Developing and Comparing Alternative Design Optimization Formulations for a Vibration Absorber Example

2017 ◽  
Author(s):  
Siyao Luan ◽  
Deborah L. Thurston ◽  
Madhav Arora ◽  
James T. Allison
Keyword(s):  
Design Problem ◽  
Mathematical Optimization ◽  
Computational Time ◽  
Vibration Absorber ◽  
Solution Quality ◽  
Design Representation ◽  
Core Elements ◽  
Improved Design ◽  
Time Required ◽  
Level Of Effort

In some cases, the level of effort required to formulate and solve an engineering design problem as a mathematical optimization problem is significant, and the potential improved design performance may not be worth the excessive effort. In this article we address the tradeoffs associated with formulation and modeling effort. Here we define three core elements (dimensions) of design formulations: design representation, comparison metrics, and predictive model. Each formulation dimension offers opportunities for the design engineer to balance the expected quality of the solution with the level of effort and time required to reach that solution. This paper demonstrates how using guidelines can be used to help create alternative formulations for the same underlying design problem, and then how the resulting solutions can be evaluated and compared. Using a vibration absorber design example, the guidelines are enumerated, explained, and used to compose six alternative optimization formulations, featuring different objective functions, decision variables, and constraints. The six alternative optimization formulations are subsequently solved, and their scores reflecting their complexity, computational time, and solution quality are quantified and compared. The results illustrate the unavoidable tradeoffs among these three attributes. The best formulation depends on the set of tradeoffs that are best in that situation.

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

Evaluation of a Multi-Goal Solver for Use in a Blackboard Architecture

2014 ◽  
Vol 6 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Jeremy Straub
Keyword(s):  
Multiple Solutions ◽  
Computational Time ◽  
Problem Analysis ◽  
Robotic Control ◽  
Operating Environment ◽  
Blackboard Architecture ◽  
Rule Based ◽  
Multiple Levels ◽  
Time Required ◽  
Naive Approach

This article presents a multi-goal solver for problems that can be modeled using a Blackboard Architecture. The Blackboard Architecture can be used for data fusion, robotic control and other applications. It combines the rule-based problem analysis of an expert system with a mechanism for interacting with its operating environment. In this context, numerous control or domain (system-subject) problems may exist which can be solved through reaching one of multiple outcomes. For these problems which have multiple solutions, any of which constitutes an end-goal, a solving mechanism which is solution-choice-agnostic and finds the lowest-cost path to the lowest-cost solution is required. Such a solver mechanism is presented and characterized herein. The performance of the solver (including both the computational time required to ascertain a solution and execute it) is compared to the naïve Blackboard approach. This performance characterization is performed across multiple levels of rule counts and rule connectivity. The naïve approach is shown to generate a solution faster, but the solutions generated by this approach, in most cases, are inferior to those generated by the solver.

scholarly journals Planning Annual LNG Deliveries with Transshipment

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1490
Author(s):  
Mingyu Li ◽  
Peter Schütz
Keyword(s):  
Storage Capacity ◽  
Computational Time ◽  
Planning Problem ◽  
Rolling Horizon ◽  
Solution Quality ◽  
Total Cost ◽  
New Variant ◽  
Solution Methods ◽  
Time Formulation ◽  
The Impact

The introduction of transshipment ports in the liquefied natural gas (LNG) supply chain in recent years offers additional flexibility, but also challenges to the planning of the annual delivery program. We present a new variant of the LNG-annual delivery program (ADP) planning problem by considering transshipment as well as time-dependent sailing times. We present a continuous time formulation for the LNG-ADP problem and propose a rolling horizon heuristic to solve the problem. Both the model and heuristic were used to solve a case inspired by the Yamal LNG project. The computational results show that the heuristic provides good solutions within a relatively short amount of time, especially compared to the exact solution methods. However, there is a trade-off between computational time and solution quality when designing the rolling horizon heuristic. The results also show the impact storage capacity at the transshipment port has on the total cost.

scholarly journals Comparison of Genetic Algorithm and Harmony Search Method for 2D Geometry Optimization.

2018 ◽  
Vol 159 ◽  
pp. 01009 ◽  
Author(s):  
Mohammad Ghozi ◽  
Anik Budiati
Keyword(s):  
Genetic Algorithm ◽  
Harmony Search ◽  
Geometry Optimization ◽  
Steel Structure ◽  
Search Method ◽  
Geometric Optimization ◽  
Computational Time ◽  
Time Required ◽  
Average Computational Time ◽  
Better Than

There are many applications of Genetic Algorithm (GA) and Harmony Search (HS) Method for solving problems in civil engineering design. The question is, still, which method is better for geometry optimization of a steel structure. The purpose of this paper is to compare GA and HS performance for geometric optimization of a steel structure. This problem is solved by optimizing a steel structure using GA and HS and then comparing the structure’s weight as well as the time required for the calculation. In this study, GA produced a structural weight of 2308.00 kg to 2387.00 kg and HS scored 2193.12 kg to 2239.48 kg. The average computational time required by GA is 607 seconds and HS needed 278 seconds. It concludes that HS is faster and better than GA for geometry optimization of a steel structure.

Ensemble of surrogates and cross-validation for rapid and accurate predictions using small data sets

2019 ◽  
Vol 33 (4) ◽  
pp. 484-501 ◽  
Author(s):  
Reza Alizadeh ◽  
Liangyue Jia ◽  
Anand Balu Nellippallil ◽  
Guoxin Wang ◽  
Jia Hao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Weighted Average ◽  
Computational Time ◽  
Small Data ◽  
Data Sets ◽  
Computationally Efficient ◽  
Cross Validation Error ◽  
Rod Rolling ◽  
Ensemble Of Surrogates ◽  
Time Required

AbstractIn engineering design, surrogate models are often used instead of costly computer simulations. Typically, a single surrogate model is selected based on the previous experience. We observe, based on an analysis of the published literature, that fitting an ensemble of surrogates (EoS) based on cross-validation errors is more accurate but requires more computational time. In this paper, we propose a method to build an EoS that is both accurate and less computationally expensive. In the proposed method, the EoS is a weighted average surrogate of response surface models, kriging, and radial basis functions based on overall cross-validation error. We demonstrate that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions. We demonstrate the use of an EoS using hot rod rolling as an example. Finally, we include a rule-based template which can be used for other problems with similar requirements, for example, the computational time, required accuracy, and the size of the data.

scholarly journals A Hybrid Computational and Analytical Model of Inline Drip Emitters

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Jaya Narain ◽  
Amos G. Winter V
Keyword(s):  
Analytical Model ◽  
Flow Rate ◽  
Flow Behavior ◽  
Computational Time ◽  
Matrix Formulation ◽  
Element Analysis ◽  
Constant Flow Rate ◽  
Patch Load ◽  
Pressure Resistance ◽  
Time Required

This paper details a hybrid computational and analytical model to predict the performance of inline pressure compensating drip irrigation emitters. Pressure compensating emitters deliver a constant flow rate over a range of applied pressures to accurately meter water to crops. Flow rate is controlled within the emitter via a fixed resistance tortuous path, and a variable flow resistance composed of a flexible membrane that deflects under changes in pressure, restricting the flow path. A pressure resistance parameter was derived using an experimentally validated computational fluid dynamics (CFD) model to describe the flow behavior in tortuous paths. The bending mechanics of the membrane were modeled analytically and refined by deriving a correction factor using finite element analysis (FEA). A matrix formulation that calculates the force applied by a line or a patch load of any shape on a rectangular membrane, along which there is a prescribed deflection, was derived and was found to be accurate to be 1%. The combined hybrid computational–analytical model reduces the computational time of modeling emitters from hours to less than 30 min, dramatically lowering the time required to iterate and select optimal designs. The model was validated experimentally using three commercially available drip emitters and was accurate to within 12% of the experimental results.

scholarly journals Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1184
Author(s):  
Geraldine Cáceres Sepulveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Neural Networks ◽  
Acrylic Acid ◽  
Performance Criteria ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Meta Model ◽  
Multi Objective ◽  
Pareto Domain ◽  
Computationally Intensive ◽  
Time Required

It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5.

Optimal Design of Fractional-Order Digital Differentiator Using Flower Pollination Algorithm

2018 ◽  
Vol 27 (08) ◽  
pp. 1850129 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Fractional Order ◽  
Optimization Technique ◽  
Phase Response ◽  
Computational Time ◽  
Infinite Impulse Response ◽  
Flower Pollination Algorithm ◽  
Solution Quality ◽  
Digital Differentiator ◽  
Flower Pollination ◽  
Real Coded Genetic Algorithm

This paper presents an efficient approach to design wideband, accurate, stable, and minimum-phase fractional-order digital differentiators (FODDs) in terms of the infinite impulse response (IIR) filters using an evolutionary optimization technique called flower pollination algorithm (FPA). The efficiency comparisons of FPA with real-coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE)-based designs are conducted with respect to different magnitude and phase response error metrics, parametric and nonparametric statistical hypotheses tests, computational time, and fitness convergence. Exhaustive simulation results clearly demonstrate that FPA significantly outperforms RGA, PSO, and DE in attaining the best solution quality consistently. Extensive analysis is also conducted in order to determine the role of control parameters of FPA on the performance of the designed FODDs. The proposed FPA-based FODDs outperform all the designs published in the recent literature with respect to the magnitude responses and also achieve a competitive performance in terms of the phase response.

Avoiding Pitfalls in Multiphase Thermo-Hydrodynamic Coupling

2002 ◽  
Author(s):  
Luis F. Ayala ◽  
Eltohami S. Eltohami ◽  
Michael A. Adewumi
Keyword(s):  
Multiphase Flow ◽  
Petroleum Industry ◽  
Material Balance ◽  
Petroleum Products ◽  
Special Consideration ◽  
Computational Time ◽  
Time Saving ◽  
Trade Off ◽  
Time Required ◽  
The Impact

Multiphase flow is prevalent in many industrial processes. Therefore, accurate and efficient modeling of multiphase flow is essential to the understanding of these processes as well as the development of technologies to handle and manage them. In the petroleum industry, the occurrence and consequence thereof associated with such hydrodynamic processes are encountered in offshore facilities, surface facilities as well as reservoir applications. In this paper, we consider the modeling of these processes with special consideration to the transport of petroleum products through pipelines. Multiphase hydrodynamic modeling is usually a trade-off between maximizing the accuracy level while minimizing the computational time required. The most fundamental modeling effort developed to achieve this goal is based on applying simplifications to the basic physical laws, as defined by continuum mechanics, governing these processes. However, the modeling of multiphase flow processes requires the coupling of these basic laws with a thermodynamic phase behavior model. This paper highlights the impact of the techniques used to computationally couple the system’s thermodynamics with its fluid mechanics while paying close attention to the trade off mentioned above. It will consider the consequences of the simplifications applied, as well as inherent deficiencies associated with such simplifications. Special consideration is given to the conservation of mass as well as the terms that govern its transfer between the phases. Furthermore, the implications related to the common simplification of isothermal conditions are studied, highlighting the loss of accuracy in the material balance associated with this computational time-saving assumption. This paper concludes by suggesting remedies to these problems, supported by results, showing considerable improvement in fulfilling both the basic constrains which are minimizing time and maximizing accuracy.

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