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190 articles for “nonlinear modelling”
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Modelling of Large Elasto-plastic Deformations by EFGM
Abstract: Current work reports modelling and simulation of geometric and material nonlinearities arising due large elasto-plastic displacements in structural specimens by invoking enriched element free Galerkin method (EFGM). The displacement approximations are constructed by using moving least square approach. Standard displacement based approximations are modified by incorporating suitable enrichment functions depending on the nature of interfaces present in the components. Large deformations give rise to geometric nonlinearities which have been modelled …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 130–143 Read article
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The Strength of Dry, Fresh and Decomposed Raffia Palm Trunk in the Bioremediation of Oil-based Drill Cutting
Abstract: In this research, the impact of dry drill cuttings and compost tea on the environment's ability to degrade oil-based drill cutting contamination is investigated. The experiment was conducted in the research center located at the workshop of Agricultural and Environmental Engineering Department, Rivers State University, Port Harcourt. Oil-based drill cutting samples were placed in bulk in eleven reactors (T1, T2, T3-T11) with four replications. The original drill cuttings' physiochemical characteristics …
Published in Journal of Modern Chemistry & Chemical Technology · Vol. 15, Issue 3, 2024 · pp. 26–33 Read article
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Investigations on Piezoactuator-Based Active Vibration Isolation for Tool-Based Micromachining Applications
Abstract: Stacked piezoactuators are suitable actuators for active vibration isolation systems due to their high stiffness and fast response. However, they exhibit hysteresis nonlinearity between applied voltage and the resulting displacement. In this paper, Bouc-Wen model is used to model the hysteresis nonlinearity of stacked piezoactuator. The model parameters are estimated from the experimental voltage-displacement data of a stacked piezoactuator using “Trust-region nonlinear least square method” in MATLAB Simulink. The resulting …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 1, 2015 · pp. 41–49 Read article
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The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks
Abstract: Nowadays, nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 75–90 Read article
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Estimation of Burger Model Parameters by Inverse Analysis of Oedometer Data
Abstract: Contrary to the forward analysis, inverse analysis can be effectively utilized to determine the parameters of a constitutive model commonly used to represent the stress-strain-time behavior or the load-deformation-time behavior of the soil. This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by formulating the problem as that of mathematical programming in terms of identification …
Published in Recent Trends in Civil Engineering & Technology · Vol. 2, Issue 1-3, 2012 · pp. 37–54 Read article
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Development and Simulation of Non-Linear Gear Dynamics Model
Abstract: With the development of high-speed mechanical transmission, the dynamic characteristics of gear transmission require continuous improvement and more accurate prediction of the gear system dynamics becomes more and more important. The literature revealed that the gear pair transmission system is inherently non-linear which exhibits complex behavior and it is significant to study its vibration characteristics in detail and to analyze the influencing factors. A nonlinear dynamic model of gear transmission …
Published in Journal of Aerospace Engineering & Technology · Vol. 3, Issue 2, 2013 · pp. 11–16 Read article
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A Novel Software Cost Estimation Model Based on the Hybrid of Artificial Neural Network and Firefly Algorithm
Abstract: Software cost estimation is the prediction of software development effort and software development time required to develop a software project. Software cost estimation is a basic and important task for both successful execution of software development life cycle and its management interms of its cost & time. Accurate software cost estimation is considered to be a difficult task as the information about the software project to be developed at the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 38–45 Read article
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POLYMER AND COMPOSITE-BASED GEOSYNTHETIC REINFORCEMENTS FOR SEISMIC STABILITY OF SOIL RETAINING STRUCTURES: MATERIALS, MECHANICS, AND PERFORMANCE REVIEW
Abstract: Geosynthetic materials based on polymer and composites have become important items for the structural performance and seismic resilience of the reinforced soil retaining systems. Mechanically stabilized earth walls in recent geotechnical engineering practice are increasingly based on enhanced polymeric reinforcements for enhanced tensile strength, durability, flexibility, and energy dissipation under dynamic loading. High-density polyethylene, polypropylene, polyester, and fiber-reinforced polymer composites are usually used. The present review focus on the recent …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Artificial Intelligence for Polymer and Nanocomposite Materials: Performance Prediction, Manufacturing Optimization, and Future Perspectives
Abstract: The exceptional mechanical properties, design flexibility, and lightweight nature of polymer composite and nanocomposite materials make them indispensable in a wide range of applications, including aerospace, automotive, construction, biomedical, and energy sectors. The optimization of the strength, durability, and manufacturing efficiency of polymer composite and nanocomposite materials is highly challenging because their performance depends on matrix composition, reinforcement type, fiber or nanoparticle distribution, interfacial interactions, processing conditions, and environmental factors. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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AI-Optimized Nano-Silica Reinforced PCM Composites for Predictive Solar-Thermal Energy Storage Networks
Abstract: This study presents an AI-optimized nano-silica reinforced polymer composite phase change material (PCM) for predictive solar-thermal energy storage networks. The proposed composite combines paraffin wax, high-density polyethylene (HDPE), and uniformly dispersed nano-silica particles to improve thermal conductivity, structural stability, leakage resistance, and long-term cycling performance. The composite was fabricated through melt blending and ultrasonication-assisted nanoparticle dispersion, followed by comprehensive morphological, chemical, thermal, and thermophysical characterization using scanning electron microscopy (SEM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Effect of Risk Priority Number Values for Multiple Effects in Maintenance Optimization
Abstract: In this paper, authors describe an optimization framework for finding optimal maintenance policies in continuous process operations and analyze and quantify the reaction of different variables on optimal maintenance schedules. A necessary idea behind maintenance optimization is that of life-cycle costing, where the total cost of design, building, maintenance, is considered over the entire life span of the system or structure in question. The system’s effectiveness measure is introduced which …
Published in Trends in Mechanical Engineering & Technology · Vol. 2, Issue 2, 2012 · pp. 1–5 Read article
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Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling
Abstract: This study proposes a novel Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling (PADT-NO) framework for predictive modelling of complex, nonlinear, and multiscale fluid flows. The proposed mathematical framework integrates fundamental conservation laws, Navier–Stokes dynamics, physics-constrained neural operators, adaptive reduced-order modelling, and uncertainty-aware state estimation within a unified computational architecture. Unlike conventional computational fluid dynamics and purely data-driven approaches, the proposed model dynamically couples high-fidelity physical information with a low-dimensional latent …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 · pp. 89–103 Read article
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Microvita‑Inspired Informational Field Dynamics as a Nonlinear Signal‑Generation Mechanism in Matter–Life–Mind Systems
Abstract: Recognizing how matter, life, and consciousness relate to one another continues to be among the most essential challenges faced by modern science. Contemporary physical theories successfully describe the behavior of elementary particles and large-scale cosmological structures, yet they do not fully explain the emergence of informational complexity and organized patterns observed in biological and cognitive systems. This study proposes a theoretical framework in which Microvita are interpreted as subtle informational …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 · pp. 34–49 Read article
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Predictive Modeling of Polymer Elasticity and Mechanical Strength Using Deep Learning for Functional Polymer Material Characterization and Performance Assessment
Abstract: Knowing the polymers elasticity and mechanical strength is essential in the characterization of functional polymer materials, for rapid characterization and to avoid extensive experiments. Traditional polymer characterization methods are often time consuming and resource intensive and the mechanical response of polymers can be nonlinear, thus complicating the prediction of the properties. These limitations can be overcome by introducing a Variational Temporal Fusion Transformer (VaTFT) for the deep learning prediction of …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Speed Estimation of High-Speed Hysteresis Motor Based on Extended Kalman Filter
Abstract: High-speed hysteresis motors are used in special industries such as medical centrifuges and gyroscopes. In spite of that, they are synchronous motors in some situations they may work in asynchronous speed. So, for reliable performance of hysteresis motor, closed loop control strategies are employed. On this way, speed/position of rotor must be known. Due to difficulty of using the speed sensors in high-speed hysteresis motor drives, employing the speed estimation …
Published in Journal of Power Electronics and Power Systems · Vol. 6, Issue 1, 2016 · pp. 34–44 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Motors Using a Deep Learning-Based Torque Control with Torque Ripple Reduction under Nonlinear Magnetic Conditions
Abstract: This research discusses a deep learning strategy for torque management to minimize the effect of torque ripple in a nonlinear electric motor. Nonlinear electric motor losses may include: magnetic saturation, harmonic flux losses and inverter losses. In many cases when the system parameters deviate and/or instability issues occur, the traditional method with a model-based approach or PI control may encounter challenges. In this case, the authors proposed a hybrid approach …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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Radial Basis Function Neural Networks for Rainfall-Runoff Modeling
Abstract: Rainfall-runoff process is purely nonlinear and varies spatially as well as temporally. Any hydrological model requires many parameters which represent different components of the process. Availability of all the parameters is difficult for any catchment and probabilistic generation of such type of data is impossible. Under such circumstances, artificial neural networks (ANNs) have proven to be a better tool to model the rainfall-runoff process with minimum available data. The present …
Published in Journal of Water Resource Engineering and Management · Vol. 1, Issue 2, 2014 · pp. 11–18 Read article
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State Space Modeling and Robust Wind Power Generation System
Abstract: AbstractAs producing power from renewable energy is increasing, facing new problems emerging from this phenomenon is becoming a good challenge for power system engineers. One of these problems is the dynamic and transient stability of such systems when they are connected to a power network. In this paper, a variable speed cage machine wind generation system is considered as the case study. The concept of power system stabilizer, which has …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 8, Issue 2, 2017 · pp. 24–39 Read article
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Fractional Calculus-Based Analysis of Magneto-Thermal Nanofluid Flow Over Stretching and Shrinking Surfaces
Abstract: This study presents a comprehensive investigation of magneto-thermal hybrid nanofluid flow over stretching and shrinking surfaces using a fractional calculus framework to capture the memory and hereditary characteristics of complex fluid transport phenomena. The proposed model incorporates the effects of magnetic field intensity, thermal radiation, viscous dissipation, Brownian motion, and thermophoretic diffusion on the velocity and temperature distributions within the boundary layer region. A fractional-order derivative formulation is employed to …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 · pp. 1–10 Read article