Optimization of a Compliant Endoscopic Radiofrequency Ablation Electrode

2017 ◽  
Author(s):  
Bradley Hanks ◽  
Mary Frecker ◽  
Matthew Moyer
Keyword(s):  
Radiofrequency Ablation ◽  
Computational Models ◽  
Optimization Procedure ◽  
Pancreatic Tissue ◽  
Design Parameters ◽  
Electrode Design ◽  
Systematic Design ◽  
Objective Functions ◽  
Spherical Tumor ◽  
Compliant Electrode

Radiofrequency ablation (RFA) is a common cancer treatment modality for patients who are ineligible for open surgery. There is a need for RFA electrodes that generate heating zones that closely match the geometry of typical tumors, especially for endoscopic ultrasound-guided (EUS) RFA. In this paper, the procedure for optimization of an RFA electrode is presented. First, a novel compliant electrode design is proposed. Next, a thermal ablation model is developed to predict the ablation zone surrounding an RFA electrode in biological tissue. Then, a multi-objective genetic algorithm is used to optimize two cases of the electrode geometry to match the region of destructed tissue to a spherical tumor of specified diameter. This optimization procedure is applied to an EUS-RFA ablation of pancreatic tissue. For a target 2.5cm spherical tumor, the optimal design parameters of the compliant electrode design were found. After simulating 40 generations of 50 designs per generation, both cases converged to optimal solutions. The objective functions were useful for simple electrode designs. For more complex electrode designs, the objective functions were unable to direct the design toward a 2.5cm sphere. The results of the optimization demonstrate how computational models combined with optimization can be used for systematic design of ablation electrodes. The optimization procedure may be applied to RFA of various tissue types for systematic design of electrodes that generate spherical ablation zones.

scholarly journals Optimization of an Endoscopic Radiofrequency Ablation Electrode

2018 ◽  
Vol 12 (3) ◽  
Author(s):  
Bradley Hanks ◽  
Mary Frecker ◽  
Matthew Moyer
Keyword(s):  
Radiofrequency Ablation ◽  
Thermal Ablation ◽  
Computational Models ◽  
Optimization Procedure ◽  
Pancreatic Tissue ◽  
Design Parameters ◽  
Electrode Design ◽  
Systematic Design ◽  
Spherical Tumor ◽  
Compliant Electrode

Radiofrequency ablation (RFA) is an increasingly used, minimally invasive, cancer treatment modality for patients who are unwilling or unable to undergo a major resective surgery. There is a need for RFA electrodes that generate thermal ablation zones that closely match the geometry of typical tumors, especially for endoscopic ultrasound-guided (EUS) RFA. In this paper, the procedure for optimization of an RFA electrode is presented. First, a novel compliant electrode design is proposed. Next, a thermal ablation model is developed to predict the ablation zone produced by an RFA electrode in biological tissue. Then, a multi-objective genetic algorithm is used to optimize two cases of the electrode geometry to match the region of destructed tissue to a spherical tumor of a specified diameter. This optimization procedure is then applied to EUS-RFA ablation of pancreatic tissue. For a target 2.5 cm spherical tumor, the optimal design parameters of the compliant electrode design are found for two cases. Cases 1 and 2 optimal solutions filled 70.9% and 87.0% of the target volume as compared to only 25.1% for a standard straight electrode. The results of the optimization demonstrate how computational models combined with optimization can be used for systematic design of ablation electrodes. The optimization procedure may be applied to RFA of various tissue types for systematic design of electrodes for a specific target shape.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

Computer-Based Design of Disc Brakes by Multi-Objective Form Optimization

2001 ◽  
Author(s):  
Sayed M. Metwalli ◽  
Hesham A. Hegazi
Keyword(s):  
Thermal Stresses ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Multi Objective ◽  
Disk Brake ◽  
Disc Brakes ◽  
Present Design ◽  
Computer Based

Abstract This paper is concerned with the application of CAD to design disc brakes using multi-objective form optimization. Temperature and disc (rotor) volume are two competing design objective functions used to obtain the optimum design parameters of disc brakes. The geometrical parameters affecting disk brake design such as the outer and inner disc diameters, disc thickness, pad thickness and pad angle are considered. The exponents of the two competing objective functions are to be optimized for better results. A sensitivity analysis of the two exponents (A and B) is used for this optimization procedure. The design vector for optimizing disc brakes includes the outer disc diameters, diameter ratio, disc thickness, and pad angle. The multi-objective formulation satisfies maximum attained torque; minimum actuating force; minimum thermal stresses; minimum temperature and minimum disc volume. A comparison with two previous optimization results indicates a marked improvement of the present design since previous results did not consider all design parameters or optimized objectives.

Optimization of a Cemented Femoral Prosthesis Considering Bone Remodeling

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Leandro Luis Corso ◽  
Leandro de Freitas Spinelli ◽  
Fernando Schnaid ◽  
Crisley Dossin Zanrosso ◽  
Rogério José Marczak
Keyword(s):  
Bone Loss ◽  
Bone Remodeling ◽  
Hip Replacement ◽  
Computational Models ◽  
Three Dimensional ◽  
Femoral Stem ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Femoral Bone ◽  
Bone Mass Loss

The study presents a numerical methodology for minimizing the bone loss in human femur submitted to total hip replacement (THR) procedure with focus on cemented femoral stem. Three-dimensional computational models were used to describe the femoral bone behavior. An optimization procedure using the genetic algorithm (GA) method was applied in order to minimize the bone loss, considering the geometry and the material of the prosthesis as well as the design of the stem. Internal and external bone remodeling were analyzed numerically. The numerical method proposed here showed that the bone mass loss could be reduced by 24%, changing the design parameters.

Optimization of Turbofan Propulsion Cycle Using a Genetic Algorithm

2010 ◽  
Author(s):  
Adel Ghenaiet
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Pressure Ratio ◽  
Engine Performance ◽  
Optimization Procedure ◽  
Specific Fuel Consumption ◽  
High Mass ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Simplifying Assumptions

This paper presents an evolutionary approach as the optimization framework to design for the optimal performance of a high-bypass unmixed turbofan to match with the power requirements of a commercial aircraft. The parametric analysis had the objective to highlight the effects of the principal design parameters on the propulsive performance in terms of specific fuel consumption and specific thrust. The design optimization procedure based on the genetic algorithm PIKAIA coupled to the developed engine performance analyzer (on-design and off-design) aimed at finding the propulsion cycle parameters minimizing the specific fuel consumption, while meeting the required thrusts in cruise and takeoff and the restrictions of temperatures limits, engine size and weight as well as pollutants emissions. This methodology does not use engine components’ maps and operates on simplifying assumptions which are satisfying the conceptual or early design stages. The predefined requirements and design constraints have resulted in an engine with high mass flow rate, bypass ratio and overall pressure ratio and a moderate turbine inlet temperature. In general, the optimized engine is fairly comparable with available engines of equivalent power range.

scholarly journals Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes

Nanophotonics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 385-392
Author(s):  
Joeri Lenaerts ◽  
Hannah Pinson ◽  
Vincent Ginis
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Linear Mapping ◽  
Inverse Design ◽  
Design Parameters ◽  
Second Step ◽  
Input Parameters ◽  
Novel Variant ◽  
Resonant Systems ◽  
The Fourier Transform

AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

Optimal design of a parallel robotic gripper using enhanced multi-objective ant lion optimizer with a sensitivity analysis approach

2020 ◽  
Vol 40 (5) ◽  
pp. 703-721
Author(s):  
Golak Bihari Mahanta ◽  
Deepak BBVL ◽  
Bibhuti B. Biswal ◽  
Amruta Rout
Keyword(s):  
Sensitivity Analysis ◽  
Distribution Function ◽  
Design Parameters ◽  
Objective Functions ◽  
Content Type ◽  
Multi Objective ◽  
The Past ◽  
Design Variables ◽  
Pick And Place ◽  
Ant Lion

Purpose From the past few decades, parallel grippers are used successfully in the automation industries for performing various pick and place jobs due to their simple design, reliable nature and its economic feasibility. So, the purpose of this paperis to design a suitable gripper with appropriate design parameters for better performance in the robotic production systems. Design/methodology/approach In this paper, an enhanced multi-objective ant lion algorithm is introduced to find the optimal geometric and design variables of a parallel gripper. The considered robotic gripper systems are evaluated by considering three objective functions while satisfying eight constraint equations. The beta distribution function is introduced for generating the initial random number at the initialization phase of the proposed algorithm as a replacement of uniform distribution function. A local search algorithm, namely, achievement scalarizing function with multi-criteria decision-making technique and beta distribution are used to enhance the existing optimizer to evaluate the optimal gripper design problem. In this study, the newly proposed enhanced optimizer to obtain the optimum design condition of the design variables is called enhanced multi-objective ant lion optimizer. Findings This study aims to obtain optimal design parameters of the parallel gripper with the help of the developed algorithms. The acquired results are investigated with the past research paper conducted in that field for comparison. It is observed that the suggested method to get the best gripper arrangement and variables of the parallel gripper mechanism outperform its counterparts. The effects of the design variables are needed to be studied for a better design approach concerning the objective functions, which is achieved by sensitivity analysis. Practical implications The developed gripper is feasible to use in the assembly operation, as well as in other pick and place operations in different industries. Originality/value In this study, the problem to find the optimum design parameter (i.e. geometric parameters such as length of the link and parallel gripper joint angles) is addressed as a multi-objective optimization. The obtained results from the execution of the algorithm are evaluated using the performance indicator algorithm and a sensitivity analysis is introduced to validate the effects of the design variables. The obtained optimal parameters are used to develop a gripper prototype, which will be used for the assembly process.

scholarly journals Modified Koch Fractal Antenna for Multi and Wideband Wireless Applications

2021 ◽  
Vol 19 (2) ◽  
pp. 182-189
Author(s):  
Shweta Rani ◽  
Sushil Kakkar
Keyword(s):  
Numerical Simulations ◽  
Impedance Matching ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Impedance Bandwidth ◽  
Fractal Antenna ◽  
Bacterial Foraging Optimization ◽  
Koch Curve ◽  
Wireless Applications ◽  
Koch Fractal

This paper focuses on the design and development of modified Koch fractal antenna. Compared to traditional Koch curve antenna, the presented antenna possesses a greater number of frequency bands and better impedance matching. Furthermore, the bacterial foraging optimization (BFO) approach is implemented to enhance the impedance bandwidth. The developed technique has been verified by employing various numerical simulations. The design parameters generated from the optimization procedure have been utilized to manufacture the antenna and the respective experimental and simulated results compared. The measured results show that the designed antenna exhibits multi and wideband behavior, covering WLAN, WIMAX, and various other wireless applications.

scholarly journals Evaluation of the Cost-Optimal Method Applied to Existing Schools Considering PV System Optimization

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 611
Author(s):  
Cecilia Ciacci ◽  
Neri Banti ◽  
Vincenzo Di Naso ◽  
Frida Bazzocchi
Keyword(s):  
Optimization Procedure ◽  
Cost Effective ◽  
Heating System ◽  
System Optimization ◽  
Cold Climate ◽  
Design Parameters ◽  
Design Environment ◽  
Pv System ◽  
Optimal Method ◽  
The Cost

In Italy in 2020, only 15.5% of school building heritage was retrofitted from an energy and environmental point of view. In this paper, the cost-optimal method was applied to two different school buildings belonging to the same Italian cold climate zone but characterized by different structural and technological solutions. The research aims at defining the cost-effective redevelopment solution among several ones proposed to apply to this building type. At the same time, this paper provides a critical analysis of the methodology applied, highlighting deficiencies related to a not proper evaluation of environmentally friendly retrofitting measures. In a cost-effective context, the main results show that the intervention on the heating system is more convenient than the retrofitting of the envelope. The energy saving is equal to about 35% for both considered schools. Among the different proposed requalification configurations, the adoption of PV (photovoltaic) electric generation is included. In this regard, an optimization procedure was implemented in a generative design environment to maximize energy production with reference to different design parameters. As a result, a solution with south oriented PV modules with a tilt angle of 42° and arranged in 0.7 m spaced rows proved to be the most effective.

Generation of Complex Energy Systems by Combination of Elementary Processes

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Toffolo ◽  
S. Rech ◽  
A. Lazzaretto
Keyword(s):  
Design Optimization ◽  
Ad Hoc ◽  
Energy Systems ◽  
Optimization Procedure ◽  
Thermodynamic Cycle ◽  
Energy System ◽  
Design Parameters ◽  
System Configuration ◽  
Complex Energy ◽  
Synthesis Design

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

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