount of flow and also to avoid probable catastrophic disasters which have been observed in the practice. We focus on minimizing the amount of energy used in the system as one of the most critical design criteria to yield an efficient operation. We optimize the actuation subsystems interacting with the highly nonlinear flow loads in order to minimize a lumped amount of energy consumed. The contribution of this work is to include coupled nonlinearities of electromechanical valve systems to optimize the actuation units. Stochastic, heuristic, and gradient based algorithms are utilized in seeking the optimal design of two sets. The results indicate that substantial amount of energy can be saved by an intelligent design that helps select parameters carefully but also uses flow torques to augment the closing efforts.Copyright

Preliminary Diagnostics of Dynamic Systems From Time Series

The state space reconstruction technique was recognized by Edward N. Lorenz as “one of the most surprising developments in nonlinear dynamics” [1]. Nowadays, the technique is applied in various scientific areas for prediction, analysis and diagnostics. This paper aims to discuss the possibility of using the embedding dimension of a reconstructed state space of time series as a tool for preliminary diagnostics. After a short description and illustration of the method, the paper considers two case studies: a single degree of freedom (DOF) and a 2 DOF system. The results of the analysis help detect a class of structural defects, including defects connected to a coupling mechanism. There is clearly a huge potential of such an approach for diagnostics of complex machinery.Copyright

Nonlinear Model-Based Adaptive Control of a Solenoid-Valve System

In this paper, a model-based control algorithm is developed for a solenoid-valve system. Solenoids and butterfly valves have uncertainties in multiple parameters in the model, which make the system difficult to adjust to the environment. These are further complicated by combining the solenoid and butterfly dynamic models. The control objective of a solenoid-valve system is to position the angle of the butterfly valve through the electric-driven actuator in spite of the complexity presented by uncertainties. The novelty of the controller design is that the current source of the solenoid valve from the model of the electromagnetic force is substituted for the control input in order to reach the set-point of the butterfly disk based on the error signals, overcoming the uncertainties represented by lumped parameters groups, and a stable controller is designed via the Lyapunov-based approach for the stability of the system and obtaining the control objective. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation is shown to demonstrate good performance of the proposed approach.Copyright