scholarly journals A sequential optimization framework for simultaneous design variables optimization and probability uncertainty allocation

2020 ◽  
Author(s):  
Hai Fang ◽  
Chunlin Gong ◽  
Chunna Li ◽  
Yunwei Zhang ◽  
Andrea Da Ronch
Keyword(s):  
Sequential Optimization ◽  
Optimization Framework ◽  
Simultaneous Design ◽  
Design Variables

scholarly journals Sector search strategies for odor trail tracking

2021 ◽  
Author(s):  
Gautam Reddy ◽  
Boris I. Shraiman ◽  
Massimo Vergassola
Keyword(s):  
Close Contact ◽  
Sequential Optimization ◽  
Odor Trail ◽  
Optimization Framework ◽  
Behavioral Paradigm ◽  
Scent Trails ◽  
Loss Of Contact ◽  
Tracking Behavior ◽  
Geometric Elements ◽  
Unified Description

Terrestrial animals such as ants, mice and dogs often use surface-bound scent trails to establish navigation routes or to find food and mates, yet their tracking strategies are poorly understood. Tracking behavior features zig-zagging paths with animals often staying in close contact with the trail. Upon sustained loss of contact, animals execute a characteristic sequence of sweeping “casts” – wide oscillations with increasing amplitude. Here, we provide a unified description of trail-tracking behavior by introducing an optimization framework where animals search in the angular sector defined by their estimate of the trail’s heading and its uncertainty.In silicoexperiments using reinforcement learning based on this hypothesis recapitulate experimentally observed tracking patterns. We show that search geometry imposes limits on the tracking speed, and quantify its dependence on trail statistics and memory of past contacts. By formulating trail-tracking as a Bellman-type sequential optimization problem, we quantify the basic geometric elements of optimal sector search strategy, effectively explaining why and when casting is necessary. We propose a set of experiments to infer how tracking animals acquire, integrate and respond to past information on the tracked trail. More generally, we define navigational strategies relevant for animals and bio-mimetic robots, and formulate trail-tracking as a novel behavioral paradigm for learning, memory and planning.

Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery

2021 ◽  
Author(s):  
Timoleon Kipouros ◽  
Ibrahim Chamseddine ◽  
Michael Kokkolaras
Keyword(s):  
Drug Delivery ◽  
Tumor Model ◽  
Mechanistic Modeling ◽  
Analytical Relationship ◽  
Parallel Coordinates ◽  
Modeling And Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Nanoparticle Design ◽  
Nanoparticle Sizes

Abstract Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclincal studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate clinical translation of computational methods.

Multi-Objective Optimization of a Segmented Lunar Wheel Concept

2011 ◽  
Author(s):  
Michele Faragalli ◽  
Damiano Pasini ◽  
Peter Radzizsewski
Keyword(s):  
Optimization Problem ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Stress Concentrations ◽  
Multi Objective ◽  
Optimization Framework ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Rider Comfort ◽  
Original Concept

The goal of this work is to develop a systematic method for optimizing the structural design of a segmented wheel concept to improve its operating performance. In this study, a wheel concept is parameterized into a set of size and shape design variables, and a finite element model of the wheel component is created. A multi-objective optimization problem is formulated to optimize its directional compliance and reduce stress concentrations, which has a direct affect on the efficiency, traction, rider comfort, maneuverability, and reliability of the wheel. To solve the optimization problem, a Matlab-FE simulation loop is built and a multi-objective genetic algorithm is used to find the Pareto front of optimal solutions. A trade-off design is selected which demonstrates an improvement from the original concept. Finally, recommendations will be made to apply the structural optimization framework to alternative wheel conceptual designs.

A Sequential Algorithm for Possibility-Based Design Optimization

2006 ◽  
Author(s):  
Jun Zhou ◽  
Zissimos P. Mourelatos
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Computational Cost ◽  
Possibility Theory ◽  
Sequential Optimization ◽  
Sequential Algorithm ◽  
Computationally Efficient ◽  
Worst Case ◽  
Reliability Based Design ◽  
Design Variables

Deterministic optimal designs that are obtained without taking into account uncertainty/variation are usually unreliable. Although reliability-based design optimization accounts for variation, it assumes that statistical information is available in the form of fully defined probabilistic distributions. This is not true for a variety of engineering problems where uncertainty is usually given in terms of interval ranges. In this case, interval analysis or possibility theory can be used instead of probability theory. This paper shows how possibility theory can be used in design and presents a computationally efficient sequential optimization algorithm. After, the fundamentals of possibility theory and fuzzy measures are described, a double-loop, possibility-based design optimization algorithm is presented where all design constraints are expressed possibilistically. The algorithm handles problems with only uncertain or a combination of random and uncertain design variables and parameters. In order to reduce the high computational cost, a sequential algorithm for possibility-based design optimization is presented. It consists of a sequence of cycles composed of a deterministic design optimization followed by a set of worst-case reliability evaluation loops. Two examples demonstrate the accuracy and efficiency of the proposed sequential algorithm.

Additive Manufacturing-Oriented Design of Graded Lattice Structures Through Explicit Topology Optimization

2017 ◽  
Vol 84 (8) ◽  
Author(s):  
Chang Liu ◽  
Zongliang Du ◽  
Weisheng Zhang ◽  
Yichao Zhu ◽  
Xu Guo
Keyword(s):  
Topology Optimization ◽  
Structure Design ◽  
Lattice Structures ◽  
New Approach ◽  
Optimization Framework ◽  
Concurrent Optimization ◽  
Design Variables ◽  
Graded Material ◽  
Graded Lattice ◽  
Moving Morphable Components

In the present work, a new approach for designing graded lattice structures is developed under the moving morphable components/voids (MMC/MMV) topology optimization framework. The essential idea is to make a coordinate perturbation to the topology description functions (TDF) that are employed for the description of component/void geometries in the design domain. Then, the optimal graded structure design can be obtained by optimizing the coefficients in the perturbed basis functions. Our numerical examples show that the proposed approach enables a concurrent optimization of both the primitive cell and the graded material distribution in a straightforward and computationally effective way. Moreover, the proposed approach also shows its potential in finding the optimal configuration of complex graded lattice structures with a very small number of design variables employed under various loading conditions and coordinate systems.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2003 ◽  
Vol 125 (1) ◽  
pp. 124-130 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Optimizing the buckling strength of filament winding composite cylinders under hydrostatic pressure

2018 ◽  
Vol 37 (13) ◽  
pp. 892-904 ◽  
Author(s):  
Shen Ke Chun ◽  
Pan Guang
Keyword(s):  
Cylindrical Shell ◽  
Hydrostatic Pressure ◽  
Filament Winding ◽  
Base Line ◽  
Composite Cylindrical Shell ◽  
Element Analysis ◽  
Optimization Framework ◽  
Number Of Layers ◽  
Design Variables ◽  
Critical Buckling Pressure

This paper presents the design optimization of filament winding composite cylindrical shell under hydrostatic pressure to maximize the critical buckling pressure. To this end, an optimization framework has been developed by employing numerical solution integrated with genetic algorithm. The design variables are fiber orientation and the corresponding number of layers. The framework is used to find the optimal design of filament winding composite cylindrical shell subjected to hydrostatic pressure. Three types of winding pattern are investigated, and the maximum critical buckling loads are increased by 26.14%, 25.82% and 20.95% compared with the base line, respectively. The influences of design variables on the critical buckling pressure are investigated. Results show that filament winding angles have more significant effect on the critical buckling pressure than the corresponding number of layers. Comparative study is carried out to verify the efficiency and accuracy of the optimization framework. Compared with the finite element analysis, the optimization framework has significant advantages in terms of calculation efficiency.

Minimal Deviation Paths for Manipulators With Joint Clearances

2014 ◽  
Author(s):  
Ming-Cheng Lai ◽  
Kuei-Yuan Chan
Keyword(s):  
Optimal Path ◽  
Joint Clearance ◽  
Control Effort ◽  
Optimization Framework ◽  
Design Variables ◽  
Additional Equipment ◽  
Angular Velocities ◽  
Minimal Deviation ◽  
Joint Clearances ◽  
Target Locations

Manipulator joint clearance is a natural consequence of manufacturing processes. Although most studies in the literature have assumed zero joint clearance, its existence is unavoidable and thus its impact needs to be evaluated. With the miniaturizing trend in engineering products, errors due to joint clearance have become an increasingly important issue. This study investigates how manipulators deviate from the desired working sites due to joint clearance. Deviations from the target locations can be reduced by properly selecting the working path. The optimal path is obtained by first parameterizing the path based on the required target task locations. Corresponding controlling inputs, namely linear and angular velocities as well as their accelerations, are calculated using inverse kinematics. Joint clearances are then added to obtain the deviations a path will make. An optimization framework with path parameters as the design variables is then formulated to minimize the resulting deviations. The proposed framework is shown to improve accuracy without additional equipment cost or control effort. A five-bar parallel manipulator is used to demonstrate the proposed method.

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

2001 ◽  
Author(s):  
Charles D. McAllister ◽  
Timothy W. Simpson
Keyword(s):  
Internal Combustion Engine ◽  
Design Optimization ◽  
Robust Design ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Collaborative Optimization ◽  
Robust Design Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Multiobjective Approach

Abstract In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed approach, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

Estimating Dynamics On-the-Fly Using Monocular Video

2011 ◽  
Author(s):  
Priyanshu Agarwal ◽  
Suren Kumar ◽  
Jason J. Corso ◽  
Venkat Krovi
Keyword(s):  
Physical System ◽  
Sequential Optimization ◽  
Physical Parameters ◽  
Double Pendulum ◽  
Equivalent System ◽  
Pendulum System ◽  
System Parameters ◽  
Optimization Framework ◽  
Video Images ◽  
Monocular Image

We present an optimization framework to help estimate on-the-fly both the motion and physical parameters of an articulated multibody system using uncalibrated monocular image sequences. The algorithm takes video images of a physical system as input and estimates the motion together with the physical system parameters, given only the underlying articulated model topology. A valid initial pose of the system is found using a sequential optimization framework and used to bootstrap the successive pose estimation as well as estimation of physical system parameters (kinematic/geometric lengths as well as mass, inertia, damping coefficients). We also address the issue of robustly estimating a dynamically-equivalent system using partial state information (solely from noisy visual observations) and without explicit inertial parameter information. This framework results in a robust dynamically-equivalent system with good predictive capabilities when tested on a double pendulum system.

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