Farbod Khoshnoud

Energy Regeneration From Suspension Dynamic Modes and Self-Powered Actuation

This paper concerns energy harvesting from vehicle suspension systems. The generated power associated with bounce, pitch, and roll modes of vehicle dynamics is determined through analysis. The potential values of power generation from these three modes are calculated. Next, experiments are carried out using a vehicle with a four jack shaker rig to validate the analytical values of potential power harvest. For the considered vehicle, maximum theoretical power values of 1.1, 0.88, and 0.97 kW are associated with the bounce, pitch, and roll modes, respectively, at 20 Hz excitation frequency and peak-to-peak displacement amplitude of 5 mm at each wheel, as applied by the shaker. The corresponding experimental power values are 0.98, 0.74, and 0.78 kW. An experimental rig is also developed to study the behavior of regenerative actuators in generating electrical power from kinetic energy. This rig represents a quarter-vehicle suspension model where the viscous damper in the shock absorber system is replaced by a regenerative system. The rig is able to demonstrate the actual electrical power that can be harvested using a regenerative system. The concept of self-powered actuation using the harvested energy from suspension is discussed with regard to applications of self-powered vibration control. The effect of suspension energy regeneration on ride comfort and road handling is presented in conjunction with energy harvesting associated with random road excitations.

Energy harvesting from suspension systems using regenerative force actuators

In this paper, harvesting vibration energy from suspension is investigated. Theoretical values for the harvested energy are calculated. Experimental evaluation of the energy is performed using vehicle road simulation facilities. An excitation signal in the frequency range of 0.5 Hz to 20 Hz is applied to the vehicle and the harvested power is calculated. Experimental results give a maximum harvested power of 984.4 W at the highest frequency, which is close to the theoretically computed value of 1,106 W, for each suspension. Application of regenerative force actuators (RFA) is explored for harvesting the vibration energy and controlling vibration. It is shown that the harvested power increases with the value of the actuator constant.

On power and control systems of the multibody advanced airship for transport

The concept of the multibody advanced airship for transport (MAAT) is presented. The power, propulsion and control systems of the airship are discussed. The energy required for operation of this airship is supplied by a solar-fuel cell system. Solar panels and fuel cells provide the power associated with the airship motion and avionics, including the control and propulsion systems. Uncertainties in modelling of the airship and a method of dealing with such uncertainties are addressed in the framework of optimal uncertainty quantification. A complete model of this airship is presented by integrating the power, propulsion and control system. The model provides the base line for the design and analysis of the airship and investigation of its components, i.e., energy, control and propulsion systems.

Bioinspired Psi intelligent control for autonomous vehicles

The term Psi denotes anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms [1]. A variant of Psi is precognition which relates to an event or state not yet experienced. Although Psi phenomenon is unexplained, it is an inspiration for the study in the present work. The representation of prediction of future events for motion control is explained in a framework inspired by Psi precognition. In the current research, motion control of an autonomous vehicle is of interest where the future state of the vehicle in a dynamic environment is predicted using a multi-agent/robot or a swarm configuration approach. This research is aimed to address a problem where an agent in a multi-agent/robot or a swarm configuration can inform the vehicle under investigation about the changes in the dynamic environment before the vehicle itself can experience or sense an event. The corresponding parameters and constraints to solve such problem are discussed. A generalized approach inspired by Psi precognition is proposed and the effect of this technique in the system response is studied.

Stochastic simulation of energy extraction with an optically controlled nano-electromechanical engine

Abstract Stochastic simulation of a nano-electromechanical energy transducer eis presented in this paper. This system acts like an engine with four transformations in each engine cycle that converts potential energy due to quantum vacuum fluctuation of the electromagnetic field, or Casimir effect, to electrical energy. The system is composed eof an oscillating semiconductor boundary parallel to a fixed plate separated by vacuum. The effect of the Casimir pressure in this engine is controlled by changing the optical properties of the boundaries when they are exposed to radiation that alters the plasma frequency of the dielectric medium of the moving and stationary boundaries. eThe stochastic simulation presented in this paper demonstrates the efficiency of the device in achieving positive net energy and deals with uncertainties in modelling such as friction and noise in the system, thereby providing a realistic model. A Langevin equation, which describes the Brownian motion, is considered for stochastic simulation of the system. The work done by the Casimir force is carried out in an open path of energy conversion, and the external radiation input to this system is not a means of input energy; rather, it is a tool to vary the system parameters.

Stochastic Simulation of a Casimir Oscillator

Stochastic simulation of a Casimir Oscillator is presented in this paper. This oscillator is composed of a flat boundary of semiconducting oscillator parallel to a fixed plate separated by vacuum. In this system the oscillating surface is attracted to the fixed plate by the Casimir effect, due to quantum fluctuations in the zero point electromagnetic field. Motion of the oscillating boundary is opposed by a spring. The stored potential energy in the spring is converted into kinetic energy when the spring force exceeds the Casimir force, which generates an oscillatory motion of the moving plate. Casimir Oscillators are used as micro-mechanical switches, sensors and actuators. In the present paper, a stochastic simulation of a Casimir oscillator is presented for the first time. In this simulation, Stochastic Variational Integrators using a Langevin equation, which describes Brownian motion, is considered. Formulations for Symplectic Euler, Constrained Symplectic Euler, Stormer-Verlet and RATTLE integrators are obtained and the Symplectic Euler case is solved numerically. When the moving parts in a micro/nano system travel in the vicinity of 10 nanometers to 1 micrometer range relative to other parts of the system, the Casimir phenomenon is in effect and should be considered in analysis and design of such system. The simulation in this paper considers modeling such uncertainties as friction, effect of surface roughness on the Casimir force, and change in environmental conditions such as ambient temperature. In this manner the paper explores a realistic model of the Casimir Oscillator. Furthermore, the presented study of this system provides a deeper understanding of the nature of the Casimir force.Copyright

Self-Powered Dynamic Systems in the Framework of Optimal Uncertainty Quantification

The energy that is needed for operating a self-powered device is provided by the energy excess in the system in the form of kinetic energy, or a combination of regenerative and renewable energy. This paper addresses the energy exchange issues pertaining to regenerative and renewable energy in the development of a self-powered dynamic system. A rigorous framework that explores the supply and demand of energy for self-powered systems is developed, which considers uncertainties and optimal bounds, in the context of optimal uncertainty quantification. Examples of regenerative and solar-powered systems are given, and the analysis of self-powered feedback control for developing a fully self-powered dynamic system is discussed.

Self-Powered and Bio-Inspired Dynamic Systems: Research and Education

Animals are products of nature and have evolved over millions of years to perform better in their activities. Engineering research and development can benefit greatly by looking into nature and finding engineering solutions by learning from animals’ evolution and biological systems. Another relevant factor in the present context is highlighted by the statement of the Nobel laureate Richard Smalley: “Energy is the single most important problem facing humanity today.” This paper focuses on how the research and education in the area of Dynamic Systems can be geared towards these two considerations. In particular, recent advances in self-powered dynamic systems and bio-inspired dynamic systems are highlighted. Self-powered dynamic systems benefit by capturing wasted energy in a dynamic system and converting it into useful energy in the mode of a regenerative system, possibly in conjunction with renewable energies. Examples of solar-powered vehicles, regenerative vibration control, and energy harvesting are presented in the paper. Particularly, development of solar-powered quadrotor, octocopter, and tricopter airships are presented, a self-powered vibration control of a mass-spring system using electromagnetic actuators/generators, and piezoelectric flutter energy harvesting using bi-stable material are discussed. As examples of bioinspired dynamic systems, flapping wing flying robots, vertical axis wind turbines inspired by fish schooling, propulsion inspired by jellyfish, and Psi Intelligent Control are given. In particular, various design and developments of bird-inspired and insect-inspired flapping wings with the piezoelectric and electromagnetic actuation mechanisms, a scaled vertical axis wind turbine farm consist of 4 turbines and the corresponding wind tunnel testing, jellyfish-inspired pulsing jet and experimenting the increase in efficiency of energy consumption, and a multi-agent/robotic based predictive control scheme inspired by Psi precognition (event or state not yet experienced). Examples of student projects and research carried out at Brunel University and the experimental rigs built (in all the mentioned areas) are discussed, as an integrated research and educational activity. For the analysis and understanding of the behavior of self-powered and bio-inspired systems, Optimal Uncertainty Quantification (OUQ) is used. OUQ establishes a unified analysis framework in obtaining optimized solutions of the dynamic systems responses, which takes into account uncertainties and incomplete information in the simulation of these systems.

Modal description of vibratory behaviour of structures using fuzzy membership functions

Abstract A method is developed to model vibratory systems in terms of their modal shapes estimated by fuzzy membership functions and updated by use of frequency response functions. In this method, fuzzy output membership functions are introduced based on ‘guessed’ mode shapes of the system. The approximate mode shapes can be estimated as they partially depend on the boundary conditions. The fuzzy membership functions are then updated using experimental modal analysis method which is used in refining ‘fuzzy mode shapes’. The fuzzy mode shapes are then interpolated with respect to geometry and frequency, giving full behaviour description of the system in frequency domain using fuzzy neural network. Although the method proposed is general in this paper, the case study is based on a simple beam. Two inputs of the fuzzy model are sampling positions on the beam and frequency. The natural frequencies of the system were found by experimental tests. Mode shape or deflection of the beam is introduced by zero, medium, large and positive and negative terms. These mode shapes are modified by using the data from experimental modal analysis. The corresponding magnitude in the fuzzy model is updated by magnitudes from mode shapes from modal testing. A fuzzy neural network is used to determine the mode shape curves from the updated mode shapes. This approach compliments modal analysis and enhances it by incorporating it with fuzzy reasoning. In that respect the proposed method offers two distinct benefits, firstly, the use of fuzzy membership functions provides a means of dealing with uncertainty in measured data and, secondly, it give access to a large repertoire of tools available in fuzzy reasoning field. The procedure proposed in this paper is a novel and has not been done before.

Modal Analysis of Systems Using a Neuro-Fuzzy Approach

A novel method of modal analysis for vibration modeling of systems is presented in this paper. In the developed method, first, mode shapes of the structure that is being analyzed are approximated. The approximate mode shapes are expressed by fuzzy sets where approximate deflections or displacement magnitudes of the mode shapes are described by fuzzy linguistic terms such as Zero, Medium, and Large. Fuzzy membership functions provide a means of dealing with the imprecisely defined system and it gives access to a large repertoire of tools available in the field of fuzzy reasoning. Second, fuzzy representations of the approximate mode shapes, called Fuzzy Mode Shapes in this paper, are updated using modal analysis data as obtained through experimentation. Finally, artificial neural networks are used as a tool to obtain an accurate version of the mode shape data by learning the target set of the data. An appropriate analogy of the application of Fuzzy Mode Shapes in the first step is the Starting Mode Shape Vectors in numerical eigenvector problem where the starting vector is updated through an iterative process. In this paper iterative updating process of mode shapes is carried out for the application of experimental modal testing. In this approach the differences between the fuzzy mode shapes and the corresponding measured modal testing data are minimized through an iterative process. In validating the developed technique for vibration modeling of one-dimensional and two-dimensional elastic bodies and structures, modeling of elastic beams, a clamped-free-clamped-free plate and a frame are used as illustrative examples. The solutions of the corresponding simulations are compared with the results from finite element computations and analytical model solutions. The good agreement of the results obtained for these models justifies the application of the developed method in experimental vibration modeling of systems. Use of the fuzzy-neural approach as developed in the paper expands the coverage of experimentally measured data, which is normally limited to a small number of measurement sets due to the limited number of available vibration sensors in the analyzed system. Neural networks provide a satisfactory interpolation of two sets of data including a) modal test data, which is accurate but is normally available only for a few measured points, and b) Fuzzy Mode Shapes, which are available for large number of points but are approximate.© 2010 ASME