Design Optimization of Solenoid Actuated Butterfly Valves Dynamically Coupled in Series

2015 ◽  
Author(s):  
Peiman Naseradinmousavi ◽  
C. Nataraj
Keyword(s):  
Intelligent Design ◽  
Nonlinear Modeling ◽  
Nonlinear Flow ◽  
Efficient Operation ◽  
Smart Systems ◽  
Critical Design ◽  
Highly Nonlinear ◽  
Wide Range ◽  
In Series ◽  
Gradient Based

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.

Optimal Design of Solenoid Actuated Butterfly Valves Dynamically Coupled in Series

2015 ◽  
Author(s):  
Peiman Naseradinmousavi
Keyword(s):  
Optimal Design ◽  
Nonlinear Modeling ◽  
Nonlinear Flow ◽  
Efficient Operation ◽  
Smart Systems ◽  
Critical Design ◽  
Highly Nonlinear ◽  
Wide Range ◽  
In Series ◽  
Gradient Based

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” 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 configurations of solenoid actuated valves. 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.

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

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Peiman Naseradinmousavi ◽  
Miroslav Krstić ◽  
C. Nataraj
Keyword(s):  
Intelligent Design ◽  
Nonlinear Modeling ◽  
Nonlinear Flow ◽  
Efficient Operation ◽  
Smart Systems ◽  
Critical Design ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Sudden Contraction ◽  
Gradient Based

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.

A 3-D Unsteady Hydrodynamics Design Methodology for Underwater Vehicles and Appendages

2014 ◽  
Author(s):  
Jason D. Geder ◽  
Ravi Ramamurti ◽  
William C. Sandberg ◽  
John Palmisano ◽  
Marius Pruessner ◽  
...  
Keyword(s):  
Proper Time ◽  
Cost Effective ◽  
Underwater Vehicles ◽  
Computational Method ◽  
Nonlinear Flow ◽  
Hazardous Environments ◽  
Starting Point ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Taylor Series Expansions

Underwater vehicles, and particularly unmanned underwater vehicles (UUVs), are becoming increasingly identified as safe and cost-effective solutions for a wide range of applications including environmental monitoring, facilities and vessel inspection, and exploration, often in hazardous environments or conditions. To address the growing need for UUV operations in hazardous environments, designs of unconventional, mission-specific platforms are being investigated in larger numbers. Operational performance requirements, such as precise hovering and very low speed maneuvering in currents and under waves for in-shore operations, demand a vehicle capable of rapid response to a changing environment by production of the proper time-varying balance of lift and thrust. In these highly dynamic flow fields, standard steady drag and moment curves are not valid models. Also, standard semi-empirical methods for coefficient estimation based on linearized Taylor Series expansions are not available for these systems, as neither full-scale nor model-scale performance data exist, except possibly in the case of torpedo-like hulls. This lack of existing data and modeling capability for unconventional systems led us to pursue the development of a high-fidelity direct computational method as a starting point to inform the designs and estimate performance in a highly nonlinear flow regime.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

scholarly journals Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

2021 ◽  
Author(s):  
Michael D. T. McDonnell ◽  
Daniel Arnaldo ◽  
Etienne Pelletier ◽  
James A. Grant-Jacob ◽  
Matthew Praeger ◽  
...  
Keyword(s):  
Short Pulse ◽  
Surface Texturing ◽  
Laser Surface Texturing ◽  
Laser Machining ◽  
Laser Materials ◽  
Parameter Optimisation ◽  
Short Pulse Laser ◽  
Alternative Approach ◽  
Highly Nonlinear ◽  
Wide Range

AbstractInteractions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

scholarly journals Assessment of Linearization Approaches for Multibody Dynamics Formulations

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Francisco González ◽  
Pierangelo Masarati ◽  
Javier Cuadrado ◽  
Miguel A. Naya
Keyword(s):  
Mechanical System ◽  
Multibody Dynamics ◽  
Equations Of Motion ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Practical Applications ◽  
Linearization Methods ◽  
Highly Nonlinear ◽  
Wide Range ◽  
The Way

Formulating the dynamics equations of a mechanical system following a multibody dynamics approach often leads to a set of highly nonlinear differential-algebraic equations (DAEs). While this form of the equations of motion is suitable for a wide range of practical applications, in some cases it is necessary to have access to the linearized system dynamics. This is the case when stability and modal analyses are to be carried out; the definition of plant and system models for certain control algorithms and state estimators also requires a linear expression of the dynamics. A number of methods for the linearization of multibody dynamics can be found in the literature. They differ in both the approach that they follow to handle the equations of motion and the way in which they deliver their results, which in turn are determined by the selection of the generalized coordinates used to describe the mechanical system. This selection is closely related to the way in which the kinematic constraints of the system are treated. Three major approaches can be distinguished and used to categorize most of the linearization methods published so far. In this work, we demonstrate the properties of each approach in the linearization of systems in static equilibrium, illustrating them with the study of two representative examples.

Numerical Simulation of Droplet Generation in Series Co-Flow Capillaries

2009 ◽  
Author(s):  
Yanxi Song ◽  
Jinliang Xu
Keyword(s):  
Reynolds Number ◽  
Disperse Phase ◽  
Weber Number ◽  
Three Dimensional ◽  
Continuous Phase ◽  
Disperse Flow ◽  
Double Emulsions ◽  
Wide Range ◽  
In Series ◽  
Dispersed Phases

We study the production and motion of monodisperse double emulsions in microfluidics comprising series co-flow capillaries. Both two and three dimensional simulations are performed. Flow was determined by dimensionless parameters, i.e., Reynolds number and Weber number of continuous and dispersed phases. The co-flow generated droplets are sensitive to the Reynolds number and Weber number of the continuous phase, but insensitive to those of the disperse phase. Because the inner and outer drops are generate by separate co-flow processes, sizes of both inner and outer drops can be controlled by adjusting Re and We for the continuous phase. Meanwhile, the disperse phase has little effect on drop size, thus a desirable generation frequency of inner drop can be reached by merely adjusting flow rate of the inner fluid, leading to desirable number of inner drops encapsulated by the outer drop. Thus highly monodisperse double emulsions are obtained. It was found that only in dripping mode can droplet be of high mono-dispersity. Flow begins to transit from dripping regime to jetting regime when the Re number is decreased or Weber number is increased. To ensure that all the droplets are produced over a wide range of running parameters, tiny tapered tip outlet for the disperse flow should be applied. Smaller the tapered tip, wider range for Re and we can apply.

Small Screen, Big Feels

2020 ◽  
Author(s):  
Melissa Ames
Keyword(s):  
United States ◽  
The United States ◽  
Presidential Debates ◽  
Audience Studies ◽  
The Walking Dead ◽  
Wide Range ◽  
In Series ◽  
Fear And Anxiety ◽  
Small Screen ◽  
Network Era

While television has always played a role in recording and curating history, shaping cultural memory, and influencing public sentiment, the changing nature of the medium in the post-network era finds viewers experiencing and participating in this process in new ways. They skim through commercials, live tweet press conferences and award shows, and tune into reality shows to escape reality. This new era, defined by the heightened anxiety and fear ushered in by 9/11, has been documented by our media consumption, production, and reaction. In Small Screen, Big Feels, Melissa Ames asserts that TV has been instrumental in cultivating a shared memory of emotionally charged events unfolding in the United States since September 11, 2001. She analyzes specific shows and genres to illustrate the ways in which cultural fears are embedded into our entertainment in series such as The Walking Dead and Lost or critiqued through programs like The Daily Show. In the final section of the book, Ames provides three audience studies that showcase how viewers consume and circulate emotions in the post-network era: analyses of live tweets from Shonda Rhimes's drama, How to Get Away with Murder (2010--2020), ABC's reality franchises, The Bachelor (2002--present) and The Bachelorette (2003--present), and political coverage of the 2016 Presidential Debates. Though film has been closely studied through the lens of affect theory, little research has been done to apply the same methods to television. Engaging an impressively wide range of texts, genres, media, and formats, Ames offers a trenchant analysis of how televisual programming in the United States responded to and reinforced a cultural climate grounded in fear and anxiety.

scholarly journals Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Maja B. Rosić ◽  
Mirjana I. Simić ◽  
Predrag V. Pejović
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Weighted Least Squares ◽  
Target Localization ◽  
Localization Problem ◽  
Ml Estimation ◽  
Comparison Results ◽  
Adaptive Differential Evolution ◽  
Highly Nonlinear ◽  
Wide Range

This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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