scholarly journals The Genetic Algorithm: Using Biology to Compute Liquid Crystal Director Configurations

Crystals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1041
Author(s):  
S. Yang ◽  
Peter J. Collings
Keyword(s):  
Genetic Algorithm ◽  
Liquid Crystal ◽  
Global Minimum ◽  
Free Energies ◽  
Elastic Surface ◽  
Trial Solution ◽  
Biological Parents ◽  
Computational Speed ◽  
Wide Range ◽  
Optimization Routine

The genetic algorithm is an optimization routine for finding the solution to a problem that requires a function to be minimized. It accomplishes this by creating a population of solutions and then producing “offspring” solutions from this population by combining two “parental” solutions in much the way that the DNA of biological parents is combined in the DNA of offspring. Strengths of the algorithm include that it is simple to implement, no trial solution is required, and the results are fairly accurate. Weaknesses include its slow computational speed and its tendency to find a local minimum that does not represent the global minimum of the function. By minimizing the elastic, surface, and electric free energies, the genetic algorithm is used to compute the liquid crystal director configuration for a wide range of situations, including one- and two-dimensional problems with various forms of boundary conditions, with and without an applied electric field. When appropriate, comparisons are made with the exact solutions. Ways to increase the performance of the algorithm as well as how to avoid various pitfalls are discussed.

Understanding Conformational Entropy in Small Molecules

2020 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Small Molecules ◽  
Degrees Of Freedom ◽  
Absolute Error ◽  
Low Energy ◽  
Standard Entropy ◽  
Free Energies ◽  
Conformational Entropy ◽  
Wide Range ◽  
High Degree ◽  
Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Closed-Form Correlation of Buildings Energy Use With Key Design Parameters Calibrated Using a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Raed I. Bourisli ◽  
Adnan A. AlAnzi
Keyword(s):  
Genetic Algorithm ◽  
Closed Form ◽  
Energy Use ◽  
Building Design ◽  
Office Buildings ◽  
Design Parameters ◽  
Wide Range ◽  
Variable Function ◽  
Real Coded Genetic Algorithm ◽  
The Difference

This work aims at developing a closed-form correlation between key building design variables and its energy use. The results can be utilized during the initial design stages to assess the different building shapes and designs according to their expected energy use. Prototypical, 20-floor office buildings were used. The relative compactness, footprint area, projection factor, and window-to-wall ratio were changed and the resulting buildings performances were simulated. In total, 729 different office buildings were developed and simulated in order to provide the training cases for optimizing the correlation’s coefficients. Simulations were done using the VisualDOE TM software with a Typical Meteorological Year data file, Kuwait City, Kuwait. A real-coded genetic algorithm (GA) was used to optimize the coefficients of a proposed function that relates the energy use of a building to its four key parameters. The figure of merit was the difference in the ratio of the annual energy use of a building normalized by that of a reference building. The objective was to minimize the difference between the simulated results and the four-variable function trying to predict them. Results show that the real-coded GA was able to come up with a function that estimates the thermal performance of a proposed design with an accuracy of around 96%, based on the number of buildings tested. The goodness of fit, roughly represented by R2, ranged from 0.950 to 0.994. In terms of the effects of the various parameters, the area was found to have the smallest role among the design parameters. It was also found that the accuracy of the function suffers the most when high window-to-wall ratios are combined with low projection factors. In such cases, the energy use develops a potential optimum compactness. The proposed function (and methodology) will be a great tool for designers to inexpensively explore a wide range of alternatives and assess them in terms of their energy use efficiency. It will also be of great use to municipality officials and building codes authors.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Sourcing and Qualifying Passive Polarised 3D TVs

2021 ◽  
Vol 2021 (2) ◽  
pp. 100-1-100-6
Author(s):  
Andrew J. Woods
Keyword(s):  
Liquid Crystal ◽  
3D Display ◽  
Consumer Market ◽  
Testing Technique ◽  
Stereoscopic 3D ◽  
Wide Range ◽  
3D Displays ◽  
Liquid Crystal Shutter ◽  
Rear Projection

Millions of Stereoscopic 3D capable TVs were sold into the consumer market from 2007 through to 2016. A wide range of display technologies were supported including rear-projection DLP, Plasma, LCD and OLED. Some displays supported the Active 3D method using liquid-crystal shutter glasses, and some displays supported the Passive 3D method using circularly polarised 3D glasses. Displays supporting Full-HD and Ultra-HD (4K) resolution were available in sizes ranging from 32" to 86" diagonal. Unfortunately display manufacturers eventually changed their focus to promoting other display technologies and 2016 was the last year that new 3D TVs were made for the consumer market. Fortunately, there are still millions of 3D displays available through the secondhand- market, however it can be difficult to know which displays have 3D display support. This paper will provide a listing of specifically Passive 3D TVs manufactured by LG, however it has been our experience that the 3D quality varied considerably from one display to another hence it is necessary to qualify the quality of the 3D available on these displays using a testing technique that will be described in the paper.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Shui-Hua Wang ◽  
Xianwei Jiang ◽  
Yu-Dong Zhang
Keyword(s):  
Multiple Sclerosis ◽  
Genetic Algorithm ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Adaptive Genetic Algorithm ◽  
Biorthogonal Wavelet ◽  
Wide Range ◽  
Wavelet Features ◽  
The Brain ◽  
Better Than

Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 wavelet is used to extract multiscale coefficients. Second, three types of biorthogonal wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods.

scholarly journals Installation for measuring the dielectric anisotropy of liquid crystals at low frequencies by the bridge method with constant displacement

2021 ◽  
Vol 1198 (1) ◽  
pp. 012006
Author(s):  
S V Kalashnikov ◽  
N A Romanov ◽  
A V Nomoev
Keyword(s):  
Liquid Crystal ◽  
Liquid Crystals ◽  
Internal Resistance ◽  
Dielectric Anisotropy ◽  
Dielectric Parameters ◽  
Low Frequencies ◽  
Offset Voltage ◽  
Wide Range ◽  
Ac Current ◽  
Constant Displacement

Abstract Installation designed to measure the dielectric anisotropy in laboratory studies of liquid crystal polymer films is described. The installation operates on the principle of a balanced alternating current (AC) bridge, allowing the application of a direct external current (bias) to the liquid crystal cell. The internal resistance of the direct current (DC) source, which affects the equilibrium condition of the bridge, is compensated. The frequency of the AC current feeding the bridge and the offset voltage of the cell is regulated within a wide range, which makes it possible to study various functional dependences of the dielectric parameters of liquid crystals and their modifiers.Introduction

scholarly journals Numerical optimisation of a small-scale wind turbine through the use of surrogate modelling

2017 ◽  
Vol 28 (3) ◽  
pp. 79 ◽  
Author(s):  
Gareth Erfort ◽  
Theodor Willem Von Backström ◽  
Gerhard Venter
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Surrogate Model ◽  
Turbine Blades ◽  
Small Scale ◽  
Final Solution ◽  
B Splines ◽  
Wind Speeds ◽  
Wide Range ◽  
Numerical Optimisation

Wind conditions in South Africa are suitable for small-scale wind turbines, with wind speeds below 7 m.s−1. This investigation is about a methodology to optimise a full wind turbine using a surrogate model. A previously optimised turbine was further optimised over a range of wind speeds in terms of a new parameterisation methodology for the aerodynamic profile of the turbine blades, using non-uniform rational B-splines to encompass a wide range of possible shapes. The optimisation process used a genetic algorithm to evaluate an input vector of 61 variables, which fully described the geometry, wind conditions and rotational speed of the turbine. The optimal performance was assessed according to a weighted coefficient of power, which rated the turbine blade’s ability to extract power from the available wind stream. This methodology was validated using XFOIL to assess the final solution. The results showed that the surrogate model was successful in providing an optimised solution and, with further refinement, could increase the coefficient of power obtained.

scholarly journals Aligning lyotropic liquid crystals with unconventional organic layers

2017 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Eva Otón ◽  
Morten Andreas Geday ◽  
Caterina Maria Tone ◽  
José Manuel Otón ◽  
Xabier Quintana
Keyword(s):  
Liquid Crystal ◽  
Liquid Crystals ◽  
Chem Phys ◽  
Water Soluble ◽  
Lyotropic Liquid Crystals ◽  
Sunset Yellow ◽  
Lab On Chip ◽  
Wide Range ◽  
Homeotropic Alignment ◽  
Alignment Layers

Lyotropic chromonic liquid crystals (LCLC) are a kind of LCs far less known and more difficult to control than conventional thermotropic nematics. Nevertheless, LCLCs are a preferred option -often the only one- for applications where hydrophilic materials must be employed. Being water-soluble, LCLC can be used in numerous biology related devices, for example in target detection in lab-on-chip devices. However, their properties and procedures to align them are still less explored, with only a very limited number of options available, especially for homeotropic alignment. In this work, novel organic alignment layers and alignment properties have been explored for selected LCLCs. Non-conventional organic alignment layers were tested and new suitable procedures and materials for both homogeneous and homeotropic alignments have been found. Full Text: PDF ReferencesS.L. Hefinstine, O.D. Lavrentovich, C.J. Woolverton, "Lyotropic liquid crystal as a real-time detector of microbial immune complexes", Lett. Appl. Microbiol. 43, 27 (2006). CrossRef M.A. Geday, M. Ca-o-García, J.M. Escolano, E. Otón, J.M. Otón, X. Quintana, Conference on Liquid Crystals CLC'16, Poland (2016).M.A. Geday, E. Otón, J.M. Escolano, J.M. Otón, X. Quintana, Patent WO 2015193525 (2015). DirectLink Yu.A. Nastishin et al., "Optical characterization of the nematic lyotropic chromonic liquid crystals: Light absorption, birefringence, and scalar order parameter", Phys. Rev. E, 72 (4) 41711 (2005). CrossRef A. Mcguire, et al., "Orthogonal Orientation of Chromonic Liquid Crystals by Rubbed Polyamide Films", Chem. Phys. Chem. 15 (7) (2014). CrossRef J. Jeong, et al., "Homeotropic Alignment of Lyotropic Chromonic Liquid Crystals Using Noncovalent Interactions", Langmuir 30(10) 2914 (2014). CrossRef J.Y. Kim, H.-Tae Jung, "Macroscopic alignment of chromonic liquid crystals using patterned substrates", Phys. Chem. Chem. Phys. 18, 10362 (2016). CrossRef E. Otón, J.M. Escolano, X. Quintana, J.M. Otón, M.A. Geday, "Aligning lyotropic liquid crystals with silicon oxides", Liq. Cryst. 42 (8) 1069 (2015). CrossRef H.S. Park, et al., "Condensation of Self-Assembled Lyotropic Chromonic Liquid Crystal Sunset Yellow in Aqueous Solutions Crowded with Polyethylene Glycol and Doped with Salt", Langmuir 27, 4164 (2011). CrossRef H.S. Park, et al., "Self-Assembly of Lyotropic Chromonic Liquid Crystal Sunset Yellow and Effects of Ionic Additives", J. Phys. Chem. B 112, 16307 (2008). CrossRef R Caputo et al., "POLICRYPS: a liquid crystal composed nano/microstructure with a wide range of optical and electro-optical applications", J. Opt. A: Pure Appl. Opt. 11, 024017 (2009). CrossRef

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