Determination Of Model Parameters Research Articles

Lifetime assessment of deep-sea manned cabin with PMMA pressure-resistant hull prior to micro-cracks

Fully-transparent cabin type, utilized in manned submersibles, is typically constructed using viscoelastic PMMA (polymethyl methacrylate) material. Accurate fatigue life assessment of manned cabin is of paramount importance to ensure structural safety. In this study, a calculation process for evaluating the fatigue life of PMMA manned cabins based on a continuum damage mechanics model combined with the viscoelastic constitutive relationship of the PMMA material in response to the absence of related specification is proposed. Fatigue tests of PMMA material are performed for determination of damage evolution model parameters and the calculation process is validated by the fatigue test data. Tailored subroutines are then developed by using ABAQUS software tool, respectively for structural analysis of a typical manned cabin with PMMA pressure-resistant hull, openings, hatches and penetration plates and simulating trapezoidal loading history acting on manned cabin. Subroutine coupling is accordingly employed to conduct parametric analysis on fatigue crack initiation life of the fully-transparent manned cabin with consideration of different outside pressure level and operation time and valuable results are obtained for identifying key areas of concern. This study intends to contribute to further cabins safety enhancement, as well as to design optimization and fatigue analysis of underwater structures involving PMMA materials.

Crystal plasticity modeling and analysis for the transition from intergranular to transgranular failure in nickel-based alloy Inconel 740H at elevated temperature

The precipitation-strengthened Nickel-based alloy Inconel® 740H® (IN740H) exhibits increased ductility at higher applied strain rates during quasi-static tensile tests at an elevated temperature of 760 °C. The examination of fracture surfaces in this context reveals a noteworthy transition of underlying fracture mechanisms from transgranular to intergranular fracture as the applied strain rate decreases from 1×10−3/s to 0.83×10−4/s. To thoroughly understand the mechanical response of IN740H under these conditions, this study develops a crystal plasticity finite element (CPFE) model. This model incorporates various deformation mechanisms including dislocation slips, climb, and grain boundary sliding, which are relevant to the test conditions. The model is calibrated using data from both tensile tests at different strain rates and creep tests across a broad stress range at 760 °C, enabling the accurate determination of model parameters for each mechanism. Simulation results well captured the experimental observations of different failure modes. At higher strain rates, the model shows a dominance of dislocation slip leading to heterogeneous plastic deformation and formation of transgranular shear bands causing the failure, while at lower strain rates, an increased activity of grain boundary sliding causes grain boundaries crack leading to intergranular failure.

Modeling of the resensitization effect on carbon-ion radiotherapy for stage I non-small cell lung cancer

Objective. To investigate the effect of redistribution and reoxygenation on the 3-year tumor control probability (TCP) of patients with stage I non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy. Approach. A meta-analysis of published clinical data of 233 NSCLC patients treated by carbon-ion radiotherapy under 18-, 9-, 4-, and single-fraction schedules was conducted. The linear-quadratic (LQ)-based cell-survival model incorporating the radiobiological 5Rs, radiosensitivity, repopulation, repair, redistribution, and reoxygenation, was developed to reproduce the clinical TCP data. Redistribution and reoxygenation were regarded together as a single phenomenon and termed ‘resensitization’ in the model. The optimum interval time between fractions was investigated for each fraction schedule using the determined model parameters. Main results. The clinical TCP data for 18-, 9-, and 4-fraction schedules were reasonably reproduced by the model without the resensitization effect, whereas its incorporation was essential to reproduce the TCP data for all fraction schedules including the single fraction. The curative dose for the single-fraction schedule was estimated to be 49.0 Gy (RBE), which corresponds to the clinically adopted dose prescription of 50.0 Gy (RBE). For 18-, 9-, and 4-fraction schedules, a 2-to-3-day interval is required to maximize the resensitization effect during the time interval. In contrast, the single-fraction schedule cannot benefit from the resensitization effect, and the shorter treatment time is preferable to reduce the effect of sub-lethal damage repair during the treatment. Significance. The LQ-based cell-survival model incorporating the radiobiological 5Rs was developed and used to evaluate the effect of the resensitization on clinical results of NSCLC patients treated with hypo-fractionated carbon-ion radiotherapy. The incorporation of the resensitization into the cell-survival model improves the reproducibility to the clinical TCP data. A shorter treatment time is preferable in the single-fraction schedule, while a 2-to-3-day interval between fractions is preferable in the multi-fraction schedules for effective treatments.

Minimization of a ship's magnetic signature under external field conditions using a multi-dipole model

The paper addresses the innovative issue of minimizing the ship's magnetic signature under any external field conditions, i.e., for arbitrary values of ambient field modulus and magnetic inclination. Varying values of the external field, depending on the current geographical location, affect only the induced part of ship's magnetization. A practical problem in minimizing the ship signature is separating permanent magnetization from induced magnetization. When the ship position changes, a signature measurement has to be made under new magnetic field conditions to update the currents in the coils. This is impractical or even difficult to do (due to the need for a measuring ground), so there is a need to predict the ship's magnetization value in arbitrary geographical location conditions based on the reference signature determined on the measuring ground. In particular, the model predicting the signatures at a new geographical location must be able to separate the two types of magnetization, as permanent magnetization is independent of external conditions. In this paper, a FEM model of the vessel is first embedded in an external field and permanent magnetization is simulated using DC coils placed inside the model. Then, using the previously developed rules for data acquisition and determination of model parameters, a multi-dipole model is synthesized in which the induced and permanent parts are separated. The multi-dipole model thus developed has been successfully confronted with the initial model in FEM environment. The separation of permanent and induced magnetization allows the latter to be scaled according to new values of the external field. In the paper, the situation of determining a signature at one geographical position and its projection onto two other positions is analyzed. Having determined the signature with a high degree of accuracy anywhere in the world, it is possible to perform classical signature minimization by determining DC currents in coils placed inside the ship's hull. The paper also analyzes the effectiveness of ship's signature minimization and the influence of ship's course on the signature value. The advantage of the method presented in this paper is an integrated approach to the issue of scaling and minimization of ship magnetic signature, which has not been presented in the literature on such a scale before.

Three-Water Differential Parallel Conductivity Saturation Model of Low-Permeability Tight Oil and Gas Reservoirs

Addressing the poor performance of existing logging saturation models in low-permeability tight sandstone reservoirs and the challenges in determining model parameters, this study investigates the pore structure and fluid occurrence state of such reservoirs through petrophysical experiments and digital rock visualization simulations. The aim is to uncover new insights into fluid occurrence state and electrical conduction properties and subsequently develop a low-permeability tight sandstone reservoir saturation model with easily determinable parameters. This model is suitable for practical oilfield exploration and development applications with high evaluation accuracy. The research findings reveal that such reservoirs comprise three types of formation water: strongly bound water, weakly bound water, and free water. These types are found in non-connected micropores, poorly connected mesopores where fluid flow occurs when the pressure differential exceeds the critical value, and well-connected macropores. Furthermore, the three types of formation water demonstrate variations in their electrical conduction contributions. By inversely solving rock electrical experiment data, it was determined that for a single sample, the overall cementation index is the highest, followed by the cementation index of pore throats containing strongly bound water, and the lowest for the pore throats with free water. Building on the aforementioned insights, this study develops a parallel electrical pore cementation index term, ϕm′, to account for the differences among the three types of water and introduces a parallel electrical saturation model suitable for logging evaluation of low-permeability tight oil and gas reservoirs. This model demonstrated positive application effects in the logging evaluation of low-permeability tight gas reservoirs in a specific basin in the Chinese offshore area, thereby confirming the advantages of its application.

An Algorithm for Soft Sensor Development for a Class of Processes with Distinct Operating Conditions.

Soft sensors are increasingly being used to provide important information about production processes that is otherwise only available through off-line laboratory analysis. However, usually, they are developed for a specific application, for which thorough process analysis is performed to provide information for the appropriate selection of model type and model structure. Wide industrial application of soft sensors, however, requires a method for soft sensor development that has a high level of automatism and is applicable to a significant number of industrial processes. A class of processes that is very common in the industry are processes with distinct operating conditions. In this paper, an algorithm that is suitable for the development of soft sensors for this class of processes is presented. The algorithm possesses a high level of automatism, as it requires minimal user engagement regarding the structure of the model, which makes it suitable for implementation as a customary industrial solution. The algorithm is based on a radial basis function artificial neural network, and it enables the automatic selection of the model structure and the determination of model parameters, only based on the training data set. The testing of the presented algorithm is done on the cement production process, since it represents a process with distinct operating conditions. The results of the test show that, besides providing a high level of automatism in model development, the presented algorithm generates a soft sensor with high estimation performance.

Numerical investigations on constitutive model parameters of HRB400 and HTRB600 steel bars based on tensile and fatigue tests

Abstract In recent years, HRB400 and HTRB600 steel bars have become the mainstream standard reinforcing steel used in concrete structures in China. However, significant controversy still exists regarding the selection of material constitutive models and the determination of model parameters for buckling, fatigue, hysteresis, and other material characteristics. In this article, an automated process of multi-parameter calculation of the constitutive model for reinforcing steel – simulation accuracy evaluation of the constitutive model – selection of the constitutive model of reinforcing steel is established based on the hybrid programming method using MATLAB and OpenSees software. First, tensile and low-cycle fatigue tests were carried out on HRB400 and HTRB600 steel bars. Second, based on the constitutive model in OpenSees software and the skeleton curve and characteristics such as yielding, fatigue, and hysteresis, the constitutive model parameters of HRB400 and HTRB600 steel bars are determined using indirect and direct fitting methods. Finally, the five similarity parameters of the simulated normalized cumulative hysteretic energy dissipation coefficient are compared with the test results. The results indicate that the simulation accuracy of the Reinforcing Steel model exceeds 72%, which is higher than other four models, making it the best choice for reinforcing steel in numerical simulation.

Determination of progressive damage modelling parameters and calibration studies of 8-harness satin weave S-2 glass fibre reinforced composites

The aim of this article is to present a progressive damage modelling study of 8-harness satin weave S-2 glass fibre-reinforced SC15 epoxy matrix composites. Extensive mechanical properties of the composite were investigated throughout the study. The material was subjected to low-velocity impact (LVI) loading for investigating progressive damage. MAT162 material model in Ls-Dyna commercial finite element software was chosen for modelling damage that occurred in composite structure at the end of LVI test. MAT162 needs different material properties including elastic and strength properties with additional damage parameters. Different standard and non-standard tests were conducted to obtain these properties. Additionally, numerical simulation studies were run to calibrate these material properties. Finally, a set of calibrated material properties of 8HS-S2G/SC15 composite were obtained.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Determination of the two-compartment model parameters of exhaled HCN by fast negative photoionization mass spectrometry

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5–100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research.

Study of hydro-mechanical behaviours of rough rock fracture with shear dilatancy and asperities using shear-flow model

The geometric properties of fracture surfaces significantly influence shear-seepage in rock fractures, introducing complexities to fracture modelling. The present study focuses on the hydro-mechanical behaviours of rough rock fractures during shear-seepage processes to reveal how dilatancy and fracture asperities affect these phenomena. To achieve this, an improved shear-flow model (SFM) is proposed with the incorporation of dilatancy effect and asperities. In particular, shear dilatancy is accounted for in both the elastic and plastic stages, in contrast to some existing models that only consider it in the elastic stage. Depending on the computation approaches for the peak dilatancy angle, three different versions of the SFM are derived based on Mohr-Coulomb, joint roughness coefficient-joint compressive strength (JRC-JCS), and Grasselli's theories. Notably, this is a new attempt that utilizes Grasselli's model in shear-seepage analysis. An advanced parameter optimization method is introduced to accurately determine model parameters, addressing the issue of local optima inherent in some conventional methods. Then, model performance is evaluated against existing experimental results. The findings demonstrate that the SFM effectively reproduces the shear-seepage characteristics of rock fracture across a wide range of stress levels. Further sensitivity analysis reveals how dilatancy and asperity affect hydraulic properties. The relation between hydro-mechanical properties (dilatancy displacement and hydraulic conductivity) and asperity parameters is analysed. Several profound understandings of the shear-seepage process are obtained by exploring the phenomenon under various conditions.

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037 % and 0.336 %, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

Analysis of Transmission Line Models for PEMFC Impedance Modelling

The behaviour of Polymer Electrolyte Membrane Fuel Cells (PEMFC) shows a complex, non-linear dependence on material properties and operation conditions. Therefore, modelling and model parameterization by electrochemical characterization of PEMFCs is challenging. To deconvolute the different loss mechanisms in PEMFCs, impedance spectra are recorded and modelled by equivalent circuit models. Transmission Line Models (TLMs) are a well-established, physicochemical approach for modelling the impedance of PEMFC electrodes. TLMs consider the processes occurring in an electrode and the model parameters are directly related to material and microstructural characteristics. PEM electrodes have inhomogeneous distributions of the material, the current density, stored water, etc. Assuming a homogeneous electrode with homogeneous current distribution, the basic TLM shown in [1] turned during impedance fitting out to be inaccurate in operation conditions where these assumptions do not hold.To increase the fit accuracy, literature proposes a number of adapted TLMs. These TLMs have additional components and therefore degrees of freedom. This enables these models to take additional processes and the microstructure into consideration.In this contribution an incremental 1 cm² PEMFC ensuring zero gradient operation is characterized by means of impedance spectroscopy. Different operating conditions are varied within wide ranges and additional characterization methods, like H2/N2 operation were applied.Different TLMs proposed in literature are evaluated by fitting them to the measured spectra with and without considering the information gained from additional measurements. The DRT was applied as additional criteria in the fitting.In general, the accuracy of a fit increased with the number of free fitting parameters. Thus more complex TLMs accounting for additional physicochemical processes provide a lower error in the fit. Nevertheless the physicochemical meaningfulness of the determined model parameters is sometimes uncertain. It will be shown that for a meaningful parameterization by CNLS-fitting such TLMs require additional tests and characterization methods.[1] Heinzmann Marcel; Weber André; Ivers-Tiffée Ellen. „Impedance modelling of porous electorde structures in polymer electrolyte membrane fuel cells“. In: Journal of Power Sources (2019).

Entropy scaling framework for transport properties using molecular-based equations of state

Modeling transport properties is important for many applications. In this work, an entropy scaling framework for modeling transport properties using molecular-based equations of state (EOS) is presented. It can be applied for modeling the viscosity, thermal conductivity, and self-diffusion coefficients. The framework is formulated in a general way such that it can be coupled with various EOS. It is demonstrated that the model, when coupled to molecular-based EOS, provides not only good descriptions of existing data but also reasonable predictions in a wide range of states covering liquid, gaseous, supercritical, and meta-stable regions. Moreover, the model can be used for reliably predicting transport properties of mixtures. The universal parameters of the model were fitted to computer experiment data of the Lennard-Jones fluid. This procedure provides inherently a robust form of the basic scaling function. Thereby, only few data points are required for the determination of the component-specific model parameters. The applicability of the developed framework is demonstrated for model fluids as well as for a wide variety of real substances including non-polar, polar, and associating pure fluids and mixtures.

Exploiting fucoxanthin mono-carrier nanoparticles to modulate digestion and metabolic regulation in an obesity model

Fucoxanthin (FX), a natural compound derived from marine algae, and FX-based nanoparticles (FZ) were investigated for their potential in regulating glucose and lipid metabolism. Gavage interventions with FX, FZ, and zein hydrolysate (ZH) were administered to obese mice, resulting in significant outcomes. These interventions led to variations in growth rates, reduced blood glucose levels, improved lipid profiles, and ameliorated liver pathology. Our mRNA expression analysis revealed modulation of key genes involved in glucose metabolism. Gut analysis, including short-chain fatty acid detection and 16S rRNA gene sequencing, provided insights into gut microbiota modulation. Notably, differences observed in vivo between FX and FZ were attributed to their distinct release rates during in vitro digestion in the stomach and intestine. This comprehensive study validates our methods, modeling, and metabolic parameter determination, reinforcing the significance of our results. In conclusion, our findings highlight the promising potential of FX-encapsulated interventions in modulating metabolic pathways. Furthermore, they emphasize the crucial role of gut microbiota dynamics in the strategic mitigation of disorders associated with obesity.

Characterization of Viscoelastic Properties Considering the Nonrelaxation for Filled Rubber

The influence of nonrelaxation on the Payne effect of carbon black-filled rubber is studied. The prestrain is introduced in the Kraus model in the form of exponential growth. Combined with studies of temperature correlations, an explicit model for predicting the Payne effect at different prestrains and temperatures is developed. Dynamic mechanical analyses are performed to determine model parameters and validate the proposed model. To further verify the proposed model, the heat buildup of rubber columns under dynamic tensile load is tested and simulated. The comparison between simulated and measured data shows that the simulation considering nonrelaxation is more accurate than without considering. With the increase of prestrain, the accuracy of considering nonrelaxation becomes more obvious.

Experiments on the Coal Measures Sandstone’s Dynamic Mechanical Parameter Characteristics under the Combined Action of Temperature and Dynamic Load

This paper conducts impact dynamics experiments on coal measures sandstone in a deep mine via the established dynamic load and temperature split Hopkinson pressure bar (SHPB) experimental system. Firstly, the experimental conditions for the impact dynamics of fine sandstone were determined, with temperatures of 18, 40, 60, 80, and 100 °C, an axial static load range of 1–9 MPa, and a preset bullet incidence velocity of 1.0–5.0 m/s. Secondly, based on the analysis of the basic parameters and physical composition, the dynamic stress and strain responses of fine sandstone under different experimental conditions were obtained, and the change mechanism of its dynamic mechanical process was theoretically analyzed. When the temperature rose from 18 °C to 100 °C, the dynamic peak stress of fine sandstone increased from 36.04 MPa to 73.41 MPa, with an increase of 103.7%. At a temperature of 40 °C, when the axial static load increased from 1 MPa to 9 MPa, the dynamic stress peak of fine sandstone increased from 57.25 MPa to 80.01 MPa, and the corresponding peak strain also showed an increasing trend. The experiment analyzed the variation characteristics of dynamic stress in fine sandstone under the combined action of different strain rates or bullet incidence velocities and different temperatures. In the strain rate range of 47.1 s−1 to 140.9 s−1, there was a significant strain rate effect on the dynamic peak stress and peak strain of fine sandstone, which increased with the increase of strain rate. The study found a polynomial relationship between the dynamic mechanical parameters of fine sandstone and the impact of experimental parameters, with a coefficient of determination greater than 0.9. A dynamic stress constitutive model for fine sandstone under one-dimensional stress state under dynamic load and temperature action was established, and the model validation and parameter determination of dynamic stress changes in fine sandstone under different temperature conditions were carried out. The research results provide a new experimental method for the static and dynamic mechanical analysis of coal and rock masses under complex geological conditions and can provide a basic reference for the prevention and control of dynamic disasters in deep mining processes.

In situ multi-perspective scanning of 3D particle deposition on flat plates with film cooling and determination of practical model parameters

A practical strategy for three-dimensional morphology measurement in the particle deposition process is of great importance to deepen the understanding of deposition physical processes. Due to the methodology limitations, it is difficult to obtain the formation process of deposition morphology. In this work, an in situ multi-perspective scanning (MPS) method using structured light was developed to capture the formation process of deposits. The point cloud registration algorithm was adopted to reconstruct the deposition morphology. Based on the validation results of Vernier caliper, the MPS method provides an uncertainty of ±0.056 mm at 4 mm. To verify the potential application of in situ measurement in gas turbine diagnostics, MPS method was preliminarily applied to the flat plate test with film cooling in particle-laden flow. The application demonstrated that this method successfully reconstructed the deposition morphology on a flat plate in the presence of film cooling. Furthermore, the obtained morphology results revealed that deposition roughness lied in the 0.35–0.56 mm and increased with an increase of blowing ratio. Additional comparisons with film cooling effectiveness showed the deposition distribution was related to the film cooling process. The detailed three-dimensional (3D) deposition morphology determined the mean curvature and mass of deposits, which may improve the prediction accuracy of deposition models in numerical simulations.

A generalized, computationally versatile plasticity model framework - Part I: Theory and verification focusing on tension‒compression asymmetry

Microstructural characteristics and complex loading conditions in the deformation of metallic materials lead to complexity in mechanical responses. In this study, we propose a generalized constitutive framework that reproduces the plastic anisotropy and asymmetry under various loading conditions. Particularly, the developed model can accurately capture distinctive flow stress, plastic flow and strain hardening between tensile and compressive dominant loadings under a wide range of stress states. The model is based on the stress triaxiality dependence of state variable (or weighting factor) newly incorporated in the existing plasticity theory to keep the computational efficiency and versatility. For example, the new generalized framework can be applied to widely employed Hill48, Yld2k-2d, and Poly6 as a class of associated flow rule-based yield functions, as well as Stoughton-Yoon2009 and Min2016 yield functions for the non-associated flow rule. Also, the model is adaptable when selecting the yield function under tension or compression, which efficiently controls the degree of accuracy in anisotropic modeling under tension and compression. The generalized plasticity framework is validated comprehensively by demonstrating the predictive capability for anisotropy in yield stress and plastic flow of metallic materials with different crystal structures. Moreover, the model can efficiently capture the continuous evolution of asymmetric yield surfaces as functions of strain, temperature, and strain rate. Finally, the identification procedure of the model is discussed by demonstrating the analytical determination of model parameters utilizing the experimental or generic material data obtained from various loading conditions such as tension, compression, plane strain loading, and pure shear.

A comprehensive parametrization approach for the Hubble parameter in scalar field dark energy models

This study proposes a novel parametrization approach for the dimensionless Hubble parameter i.e. E2(z)=A(z)+β(1+γB(z)) in the context of scalar field dark energy models. The parametrization is characterized by two functions, A(z) and B(z), carefully chosen to capture the behavior of the Hubble parameter at different redshifts. We explore the evolution of cosmological parameters, including the deceleration parameter, density parameter, and equation of state parameter. Observational data from Cosmic Chronometers (CC), Baryonic Acoustic Oscillations (BAO), and the Pantheon＋ datasets are analyzed using MCMC methodology to determine model parameters. The results are compared with the standard ΛCDM model using the Planck observations. Our approach provides a model-independent exploration of dark energy, contributing to a comprehensive understanding of late-time cosmic acceleration.