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.

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.

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.

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.

scholarly journals Robust estimation of vertical symmetry axis models via joint migration inversion: Including multiples in anisotropic parameter estimation

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. C57-C74 ◽  
Author(s):  
Abdulrahman A. Alshuhail ◽  
Dirk J. Verschuur
Keyword(s):  
Symmetry Axis ◽  
Transverse Isotropy ◽  
Half Cycle ◽  
Anisotropic Parameter ◽  
Data Set ◽  
Trade Off ◽  
Trade Offs ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Internal Multiples

Because the earth is predominately anisotropic, the anisotropy of the medium needs to be included in seismic imaging to avoid mispositioning of reflectors and unfocused images. Deriving accurate anisotropic velocities from the seismic reflection measurements is a highly nonlinear and ambiguous process. To mitigate the nonlinearity and trade-offs between parameters, we have included anisotropy in the so-called joint migration inversion (JMI) method, in which we limit ourselves to the case of transverse isotropy with a vertical symmetry axis. The JMI method is based on strictly separating the scattering effects in the data from the propagation effects. The scattering information is encoded in the reflectivity operators, whereas the phase information is encoded in the propagation operators. This strict separation enables the method to be more robust, in that it can appropriately handle a wide range of starting models, even when the differences in traveltimes are more than a half cycle away. The method also uses internal multiples in estimating reflectivities and anisotropic velocities. Including internal multiples in inversion not only reduces the crosstalk in the final image, but it can also reduce the trade-off between the anisotropic parameters because internal multiples usually have more of an imprint of the subsurface parameters compared with primaries. The inverse problem is parameterized in terms of a reflectivity, vertical velocity, horizontal velocity, and a fixed [Formula: see text] value. The method is demonstrated on several synthetic models and a marine data set from the North Sea. Our results indicate that using JMI for anisotropic inversion makes the inversion robust in terms of using highly erroneous initial models. Moreover, internal multiples can contain valuable information on the subsurface parameters, which can help to reduce the trade-off between anisotropic parameters in inversion.

The Intelligent Design of the Gear Reducer

2012 ◽  
Vol 522 ◽  
pp. 823-827
Author(s):  
Jian Jiang Fang ◽  
Wen Jun Qi
Keyword(s):  
Intelligent Design ◽  
Object Oriented ◽  
Data Access ◽  
Mechanical Transmission ◽  
Design Efficiency ◽  
Integrated Technology ◽  
Gear Drive ◽  
Object Oriented Technology ◽  
Wide Range ◽  
User Friendly

The gear drive is the wide range of applications and is particularly important as a form of mechanical transmission, but the design process requires large amounts of data access and computation. In the paper, computer integrated technology and object-oriented technology is used to research and develop the intelligent design of Straight gear reducer system with user-friendly interactive platform, easy to use, high design efficiency and reliable data.

Sign in / Sign up

Export Citation Format

Share Document