Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients

An integrated approach to determine the rational design of wall ceramic products based on modeling their behavior under operating conditions is proposed. This approach was used in the development of technology for heat–efficient insulating construction ceramic materials for energy–saving construction. For two models of porous–hollow ceramic products with a porous frame (40 % of voids) and a dense frame (60 % of voids), a predictive assessment of their heat–shielding and mechanical properties was carried out. Calculations of the equivalent coefficient of thermal conductivity of models based on Fourier’s law established that with a decrease in the voidness of products with a porous wall, the coefficient of their thermal conductivity decreases by 12 %, which improves the heat–shielding properties. Based on the results of computer simulation of the behavior of models under the influence of static power loads, it was determined that porosity of the ceramic framework of products leads to degradation of mechanical strength almost proportionally to a decrease in voidness. The stress–strain state of 3D models of ceramic structures with different pore geometry (spherical, globular, ellipsoidal) is analyzed and it is shown that stresses are concentrated in the contact zones of a ceramic matrix with pores. It is shown that the most durable is the structural model with spherical pores. The expediency of organizing such a structure, the need to strengthen the ceramic matrix of materials and zones surrounding the pores, as the most vulnerable structural sites, is shown. The results of predictive calculations have been experimentally confirmed in the development of technology for structural and heat–insulating composite–type ceramic materials based on low–melting loam and ash microspheres, which provide a given structural picture of the ceramic material.

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Probabilistic interval estimation of design floods under non-stationary conditions by an integrated approach

Hydrology Research ◽

10.2166/nh.2021.007 ◽

2021 ◽

Author(s):

Yanlai Zhou ◽

Shenglian Guo ◽

Chong-Yu Xu ◽

Lihua Xiong ◽

Hua Chen ◽

...

Keyword(s):

Frequency Analysis ◽

Additive Model ◽

Interval Estimation ◽

Integrated Approach ◽

Copula Function ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Time Varying ◽

Joint Distributions ◽

Design Floods

Abstract Quantifying the uncertainty of non-stationary flood frequency analysis is very crucial and beneficial for planning and design of water engineering projects, which is fundamentally challenging especially in the presence of high climate variability and reservoir regulation. This study proposed an integrated approach that combined the Generalized Additive Model for Location, Scale and Shape parameters (GAMLSS) method, the Copula function and the Bayesian Uncertainty Processor (BUP) technique to make reliable probabilistic interval estimations of design floods. The reliability and applicability of the proposed approach were assessed by flood datasets collected from two hydrological monitoring stations located in the Hanjiang River of China. The precipitation and the reservoir index were selected as the explanatory variables for modeling the time-varying parameters of marginal and joint distributions using long-term (1954–2018) observed datasets. First, the GAMLSS method was employed to model and fit the time-varying characteristics of parameters in marginal and joint distributions. Second, the Copula function was employed to execute the point estimations of non-stationary design floods. Finally, the BUP technique was employed to perform the interval estimations of design floods based on the point estimations obtained from the Copula function. The results demonstrated that the proposed approach can provide reliable probabilistic interval estimations of design floods meanwhile reducing the uncertainty of non-stationary flood frequency analysis. Consequently, the integrated approach is a promising way to offer an indication on how design values can be estimated in a high-dimensional problem.

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Measurement of radial thermal conductivity of a cylinder using a time-varying heat flux method

International Journal of Thermal Sciences ◽

10.1016/j.ijthermalsci.2018.03.008 ◽

2018 ◽

Vol 129 ◽

pp. 301-308 ◽

Cited By ~ 4

Author(s):

Muhammad B. Ahmed ◽

Salwa Shaik ◽

Ankur Jain

Keyword(s):

Thermal Conductivity ◽

Heat Flux ◽

Time Varying ◽

Flux Method

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Integrated Approach for Detecting Convection Effects in Geothermal Environments Based on TIR Camera Measurements

Applied Sciences ◽

10.3390/app11073185 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3185

Author(s):

Susana Del Pozo ◽

Cristina Sáez-Blázquez ◽

Ignacio Martín Nieto ◽

Susana Lagüela

Keyword(s):

Thermal Conductivity ◽

Infrared Camera ◽

Heat Rate ◽

Thermal Characterization ◽

Thermal Infrared ◽

Integrated Approach ◽

Geothermal System ◽

Radiometric Calibration ◽

Geothermal Systems

Thermal characterization of soils is essential for many applications, including design of geothermal systems. Traditional devices focus on the computation of thermal conductivity, omitting the analysis of the convection effect, which is important for horizontal geothermal systems. In this paper, a procedure based on the monitoring of the surface of the soil with a thermal infrared (TIR) camera is developed for the evaluation of the global thermal imbalance on the surface and in-depth. This procedure allows for the computation of thermal conductivity and global convection heat rate, consequently constituting a complete thermal characterization of the geothermal system. The validation of the results is performed through the evaluation of the radiometric calibration of the thermal infrared camera used for the monitoring and the comparison of the thermal conductivity values obtained in-depth, with traditional methods, and for the surface of the system.

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Effective thermal conductivity of particulate composites for time-varying temperature fields

International Journal of Engineering Science ◽

10.1016/j.ijengsci.2021.103492 ◽

2021 ◽

Vol 164 ◽

pp. 103492

Author(s):

S. Kanaun ◽

V. Levin

Keyword(s):

Thermal Conductivity ◽

Effective Thermal Conductivity ◽

Temperature Fields ◽

Particulate Composites ◽

Time Varying

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Accurate thermal conductivity measurement of Java and Sumatra rock samples using time varying heat flow measurement

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/311/1/012045 ◽

2019 ◽

Vol 311 ◽

pp. 012045

Author(s):

Bagus Endar B. Nurhandoko ◽

Rizal Kurniadi ◽

Sri Widowati ◽

Susilowati ◽

Rio K. Martha ◽

...

Keyword(s):

Thermal Conductivity ◽

Heat Flow ◽

Flow Measurement ◽

Conductivity Measurement ◽

Thermal Conductivity Measurement ◽

Time Varying ◽

Rock Samples

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Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

SAGE Open ◽

10.1177/21582440211005758 ◽

2021 ◽

Vol 11 (1) ◽

pp. 215824402110057

Author(s):

Fahim Afzal ◽

Pan Haiying ◽

Farman Afzal ◽

Asif Mahmood ◽

Amir Ikram

Keyword(s):

At Risk ◽

Stock Returns ◽

Stock Markets ◽

Value At Risk ◽

Stock Exchange ◽

Integrated Approach ◽

Actual Behavior ◽

Time Varying ◽

Conditional Correlation ◽

Dynamic Conditional Correlation

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

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