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Stability Analysis of Unsteady Oriented Magneto-Convective Porous Medium: Exploring Boundary-Layer Flow Dynamics Through regression modeling

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HEAT TRANSFER 2025年

作者： Uddin, Mohammed Jahir Nasrin, Rehena Alatawi, Eid S. Bangladesh Univ Engn & Technol Dept Math Dhaka Bangladesh Feni Govt Coll Dept Math Feni Bangladesh Univ Tabuk Fac Engn Dept Mech Engn Tabuk Saudi Arabia

Exploring flow stability in porous media, the consequence of magnetic fields on heat transfer (HT), the influence of inclination on flow, and optimizing industrial cooling systems are crucial. This study explores the stability of magneto-convective flow in unsteady porous media, focusing on orientation effects and the impact of boundary layer (BL) conditions on flow behavior and heat transmission while developing regression models to predict these dynamics. We explore nonlinear, time-varying partial differential equations (PDEs) that govern mass conservation, momentum, energy, and concentration, making relevant adjustments as required. A comprehensive numerical framework is developed to address these governing equations, employing a finite difference method (FDM) for spatial discretization and an implicit approach for time integration. Through stability analysis, we assess the flow behavior under diverse conditions, elucidating the critical parameters influencing flow stability and transitions. Furthermore, an extensive investigation is undertaken to establish a suitable steady-state condition and to ensure uniform meshing throughout the process. regression analysis is applied to elucidate the relationships between the key factors. This study examines the consequence of several physical factors on the distribution of velocity, temperature, and concentration within the system. The findings indicate that increasing the mass Grashof number significantly enhances buoyancy-driven convection, while an inclined magnetic field profoundly modifies the flow dynamics and thermal profiles. The newly developed two linear regression models of multiple variables have 95.25% and 98.49% correlation coefficients for mean Nusselt number and shear stress, respectively. The study's originality lies in its detailed examination of how these parameters interact to impact inclined magnetic field convection flows. This comprehensive understanding may facilitate more accurate predictive model

关键词： boundary layer EFDM inclined magnetic field porous medium regression modeling

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regression modeling and its Application to the Problem of Prediction of Space Weather

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JOURNAL OF AUTOMATION AND INFORMATION SCIENCES 2009年第5期41卷 61-69页

作者： Parnowskiy, A. S. Natl Acad Sci Ukraine Inst Space Res Kiev Ukraine Natl Space Agcy Ukraine Kiev Ukraine

A new method for simulation of nonlinear discrete dynamical systems with unknown dimension on the example of the problem of forecasting space weather. The proposed method allows to predict the system's output several steps ahead, determine its dynamic characteristics, as well as identify significant and insignificant input parameters, which leads to the construction of the phenomenological model of the system.

关键词： regression modeling forecasting space weather nonlinear discrete dynamic unknown dimension construction of the phenomenological model

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regression modeling and quality prediction for multiphase batch processes with inner-phase analysis

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 135卷 1-16页

作者： Zhao, Luping Zhao, Chunhui Gao, Furong Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Hong Kong Hong Kong Peoples R China Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Fok Ying Tung Grad Sch Hong Kong Hong Kong Peoples R China

Many batch processes have multiple phases which may exhibit significantly different underlying behaviors. Besides, within each phase, processes in general evolve following certain underlying rules, called inner-phase evolution here. In this paper, a new statistical process analysis and quality prediction method is proposed for multiphase batch processes. A two-level phase division algorithm is proposed to capture the changes of relationship between process variables and quality variables within each phase. It reveals that the quality-related inner-phase evolutions in general goes through three statuses sequentially, i.e., transition, steady-phase and transition. Partial least squares (PLS), canonical correlation analysis (CCA) and qualitative trend analysis (QTA) are effectively combined to distinguish different inner-phase process statuses. Their different characteristics are then analyzed respectively for regression modeling and quality analysis. Meanwhile, the uneven-length problem of batch processes caused by operation conditions is handled properly according to their different characteristics in each inner-phase parts. Cumulative effect is considered and modeled both within inner-phase parts and between inner-phase parts for quality perdition. During online application, different quality-related process behaviors within each phase are tracked, revealing the inner-phase evolution. Online quality prediction is performed at each time by adopting different regression models. The application to a typical multiphase batch process, injection molding, illustrates the feasibility and performance of the proposed algorithm for uneven-length batch group quality prediction. (C) 2014 Elsevier B.V. All rights reserved.

关键词： regression modeling Quality prediction Multiphase batch process Inner-phase evolution Uneven-length problem

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regression modeling of welded joint quality in gas metal arc welding process using acoustic and electrical signals

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2015年第10期229卷 1711-1721页

作者： Shahabi, Hamid Kolahan, Farhad Ferdowsi Univ Mashhad Dept Mech Engn Mashhad Islamic Iran

In any welding process, the quality of joints is significantly affected by the bead geometry as well as the presence of welding defects. One promising approach is to utilize the features of the welding signals such as voltage, current and sound to monitor and predict the quality of the welded joints. In this work, for gas metal arc welding, a regression modeling has been proposed to predict two main weld quality measures, namely, welding defects and bead shape factor. The proposed approach is based on the features of welding electrical and sound signals. The weld quality characteristics are defined in terms of weld defects (discontinuity, lack of fusion and overlap) as well as weld shape factor. These measures are modeled using statistical parameters of weld sound and electrical signals. To develop the models, 13 statistical parameters of the signals have been employed. To gather the required data, a total of 57 experiments have been performed based on the optimal determinant method (D-optimal) of design of experiments approach. The curvilinear function has proved to have the best fit for the process under consideration. The proposed models are validated through further experiments. Comparisons between the experimental measurements and the predicted values indicate that the developed models are quite accurate in predicting the actual welding process in terms of the two main weld quality indices. The proposed approach may be used to adjust the input parameters in such a way that the desired bead geometry is obtained while the welding defects are minimized.

关键词： Gas metal arc welding welding output signals weld defects regression modeling design of experiments

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regression modeling of the Radial Increment Dynamics in Scots Pine in Forests Contaminated with Radionuclides in Bryansk Oblast

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CONTEMPORARY PROBLEMS OF ECOLOGY 2017年第7期10卷 784-789页

作者： Belov, A. A. All Russian Res Inst Silviculture & Mechanizat Fo Pushchino 141202 Moscow Oblast Russia

We study the dynamics of radial increment in Scots pine in the forests of Bryansk oblast contaminated with radionuclides after the Chernobyl disaster. We have developed regression models of the relationship between specific soil activity and the radial increment of trees for the transition stage of the disaster in 1987-1989 and four 5-year-long periods from 1990 to 2009, characterizing 20 years of the recovery period. We have found a strong positive correlation between the radial increment and specific soil activity. The correlation coefficients vary from 0.695 +/- 0.198 to 0.895 +/- 0.124 for early increment, and from 0.549 +/- 0.231 to 0.937 +/- 0.097 for late increment in different time periods. It is found that the cubic parabolic equation is the optimal statistical description of the relationship between the radial increment and concentration of radiocesium in soil. The minimal effective specific soil activity for the stimulation of radioactivity decreased from 25-26 kBq kg(-1) in 1987-1989 to 9-10 kBq kg(-1) in 2004-2009. The same value for the late increment varied from 20-21 to 16-17 kBq kg(-1) in the same periods. The maximal effective specific soil activity indicative of the highest effect of radiation decreased consequently from 110-120 kBq kg(-1) in 1987-1989 to 60-70 kBq kg(-1) in 2004-2009 for early increment. The same value for late increment varied in the same range with no tendencies in the same periods. Based on the model of radial increment dynamics, we assessed the role of radiation in the elevation of standing volume in a typical pine forest in the study area 25 years after the Chernobyl disaster.

关键词： Scots pine radial increment soil contamination with radiocesium exposure-effect relationship regression modeling

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regression modeling of Surface Roughness in Grinding

Regression Modeling of Surface Roughness in Grinding

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International Conference on Advanced Materials and Information Technology Processing (AMITP 2011)

作者： Asilturk, Ilhan Celik, Levent Canli, Eyub Onal, Gurol Selcuk Univ Tech Educ Fac TR-42075 Konya Turkey Selcuk Univ Fac Engn TR-42075 Konya Turkey

ISBN: (纸本)9783037851579

Grinding is a widely used manufacturing method in state of art industry. By realizing needs of manufacturers, grinding parameters must be carefully selected in order to maintain an optimum point for sustainable process. Surface roughness is generally accepted as an important indicator for grinding parameters. In this study, effects of grinding parameters to surface roughness were experimentally and statistically investigated. A complete factorial experimental flow was designed for three level and three variable. 62 HRC AISI 8620 cementation steel was used in grinding process with 95-96% Al2O3 grinding wheel. Surface roughness values (Ra, Rz) were measured at the end of process by using depth of cut, feed rate and workpiece speed as input parameters. Experimental results were used for modeling surface roughness values with linear, quadric and logarithmic regressions by the help of MINITAB 14 and SPSS 16 software. The best results according to comparison of models considering determination coefficient were achieved with quadric regression model (84.6% for Ra and 89% for Rz). As a result, a reliable model was developed in grinding process which is a highly complex machining operation and depth of cut was determined as the most effective parameter on grinding by variance analysis (ANOVA). Obtained theoretical and practical acquisitions can be used in various areas of manufacturing sector in the future.

关键词： CNC grinding surface roughness regression modeling grinding parameters

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regression modeling of reduction in spatial accuracy and detail for multiple geometric line simplification procedures

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INTERNATIONAL JOURNAL OF CARTOGRAPHY 2020年第1期6卷 47-70页

作者： Samsonov, Timofey Yakimova, Olga Lomonosov Moscow State Univ Fac Geog Dept Cartog & Geoinformat Moscow Russia Demidov Yaroslavl State Univ Fac Math Dept Comp Secur & Math Methods Informat Proc Yaroslavl Russia

One of the important stages of map generalization is the selection of optimal simplification procedure for each spatial feature or feature type. Selected algorithms are then applied collaboratively to simplify the whole set of features. However, there is a lack of investigations that report a systematic approach of deriving a similar reduction in accuracy and detail by using different algorithms. In current paper we propose the solution to this problem on the basis of regression modeling between tolerance value of each algorithm and the value of some geometric measure which describes changes in accuracy and detail of the line. This allows fitting the regression model between tolerance values of the two selected algorithms which can be used to obtain similar simplification results. regressions between Douglas-Peucker, Li-Openshaw and Visvalingam-Whyatt algorithm tolerance values are investigated. Application of methodology is illustrated on the example of three coastlines with significantly different spatial character. Results of the study show that regression coefficients depend highly both on the combination of the two algorithms, and on the character of the line. Finally, it is shown that a weighted combination of accuracy and detail regression models can be used to model the changes in level of detail of the line. RESUMEUne des etapes importante de la generalisation est le choix d'un processus optimal de simplification pour chaque objet spatial ou chaque type d'objet. Les algorithmes selectionnes sont alors appliques de facon collaborative pour simplifier l'ensemble des objets. Pourtant, peu de recherches portent sur une approche systematique pour obtenir un changement de precision et de niveau de detail identique en utilisant differents algorithmes. Dans ce papier nous proposons une solution a ce probleme en se basant sur une modelisation par regression entre la valeur de tolerance de chaque algorithme et la valeur d'une mesure geometrique decrivant les changeme

关键词： Geometric line simplification algorithms level of detail regression modeling

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A Robust Extreme Learning Machine Based on Adaptive Loss Function for regression modeling

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NEURAL PROCESSING LETTERS 2023年第8期55卷 10589-10612页

作者： Zhang, Fangkun Chen, Shuobo Hong, Zhenqu Shan, Baoming Xu, Qilei Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China

The extreme learning machine (ELM) algorithm is advantageous to regression modeling owing to its simple structure, fast computation, and good generalization performance. However, the existing ELM algorithm uses an l(2)-norm loss function, which is sensitive to outliers and has low robustness. In addition, some existing robust loss functions are not sufficiently flexible to accurately estimate the relationship between sample points and loss values, resulting in unsatisfactory ELM performance. To address these problems, this study established a robust ELM (ALFELM) algorithm. First, an adaptive loss function with two tunable hyperparameters was introduced;the function can be transformed into several robust loss functions by varying the parameters. It overcomes the limitations of fixed robust loss functions. Then, the Bayesian optimization strategy was used to determine the optimal parameters of the loss function. Furthermore, the classical iterative reweighted least squares method was used to solve for output weights, with a weight function corresponding to the loss function and a regularization parameter to prevent overfitting. Finally, the proposed method was tested using several artificial and benchmark datasets, and its effectiveness was verified for a real engineering case. The results indicated that the proposed ALFELM algorithm is more robust and accurate compared with other methods, especially for a large number of outliers. In addition, the algorithm can be used to establish effective regression models for actual processes.

关键词： regression modeling Extreme learning machine Adaptive loss function Bayesian optimization Iterative reweighted least squares

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A Quality-Relevant Sequential Phase Partition Approach for regression modeling and Quality Prediction Analysis in Manufacturing Processes

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第4期11卷 983-991页

作者： Zhao, Chunhui Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

Competition and demand for consistent and high-quality product have spurred the development of quality prediction methods for industrial manufacturing processes. Multiplicity of phases is, in general, common nature of many batch manufacturing processes. Considering that different phases may have different effects on qualities, one of the key issues is how to partition the whole batch process into multiple phases. In the present work, an automatic quality-relevant step-wise sequential phase partition (QSSPP) algorithm is developed for phase-based regression modeling and quality prediction. It considers the time sequence of operation phases and can capture the time-varying quality prediction relationships. Using this algorithm, phases are separated in order from quality-relevant perspective, revealing different quality prediction relationships. The phase-based regression system is set up for online quality prediction and the online prediction results are quantitatively evaluated for each phase. The feasibility and performance of the proposed algorithm are illustrated by an important manufacturing process, injection molding.

关键词： Cumulative analysis multiphase batch processes quality prediction regression modeling sequential phase partition

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A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay watershed

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2003年第1-3期81卷 73-84页

作者： Brakebill, JW Preston, SD US Geol Survey Baltimore MD USA US Geol Survey Chesapeake Bay Program Annapolis MD USA

The U. S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced regressions On Watershed attributes ( SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, pointsources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D. C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U. S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow deriv

关键词： SPARROW Chesapeake Bay nutrients loads regression modeling reach stream RF1 DEM

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