scholarly journals Simultaneous Identification of Number, Location, and Release History of Groundwater Contamination Sources

2021 ◽  
Author(s):  
Jiuhui Li ◽  
Wenxi Lu ◽  
Zhengfang Wu ◽  
Hongshi He
Keyword(s):  
Simulation Model ◽  
Groundwater Contamination ◽  
Surrogate Model ◽  
Solution Process ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Integer Variable ◽  
Contamination Sources ◽  
History Of ◽  
Learning Machine

Abstract In previous studies, a 0-1 mixed integer nonlinear programming optimization model (0-1MINLPOM) could only identify the location and release intensity for groundwater contamination sources (GCSs), and the location of each GCS was regarded as a 0-1 integer variable, selected from several locations determined in advance. However, in actual situations, the locations usually cannot be accurately isolated to a few GCSs and the number of GCSs is often unknown, so 0-1MINLPOM was improved in this study. Based on the estimation that there is a maximum of three GCSs in the study area, an improved 0-1 MINLPOM was established to simultaneously identify the number of GCSs (treated as 0-1 integer variable), the location (treated as integer variable) and release history of GCS (treated as continuous variables). The simulation model was constructed as an equality constraint embedded improved 0-1 MINLPOM. In the improved 0-1 MINLPOM solution process, repeatedly calling the simulation model would have incurred a massive computational load and taken a long time. Thus, a surrogate model based on kriging and extreme learning machine (ELM) was established respectively for the simulation model to avoid this shortcoming. The results show that the accuracy of the kriging surrogate model (Krig-SM) was higher compared with the ELM surrogate model (ELM-SM). The improved 0-1 MINLPOM could identify the number, location, and release history of GCSs simultaneously. The accuracy of identifying the number of GCSs was 100%, and the accuracies of identifying the locations and release history were above 91.67% and 90.14%, respectively.

Comparative Morbidity Profile of Elective vs Therapeutic Neck Dissection

2021 ◽  
pp. 019459982110089
Author(s):  
Quinn Dunlap ◽  
James Reed Gardner ◽  
Amanda Ederle ◽  
Deanne King ◽  
Maya Merriweather ◽  
...  
Keyword(s):  
Neck Dissection ◽  
Surgical Complications ◽  
Chart Review ◽  
Care Center ◽  
Tertiary Care ◽  
Retrospective Chart Review ◽  
Neck Surgery ◽  
Continuous Variables ◽  
Significant Difference ◽  
History Of

Objective Neck dissection (ND) is one of the most commonly performed procedures in head and neck surgery. We sought to compare the morbidity of elective ND (END) versus therapeutic ND (TND). Study Design Retrospective chart review. Setting Academic tertiary care center. Methods Retrospective chart review of 373 NDs performed from January 2015 to December 2018. Patients with radical ND or inadequate chart documentation were excluded. Demographics, clinicopathologic data, complications, and sacrificed structures during ND were retrieved. Statistical analysis was performed with χ2 and analysis of variance for comparison of categorical and continuous variables, respectively, with statistical alpha set a 0.05. Results Patients examined consisted of 224 males (60%) with a mean age of 60 years. TND accounted for 79% (n = 296) as compared with 21% (n = 77) for END. Other than a significantly higher history of radiation (37% vs 7%, P < .001) and endocrine pathology (34% vs 2.6%, P < .001) in the TND group, no significant differences in demographics were found between the therapeutic and elective groups. A significantly higher rate of structure sacrifice and extranodal extension within the TND group was noted to hold in overall and subgroup comparisons. No significant difference in rate of surgical complications was appreciated between groups in overall or subgroup analysis. Conclusion While the significantly higher rate of structure sacrifice among the TND population represents an increased morbidity profile in these patients, no significant difference was found in the rate of surgical complications between groups. The significant difference seen between groups regarding history of radiation and endocrine pathology likely represents selection bias.

A Method to Define the Best Weld Sequence Using a Limited Number of Welding Simulation Analysis

2015 ◽  
Author(s):  
Mahyar Asadi ◽  
Ghazi Alsoruji
Keyword(s):  
Simulation Model ◽  
Surrogate Model ◽  
Simulation Analysis ◽  
Combinatorial Problem ◽  
Fem Analysis ◽  
Simulation Models ◽  
Computational Time ◽  
Optimal Sequence ◽  
Welded Structure ◽  
Welding Sequence

Weld sequence optimization, which is determining the best (and worst) welding sequence for welding work pieces, is a very common problem in welding design. The solution for such a combinatorial problem is limited by available resources. Although there are fast simulation models that support sequencing design, still it takes long because of many possible combinations, e.g. millions in a welded structure involving 10 passes. It is not feasible to choose the optimal sequence by evaluating all possible combinations, therefore this paper employs surrogate modeling that partially explores the design space and constructs an approximation model from some combinations of solutions of the expensive simulation model to mimic the behavior of the simulation model as closely as possible but at a much lower computational time and cost. This surrogate model, then, could be used to approximate the behavior of the other combinations and to find the best (and worst) sequence in terms of distortion. The technique is developed and tested on a simple panel structure with 4 weld passes, but essentially can be generalized to many weld passes. A comparison between the results of the surrogate model and the full transient FEM analysis all possible combinations shows the accuracy of the algorithm/model.

An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design

1993 ◽  
Author(s):  
B. K. Kannan ◽  
Steven N. Kramer
Keyword(s):  
Lagrange Multiplier ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Continuous Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Augmented Lagrange Multiplier

Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

scholarly journals Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network

2018 ◽  
Author(s):  
Ghaith Ghanim Al-Ghazal ◽  
Philip Bonello ◽  
Sergio G. Torres Cedillo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Structural Model ◽  
Solution Process ◽  
Time History ◽  
Inverse Model ◽  
Training Data ◽  
Squeeze Film ◽  
Squeeze Film Damper ◽  
History Of

Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.

scholarly journals Morality, protection, security and gain: lessons from a minimalistic, economically inspired multi-agent model

2020 ◽  
Vol 45 (1) ◽  
pp. 17-33
Author(s):  
Maciej Komosinski ◽  
Tomasz Żok
Keyword(s):  
Theoretical Analysis ◽  
Simulation Model ◽  
Continuous Variables ◽  
Agent Model ◽  
Agent Simulation ◽  
Multi Agent ◽  
Mathematical Equations

AbstractIn this work, we introduce a simple multi-agent simulation model with two roles of agents that correspond to moral and immoral attitudes. The model is given explicitly by a set of mathematical equations with continuous variables and is characterized by four parameters: morality, protection, and two efficiency parameters. Agents are free to adjust their roles to maximize individual gains. The model is analyzed theoretically to find conditions for its stability, i.e., the fractions of agents of both roles that lead to an equilibrium in their gains. A multi-agent simulation is also developed to verify the dynamics of the model for all values of morality and protection parameters, and to identify potential discrepancies with the theoretical analysis.

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

2021 ◽  
pp. 147592172110565
Author(s):  
Chungeon Kim ◽  
Hyunseok Oh ◽  
Byung Chang Jung ◽  
Seok Jun Moon
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Sensor Network ◽  
Sensor Placement ◽  
Mixed Integer ◽  
Working Fluid ◽  
Small Scale ◽  
Pipeline System ◽  
Optimal Sensor Placement ◽  
Pipeline Network

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

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