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Recent Advances in Stochastic Methods for Energy Analysis
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Recent Advances in Stochastic Methods for Energy Analysis

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 15333

Special Issue Editor

Prof. Dr. Marcin Kamiński

E-Mail Website
Guest Editor
Department of Structural Mechanics, Lodz University of Technology, 6 Politechniki Street, 90-924 Lodz, Poland
Interests: stochastic finite element method; stochastic reliability; random composites; probabilistic entropy; wavelet analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main aim of this issue would be to collect various works concerning energy analysis methods in mechanical systems with given boundary values and/or initial problems including some uncertainties. This uncertainty includes random variables, fields or processes modeling physical and mechanical parameters of solids and composite constituents, geometrical parameters or imperfections as well as active loads applied to the given system. Numerical solutions for the given problem using some extensions of the finite or boundary element methods, finite difference or volume methods as well as some meshless techniques are expected, together with some analytical or semi-analytical techniques. Probabilistic techniques of interest are the Monte-Carlo simulation method, stochastic perturbation or spectral techniques, and fuzzy sets theory, as well as their modern extensions or combinations. Computational and theoretical case studies starting from civil through mechanical up to aeronautical as well as electric or chemical engineering are invited. Specific applications towards homogenization methods, multi-field and/or multi-scale analyses as well as coupled magneto-electro-thermo-elastic problems are also welcome. A very attractive aspect would be reliability assessments for both time-independent (quality control or experimental statistics) and time-dependent uncertainty problems (like corrosion, fatigue and ageing) in all the above cases, where the energy estimate can be the basis of the limit state function. Probabilistic entropy computations in various engineering systems would be also interesting.

Prof. Dr. Marcin Kamiński
Guest Editor

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Keywords

  • Monte-Carlo simulation
  • stochastic perturbation method
  • semi-analytical probabilistic technique
  • energy analysis
  • coupled phenomena
  • reliability assessment
  • stochastic finite element method
  • probabilistic entropy

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Published Papers (5 papers)

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Research

39 pages, 568 KiB  
Article
A Robust q-Rung Orthopair Fuzzy Information Aggregation Using Einstein Operations with Application to Sustainable Energy Planning Decision Management
by Muhammad Riaz, Wojciech Sałabun, Hafiz Muhammad Athar Farid, Nawazish Ali and Jarosław Wątróbski
Energies 2020, 13(9), 2155; https://doi.org/10.3390/en13092155 - 1 May 2020
Cited by 93 | Viewed by 4996
Abstract
A q-rung orthopair fuzzy set (q-ROFS), an extension of the Pythagorean fuzzy set (PFS) and intuitionistic fuzzy set (IFS), is very helpful in representing vague information that occurs in real-world circumstances. The intention of this article is to introduce several aggregation operators in [...] Read more.
A q-rung orthopair fuzzy set (q-ROFS), an extension of the Pythagorean fuzzy set (PFS) and intuitionistic fuzzy set (IFS), is very helpful in representing vague information that occurs in real-world circumstances. The intention of this article is to introduce several aggregation operators in the framework of q-rung orthopair fuzzy numbers (q-ROFNs). The key feature of q-ROFNs is to deal with the situation when the sum of the qth powers of membership and non-membership grades of each alternative in the universe is less than one. The Einstein operators with their operational laws have excellent flexibility. Due to the flexible nature of these Einstein operational laws, we introduce the q-rung orthopair fuzzy Einstein weighted averaging (q-ROFEWA) operator, q-rung orthopair fuzzy Einstein ordered weighted averaging (q-ROFEOWA) operator, q-rung orthopair fuzzy Einstein weighted geometric (q-ROFEWG) operator, and q-rung orthopair fuzzy Einstein ordered weighted geometric (q-ROFEOWG) operator. We discuss certain properties of these operators, inclusive of their ability that the aggregated value of a set of q-ROFNs is a unique q-ROFN. By utilizing the proposed Einstein operators, this article describes a robust multi-criteria decision making (MCDM) technique for solving real-world problems. Finally, a numerical example related to integrated energy modeling and sustainable energy planning is presented to justify the validity and feasibility of the proposed technique. Full article
(This article belongs to the Special Issue Recent Advances in Stochastic Methods for Energy Analysis)
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16 pages, 7002 KiB  
Article
Energy Fluctuations in the Homogenized Hyper-Elastic Particulate Composites with Stochastic Interface Defects
by Damian Sokołowski, Marcin Kamiński and Artur Wirowski
Energies 2020, 13(8), 2011; https://doi.org/10.3390/en13082011 - 17 Apr 2020
Cited by 2 | Viewed by 2424
Abstract
The principle aim of this study is to analyze deformation energy of hyper-elastic particulate composites, which is the basis for their further probabilistic homogenization. These composites have some uncertain interface defects, which are modelled as small semi-spheres with random radius and with bases [...] Read more.
The principle aim of this study is to analyze deformation energy of hyper-elastic particulate composites, which is the basis for their further probabilistic homogenization. These composites have some uncertain interface defects, which are modelled as small semi-spheres with random radius and with bases positioned on the particle-matrix interface. These defects are smeared into thin layer of the interphase surrounding the reinforcing particle introduced as the third component of this composite. Matrix properties are determined from the experimental tests of Laripur LPR 5020 High Density Polyurethane (HDPU). It is strengthened with the Carbon Black particles of spherical shape. The Arruda–Boyce potential has been selected for numerical experiments as fitting the best stress-strain curves for the matrix behavior. A homogenization procedure is numerically implemented using the cubic Representative Volume Element (RVE). Spherical particle is located centrally, and computations of deformation energy probabilistic characteristics are carried out using the Iterative Stochastic Finite Element Method (ISFEM). This ISFEM is implemented in the algebra system MAPLE 2019 as dual approach based upon the stochastic perturbation method and, independently, upon a classical Monte-Carlo simulation, and uniform uniaxial deformations of this RVE are determined in the system ABAQUS and its 20-noded solid hexahedral finite elements. Computational experiments include initial deterministic numerical error analysis and the basic probabilistic characteristics, i.e., expectations, deviations, skewness and kurtosis of the deformation energy. They are performed for various expected values of the defects volume fraction. We analyze numerically (1) if randomness of homogenized deformation energy can correspond to the normal distribution, (2) how variability of the interface defects volume fraction affects the deterministic and stochastic characteristics of composite deformation energy and (3) whether the stochastic perturbation method is efficient in deformation energy computations (and in FEM analysis) of hyper-elastic media. Full article
(This article belongs to the Special Issue Recent Advances in Stochastic Methods for Energy Analysis)
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10 pages, 2650 KiB  
Article
Computer Simulation of Stochastic Energy Fluctuations in Tensile Test of Elasto-Plastic Porous Metallic Material
by Marcin Kamiński and Michał Strąkowski
Energies 2020, 13(2), 485; https://doi.org/10.3390/en13020485 - 19 Jan 2020
Viewed by 2342
Abstract
The main aim of this work is the computational implementation and numerical simulation of a metal porous plasticity model with an uncertain initial microdefects’ volume fraction using the Stochastic Finite Element Method (SFEM) based on the semi-analytical probabilistic technique. The metal porous plasticity [...] Read more.
The main aim of this work is the computational implementation and numerical simulation of a metal porous plasticity model with an uncertain initial microdefects’ volume fraction using the Stochastic Finite Element Method (SFEM) based on the semi-analytical probabilistic technique. The metal porous plasticity model applied here is based on Gurson–Tvergaard–Needleman theory and is included in the ABAQUS finite element system, while the external probabilistic procedures were programmed in the computer algebra system MAPLE 2017. Hybrid usage of these two computer systems enabled the determination of fluctuations in elastic and plastic energies due to initial variations in the ratio of the metal micro-voids, and the calculation of the first four probabilistic moments and coefficients of these energies due to Gaussian distribution of this ratio. A comparison with the Monte-Carlo simulation validated the numerical efficiency of the proposed approach for any level of input uncertainty and for the first four probabilistic characteristics traditionally seen in the experimental series. Full article
(This article belongs to the Special Issue Recent Advances in Stochastic Methods for Energy Analysis)
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17 pages, 3512 KiB  
Article
Enhanced Flat Window-Based Synchrophasor Measurement Algorithm for P Class PMUs
by Hui Xue, Yifan Cheng and Mengjie Ruan
Energies 2019, 12(21), 4039; https://doi.org/10.3390/en12214039 - 23 Oct 2019
Cited by 3 | Viewed by 1838
Abstract
Accurate and fast synchrophasor measurement, especially under dynamics and distortions, is crucial for control and protection of power grid. The dynamics and distortions in the power grid may occur simultaneously, which increase the complexity of the problem. To address this issue, an enhanced [...] Read more.
Accurate and fast synchrophasor measurement, especially under dynamics and distortions, is crucial for control and protection of power grid. The dynamics and distortions in the power grid may occur simultaneously, which increase the complexity of the problem. To address this issue, an enhanced flat window-based P class synchrophasor measurement algorithm (EFW-PSMA) is proposed in this paper. Firstly, an EFW is design based on the least square (LS) approach. Secondly, the EFWs are adopted as the low pass filters (LPFs) in the EFW-PSMA structure to extract the fundamental component. Finally, the frequency and rate of change of frequency (ROCOF) are estimated based on the LS approach. The EFW-PSMA has a simple implementation structure and low computation complexity. Theoretical analysis and simulation results verify the superiority of the method, especially under stressed grid conditions, where several types of disturbances occur simultaneously. The maximum total vector error (TVE) is 0.3% under the most stressed conditions that all the disturbances specified in the benchmark tests specified in the IEC/IEEE 60255-118-1 occur simultaneously. It means that the EFW-PSMA could be used for the protection applications of the synchrophasor measurement algorithm, which is important for PMUs to fast response in the control and protection actions in order to avert a possible collapse or other abnormal conditions. Full article
(This article belongs to the Special Issue Recent Advances in Stochastic Methods for Energy Analysis)
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17 pages, 3519 KiB  
Article
Propeller Synchrophasing Control with a Cylindrical Scaled Fuselage Based on an Improved Data Selection Algorithm
by Long Sheng, Xianghua Huang and Yunfei Cao
Energies 2019, 12(14), 2736; https://doi.org/10.3390/en12142736 - 17 Jul 2019
Viewed by 3034
Abstract
Propeller synchrophasing control is an active noise control method which can effectively reduce the noise in the cabin of a turboprop aircraft. The propeller signature model identified by the measured acoustic noise data is easily affected by flight speed, altitude, and the existence [...] Read more.
Propeller synchrophasing control is an active noise control method which can effectively reduce the noise in the cabin of a turboprop aircraft. The propeller signature model identified by the measured acoustic noise data is easily affected by flight speed, altitude, and the existence of the fuselage. Meanwhile, the noise excited by the propellers is nonstationary signal, which often fluctuates greatly, thus affecting the accuracy of the identification of the model. In this paper, a synchrophasing control experimental platform with a cylindrical scaled fuselage on ground is firstly established to validate the actual noise reduction in the cabin. Then, a minimum fluctuation data selection method based on wavelet filtering and three-parameter sinusoidal fitting is proposed to improve the identification accuracy of the propeller signature model. This method extracts the high-precision propeller blade passing frequency signal from the noise signal by using a wavelet filtering algorithm and selects the minimum fluctuation data segment by using a three-parameter sinusoidal fitting algorithm. The experimental results firstly show the significant noise attenuation achieved in the cabin using propeller synchrophasing control. Secondly, the propeller signature model improved by the minimum fluctuation data selection method has higher accuracy than that identified by the traditional method. Full article
(This article belongs to the Special Issue Recent Advances in Stochastic Methods for Energy Analysis)
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