Geometries, stabilities and fragmental channels of neutral and charged sulfur clusters: SnQ(n = 3–20, Q = 0, ±1)

2015 ◽  
Vol 17 (20) ◽  
pp. 13590-13597 ◽  
Author(s):  
Yuanyuan Jin ◽  
George Maroulis ◽  
Xiaoyu Kuang ◽  
Liping Ding ◽  
Cheng Lu ◽  
...  
Keyword(s):  
Global Minimum ◽  
Geometric Optimization

We have performed unbiased searches for the global minimum structures of neutral and charged sulfur clusters relying on the CALYPSO method combined with DFT geometric optimization.

scholarly journals COMPUTER SIMULATION OF THE POROUS STRUCTURE OF HYDROLYSIS LIGNIN AND ITS HARD COMPO-SITES WITH COMPONENTS OF OIL FUELS AND WATER

2019 ◽  
pp. 73-84
Author(s):  
Татьяна (Tat'yana) Александровна (Aleksandrovna) Савицкая (Savitskaya) ◽  
Иван (Ivan) Вадимович (Vadimovich) Резников (Reznikov) ◽  
Дмитрий (Dmitriy) Давидович (Davidovich) Гриншпан (Grinshpan)
Keyword(s):  
Pore Structure ◽  
Global Minimum ◽  
Dipole Interaction ◽  
Geometric Optimization ◽  
Alcohol Molecule ◽  
Hydrolysis Lignin ◽  
Organic Substances ◽  
Sorption Activity ◽  
Structural Units ◽  
Dynamics Method

A fragment of the supramolecular structure of lignin was simulated by cross-linking the C546H600O196 structural units along the corresponding CH–bonds followed by geometric optimization by the Polac-Ribier’s conjugate gradient method in the MNDO field of the HyperChem 8.0 software and ChemBio 13.0. The pore structure of lignin was simulated as well with optimization by the molecular mechanic’s method according to the MM2 Norman-Alinger’s algorithm taking into account the dipole-dipole interaction. Сoumaric alcohol molecule was a building block. The calculation of the energy parameters of lignin’s pore structure filling by the molecules of organic substances and water were carried out. It included a search for a global minimum of the potential energy by the molecular mechanic’s method according to the MM2 program and by the molecular dynamics method. The calculation had its basis in the “closed” pores existence in lignin. The calculated values of pore volume and specific surface area correlate with the experimental data. The way of prediction of organic substances sorption activity to lignin was proposed. This is evident from their calculated hydrophobicity parameter that correlates with the interaction energy of the molecules in the lignin pore.

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

Researching nitrogen solubility in nitrogen-containing austenitic steels at melting and recrystallization by CALPHAD method

2019 ◽  
pp. 53-66 ◽  
Author(s):  
L. A. Smirnov ◽  
I. I. Gorbachev ◽  
V. V. Popov ◽  
A. Yu. Pasynkov ◽  
A. S. Oryshchenko ◽  
...  
Keyword(s):  
Global Minimum ◽  
Nitrogen Solubility ◽  
Thermodynamic Description ◽  
Solidus Temperature ◽  
Liquidus Line ◽  
Calphad Method ◽  
Austenitic Steels ◽  
Nitrogen Transfer ◽  
Fixed Nitrogen ◽  
System Phase

The CALPHAD method has been employed to compose thermodynamic description of the Fe–Cr–Mn–Ni–Si–C–N system. Using an algorithm based on finding a global minimum of Gibbs energy, the calculations of system phase composition were performed in the temperature range from 1750°C to hardening and in the range of compositions corresponding to 04Kh20N6G11M2AFB steel. Calculations showed that at temperatures above liquidus line, Cr and Mn increase nitrogen solubility in the melt, while Ni and Si reduce it. With an increase in the content of Cr, Mn, Ni, and Si in steel in the studied composition range, both liquidus and solidus temperature decrease. The degree of influence on these temperatures of Cr, Mn, Ni and Si within the steel grade is different and ranges from ~3 to ~14°C. Calculations taking into account the possibility of nitrogen transfer between steel and the atmosphere of air showed that the amount of fixed nitrogen in the alloy under study varies, depending on the composition of the steel and temperature, from ~0.3 to ~0.6 wt%. As the temperature decreases from liquidus to solidus, the amount of fixed nitrogen increases, with the exception of those steel compositions when ferrite and not austenite is released from the liquid phase.

scholarly journals Adding Prior Information in FWI through Relative Entropy

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 599
Author(s):  
Danilo Cruz ◽  
João de Araújo ◽  
Carlos da Costa ◽  
Carlos da Silva
Keyword(s):  
Inverse Problem ◽  
Relative Entropy ◽  
Global Minimum ◽  
Prior Information ◽  
Waveform Inversion ◽  
Petroleum Industry ◽  
Synthetic Data ◽  
Full Waveform Inversion ◽  
Full Waveform

Full waveform inversion is an advantageous technique for obtaining high-resolution subsurface information. In the petroleum industry, mainly in reservoir characterisation, it is common to use information from wells as previous information to decrease the ambiguity of the obtained results. For this, we propose adding a relative entropy term to the formalism of the full waveform inversion. In this context, entropy will be just a nomenclature for regularisation and will have the role of helping the converge to the global minimum. The application of entropy in inverse problems usually involves formulating the problem, so that it is possible to use statistical concepts. To avoid this step, we propose a deterministic application to the full waveform inversion. We will discuss some aspects of relative entropy and show three different ways of using them to add prior information through entropy in the inverse problem. We use a dynamic weighting scheme to add prior information through entropy. The idea is that the prior information can help to find the path of the global minimum at the beginning of the inversion process. In all cases, the prior information can be incorporated very quickly into the full waveform inversion and lead the inversion to the desired solution. When we include the logarithmic weighting that constitutes entropy to the inverse problem, we will suppress the low-intensity ripples and sharpen the point events. Thus, the addition of entropy relative to full waveform inversion can provide a result with better resolution. In regions where salt is present in the BP 2004 model, we obtained a significant improvement by adding prior information through the relative entropy for synthetic data. We will show that the prior information added through entropy in full-waveform inversion formalism will prove to be a way to avoid local minimums.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Geometric optimization of dielectric elastomer electrodes for dynamic applications

2021 ◽  
Vol 181 ◽  
pp. 108120
Author(s):  
Emil Garnell ◽  
Bekir Aksoy ◽  
Corinne Rouby ◽  
Herbert Shea ◽  
Olivier Doaré
Keyword(s):  
Dielectric Elastomer ◽  
Geometric Optimization

scholarly journals A New Approach for Design Optimization and Parametric Analysis of Symmetric Compound Parabolic Concentrator for Photovoltaic Applications

2021 ◽  
Vol 13 (9) ◽  
pp. 4606
Author(s):  
Faisal Masood ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Javed Akhter ◽  
Mohammad Azad Alam
Keyword(s):  
Parametric Analysis ◽  
Concentration Ratio ◽  
Synergistic Effects ◽  
Geometric Optimization ◽  
Design Parameters ◽  
Imaging Device ◽  
Compound Parabolic Concentrator ◽  
Photovoltaic Applications ◽  
Half Angle ◽  
The Impact

A compound parabolic concentrator (CPC) is a non-imaging device generally used in PV, thermal, or PV/thermal hybrid systems for the concentration of solar radiation on the target surface. This paper presents the geometric design, statistical modeling, parametric analysis, and geometric optimization of a two-dimensional low concentration symmetric compound parabolic concentrator for potential use in building-integrated and rooftop photovoltaic applications. The CPC was initially designed for a concentration ratio of “2×” and an acceptance half-angle of 30°. A MATLAB code was developed in house to provoke the CPC reflector’s profile. The height, aperture width, and concentration ratios were computed for different acceptance half-angles and receiver widths. The interdependence of optical concentration ratio and acceptance half-angle was demonstrated for a wide span of acceptance half-angles. The impact of the truncation ratio on the geometric parameters was investigated to identify the optimum truncation position. The profile of truncated CPC for different truncation positions was compared with full CPC. A detailed statistical analysis was performed to analyze the synergistic effects of independent design parameters on the responses using the response surface modeling approach. A set of optimized design parameters was obtained by establishing specified optimization criteria. A 50% truncated CPC with an acceptance half-angle of 21.58° and receiver width of 193.98 mm resulted in optimum geometric dimensions.

scholarly journals Study on geometric optimization for new type of steel mortise and tenon joint based on performance

2021 ◽  
Vol 676 (1) ◽  
pp. 012039
Author(s):  
Shuohui Chen ◽  
Xuan Teng ◽  
Xiuxing Sang ◽  
Birun Ye ◽  
Zongmin Liang
Keyword(s):  
Geometric Optimization ◽  
New Type ◽  
Mortise And Tenon
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