Sensitivity Analysis Of Parameters Research Articles

Optimal hydraulic PTO and linear permanent magnet generator for a floating two-buoy wave energy converter

A fully coupled fluid-structure-power take-off model is used to investigate the optimal energy capture of a floating two-buoy wave energy converter excited by regular waves and irregular waves. The system incorporates hydraulic power take-off and a linear permanent magnet generator. By considering the coupling relationship between the power take-off parameters for regular waves, a power take-off parameter interval is determined for the optimization model. Parametric optimization of the hydraulic power take-off and linear permanent magnet generator systems is proposed, based on response surface methodology in conjunction with central combination design for sensitivity analysis and optimal configuration of different parameters. The methodology efficiently predicts optimal electric efficiency while reducing the requirement for multiple boundary element simulations. It is found that a model with predicted optimal configuration of the hydraulic power take-off system can achieve 10 % more electrical efficiency than that with linear permanent magnet generator. Comparison between the energy capacities of the different systems indicates that a device with linear permanent magnet generator attains higher energy conversion efficiency within a wider power take-off bandwidth. This study provides guidance for the design and selection of efficient power take-off systems for floating two-buoy wave energy converters.

Analyzing parametric influences driving age-associated changes in absorption using a PBPK-GSA approach

The advanced age population may be susceptible to an increased risk of adverse effects due to increased drug exposure after oral dosing. Factors such as high-interindividual variability and lack of data has led to poor characterization of absorption's role in pharmacokinetic changes in this population. Physiologically based pharmacokinetic (PBPK) models are increasingly being used during the drug development process, as their unique qualities are advantageous in atypical scenarios such as drug-drug interactions or special populations such as older people. Along with relying on various sources of data, auxiliary tools including parameter estimation and sensitivity analysis techniques are employed to support model development and other applications. However, sensitivity analyses have mostly been limited to localized techniques in the majority of reported PBPK models using them. This is disadvantageous, since local sensitivity analyses are unsuitable for risk analysis, which require assessment of parametric interactions and proper coverage of the input space to better estimate and subsequently mitigate the effects of the phenomenon of interest. For this reason, this study seeks to integrate a global sensitivity analysis screening method with PBPK models based in PK-Sim® to characterize the consequences of potential changes in absorption that are often associated with advanced age. The Elementary Effects (Morris) method and visualization of the results are implemented in R and three model drugs representing Biopharmaceutical Classification System classes I-III that are expected to exhibit some sensitivity to three age-associated hypotheses were successfully tested.

Extracting and analyzing the governing model for plastic deformation of metallic glasses

This paper first detects hidden system from plastic deformation of metallic glasses by sparse identification. The extracted model simulates four types of stress-time curves and displays the prediction of serrated events. This interpretation effectively explains various experimental phenomena of repeated yielding. Further, in terms of parametric sensitivity analysis to the model, two parameters are taken as bifurcation parameter, and the analysis of codimension-one and codimension-two bifurcation are carried out to excavate the causes of dynamic transformation, including saddle–node bifurcation, Hopf bifurcation, Bogdanov–Takens bifurcation and cusp bifurcation. Different bifurcation points correspond different types of stress-time curves. The homologous phase diagrams including periodic orbit, unstable orbit and chaotic behavior are presented to show the dynamics diversity of the model. In addition to dynamic analysis, statistical analysis for plasticity values is also applied to excavate the crossover between periodic and chaotic plastic dynamic transitions. Our results provide a novel perspective on the deformation of metallic glasses from the viewpoint of dynamic model and are also important for evaluating the plastic deformation properties of metallic glasses in practical applications.

Analysis of the thermodynamic performance of the SOFC-GT system integrated solar energy based on reverse Brayton cycle

Efficient power generation systems are an essential way to reduce carbon emissions. To avoid the complex effects of high-pressure conditions on the solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems, while further improving energy efficiency. A SOFC-GT hybrid system based on the reverse Brayton cycle (RBC) is proposed, in which the SOFC operates at near atmospheric pressure conditions and GT expands below atmospheric pressure. Solar energy is integrated to further enhance system performance. Rigorous assessments of the novel system and the pressurized system are conducted utilizing energy, exergy and economic analysis. The sensitivity analysis of environmental parameters on the novel system's performance is performed. The results show that the novel system generates power and efficiency better than the conventional system when the GT compression ratio is 3 and 6, respectively. The power generation and exergy efficiency of the novel system at the design point is 60.47 % and 58.57 %, respectively. The largest exergy destruction occurred in the SOFC. The lowest exergy efficient components are the parabolic dish collector (PDC) and heat exchanger 2. The novel system is more cost-effective due to the higher lifetime of the SOFC, with the levelized cost of energy (LCOE) is 0.1418 $/kWh.

Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.

A line balancing problem with parallel workers and cycle time minimization

We study the problem of balancing assembly lines with parallel workers, motivated by features observed at a third-party logistics provider. The assembly line differs from the two known types of line balancing problems in the literature since it determines simultaneously the line cycle time and the number of workers per station. Furthermore, each station can be occupied by more than one worker and a restriction on the minimum number of stations is imposed. This new type of line balancing problem minimizes the line cycle time, where two types of decisions must be made: how to group the tasks into stations and how many workers to assign to each station. We adapt mathematical models based on assembly line balancing problems with parallel stations. Furthermore, we propose a new model based on network flow formulations for the special case of lines with a serial task structure, frequently observed at the company. We perform an extensive computational study with realistic instances in order to compare the speed of solving different formulations, which is important as such a problem is at the operational level. We also provide a sensitivity analysis of key parameters aiming to better understand the trade-offs and provide meaningful managerial insights.

Revealing the sentiment propagation under the conscious emotional contagion mechanism in the social media ecosystem: For public opinion management

In public opinion events, the breeding of negative sentiment has a serious negative impact on the network environment and even offline lives. Hence, establishing an emotion-based propagation dynamic model to catch the sentiment development patterns is essential for helping public opinion management. We propose an E-SLFI (Emotion-driven Susceptible-Latent-Forwarding-Immune) model, which describes the dynamics of the sentiment propagation of ternary polarities under the promoting effect of the conscious emotional contagion mechanism. An empirical case composed of 16,354 pieces of forwarding information and two phases verifies the effectiveness of the proposed sentiment propagation dynamic model, due to the fitting optimization indicator MAPE equals 0.0942 % and 0.0066 % respectively. Further, we simulate the model and implement sensitivity analysis of the important parameters of the model. Combining the results of experiments, we find that enhancing the emotional consensus of recipients and inducers can decide the main sentiment in the system, and anti-emotional consensus can improve the existence of weak sentiments. Our work here is conducive to designing online public sentiment guidance strategies to manage public opinion and calming the network atmosphere to a certain extent.

Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework

Electrochemical-thermal models offer profound insight into the internal state of batteries, demonstrating significant potential in energy storage. However, model complexity makes accurate parameter acquisition challenging. This study proposes a novel hybrid parameter estimation framework for effective parameterisation of battery electrochemical-thermal models. A high-fidelity reduced-order electrochemical-thermal model was established to accelerate the simulation of the entire time-domain process. A global sensitivity analysis of the model parameters was conducted, comprehensively evaluating their impact on simulated voltage and temperature. Highly sensitive parameters were categorised into static and time-dependent dynamic estimation groups for hierarchical estimation. A deep learning method was applied to generate preliminary parameter estimates to accelerate the convergence of estimation process. Subsequently, a genetic algorithm was employed to optimise the preliminary estimates of static parameters. A full temporal domain estimation was conducted using Markov Chain Monte Carlo methods for time-dependent dynamic parameters. The experimental data covers six dynamic and nine constant current conditions to validate the proposed method's feasibility. After parameter estimation, the proposed method reduces the comprehensive normalised root mean square error of voltage and temperature by up to 80.36% compared to four conventional methods. The minimum average absolute errors of voltage and temperature under constant and dynamic current conditions are 10.63 mV and 0.10 °C, respectively. The proposed method provides new insights for the parameterisation of battery models.

Calibration guide for watershed modeling with distributed groundwater modeling: application for the SWAT+ model

ABSTRACT We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling.

Confinement strength prediction of corroded rectangular concrete columns using BP neural networks and support vector regression

The application of machine learning in predicting the mechanical performance of reinforced concrete columns subjected to reinforcement corrosion was investigated in this study. Traditional models, often relying on empirical formulas or simplified assumptions, are known to struggle in capturing all variables and relationships in complex problems, leading to inadequate prediction accuracy. Through theoretical analysis, the confinement effect of stirrups on core concrete and its performance deterioration caused by reinforcement corrosion were determined. To improve the prediction accuracy and generalization ability, the error Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models were employed. Parameter effects in the BPNN model were interpreted using the SHapley Additive exPlanations (SHAP) and compared with traditional parameter sensitivity analyses. The investigation indicates that the machine learning models have a high degree of fit and low error levels, particularly within the training set. After analyzing using the SHAP approach, it is found that the corrosion ratio of the stirrup and longitudinal reinforcement have a significant effect on the confinement strength of the concrete, whereas the effects of the stirrup configuration and size effect are negligible, which is different from conventional parametric sensitivity analysis. As to the stability analysis of model predictions, the SVR model shows lower volatility and higher prediction stability compared with the BPNN model, despite the latter occasionally providing superior generalization ability. This research underscores the significant advantages offered by machine learning models in addressing longstanding challenges associated with strength prediction, and thus presents new perspectives for future study.

Modification of Green-Ampt model based on multi-stage characteristics of soil water infiltration

ABSTRACT Water infiltration in unsaturated soil is significantly affected by multi-stage characteristics that depend on the movement of the wetting front. We modified the Green-Ampt model to accurately capture these multi-stage characteristics during infiltration. The proposed model divides the water-infiltrated unsaturated soil into three zones, while the infiltration process itself is divided into three stages, with the quarter-elliptic wetting front curve being defined. The proposed model, together with other Green-Ampt based models, is analysed and compared with experimental investigations conducted in this study, as well as with published results from the literature. The results demonstrate the proposed modified model successfully describes the measured infiltration time for soil columns with different heights. Additionally, a parametric sensitivity analysis of the proposed model reveals the water infiltration process is mainly controlled by the hydraulic conductivity in the infiltrated zone and the suction head between the wetting front zone and the unpenetrated zone.

A Comparative Study of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling

Lithium-ion battery (LIB) with high specific energy density and long cycle life is one widely used energy storage technology. In order to ensure longevity and safety of the battery system, a battery management system (BMS) that relies on battery model is required. With the development of BMS technique, more attention has been given to electrochemical models that provide insights into the battery internal states. In general, battery electrochemical models consist of partial differential equations (PDEs) describing the thermodynamic and electrochemical process inside the cell, and those PDEs can only be solved numerically due to their complexity.Existing battery simulation software utilize numerical methods such as finite difference method (FDM), finite volume method (FVM), and finite element method (FEM) to solve PDEs in battery models. However, there are little discussions for the selection of a specific numerical methods. In our recent study [1], we compare two spatial discretization methods commonly used to numerically solve the governing PDEs of LIB electrochemical models, namely FDM and FVM, in terms of model accuracy and mass conservation guarantee. First, we provide the mathematical details of the spatial discretization process for both FDM and FVM to solve the battery single particle model (SPM), this result has not been shown in any publication before. Second, we propose a new Hermite extrapolation based FVM scheme leading to higher accuracy when compared to the normally used linear extrapolation based FVM scheme. Then, SPM parameters are identified using experimental data, and a comprehensive parameter sensitivity analysis is conducted under different current input profiles to study parameter identifiability. Finally, model accuracy and mass conservation analysis of the FDM and FVM schemes are presented. Our study shows that the FVM scheme with Hermite extrapolation leads to accurate and robust control-oriented battery model while guaranteeing mass conservation and high accuracy. Also, the proposed new FVM scheme can be extended to solve other battery models, such as SPM model with electrolyte and Doyle-Fuller-Newman model. Moreover, we provide findings of mass conservation analysis for FDM and FVM schemes, which we hope can facilitate BMS model selection.

A deep-learning-boosted surrogate model of a metal foam based protonic ceramic electrolysis cell stack for uncertainty quantification

Protonic ceramic electrolysis cells (PCECs) are widely considered as an emerging technology that shows capability for enabling environmentally friendly large-scale production of hydrogen. PCEC stacks’ performance is considerably lower compared to smaller, lab-scale button cells. This performance gap is largely attributed to the highly uneven distribution of reactants throughout the larger cell or stack, which presents a significant challenge to the development of PCEC stack. The issue of non-uniform reactant distribution in PCEC stacks can be effectively addressed by incorporating metal foam structures to replace the conventional rib design of the interconnect components. A 3D multiphysical model is developed to train a deep-learning-boosted surrogate model for PCEC with metal foam, which is subsequently used for parameter screening and sensitivity analysis. A collection of 12 inputs that encompass operating/structural/microstructural/material parameters are chosen, in which the operating parameters are identified as influential parameters. Three distribution uniformity indices maintain strong consistence to variations in the inputs, implying the positive role of metal foam. Faradaic efficiency (FE) is primarily influenced by the operating voltage with a total Sobol index of 0.6. The current density and FE are determined as the most stable performance indicators, exhibiting standard deviations of 0.15 and 0.81 respectively. According to a safety threshold of 10 K cm−1 for temperature gradient, the probability of the stack operating below this threshold is 88.61 %. Valuable insights into the design and performance of PCEC stacks that incorporate metal foam structures are provided. The workflow developed in this work can be further leveraged to explore and optimize PCECs.

A novel efficient calculation method for rotor aeroacoustic characteristics based on multiple aerodynamic surfaces source model

Based on the simplified acoustic source model of multiple aerodynamic surfaces, an efficient calculation method for rotor aeroacoustic characteristics has been established. Firstly, the aerodynamic characteristics database of the airfoil is obtained based on the Computational Fluid Dynamics (CFD) method. The inflow environment of each blade element is calculated using the free wake method, and the distribution of chord-wise loading is obtained by combining the established database. Subsequently, based on the F1A equation, a simplified formula for loading noise with pressure difference as a parameter is derived. The thickness noise is obtained from the blade shape and operating conditions, and the blade surface mesh is constructed by using the adaptive curvature distribution strategy of airfoil points. The validation is carried out by comparing with experimental values in the hover and forward flight states. Then, A hybrid weighted interpolation scheme is proposed based on inverse distance weighted interpolation (IDW) and bidirectional linear interpolation method, which is suitable for capturing blade/vortex interaction (BVI) noise. A set of parameters suitable for engineering applications is obtained through the parametric sensitivity analysis. The computational time and memory usage are reduced to 1/47 and 1/4 of the original amount, respectively. Finally, Using the above method, the influence of rotational speeds (ω) on rotor aeroacoustic characteristics are studied. The results show that ω and sound pressure level (SPL) are linearly related and the SPL decreases by approximately 5 dB for every 0.1 Ω decrease in ω. The rotational speed ω≈90 %Ω can effectively suppress rotor aeroacoustic levels, benefiting the stealth design of helicopters.

Penetration mechanism of open-ended pipe piles under jacking and driving methods

Different pile penetration methods will cause different pile penetration results. Therefore, it is very necessary to study the pile penetration mechanism under different pile penetration methods. In this paper, numerical simulations using the discrete element method (DEM) are carried out to investigate the meso-scopic mechanism of pipe pile penetration under jacking and driving methods. The mechanism is comprehensively compared and analyzed in terms of particle movement, changes in the force chain, displacement changes, resistance of penetration pile, soil lateral stress and soil vertical stress. Results show that the height of the soil plug in the jacked pile is lower than that in the driven pile. The degree of contact force chain at the bottom of the soil plug in the driven pile is slightly higher than that at the bottom of the soil plug in the pile of the jacked pile. The total resistance of the driven pile greater than that of the jacked pile. Within a certain distance below the pile tip, the jacked pile affects the vertical stresses of the soil in this area to a greater extent than the driven pile. Then, parametric sensitivity analysis is performed, which captures the effects of the penetration velocity in the jacking method and the hammering frequency in the driving method. This study can provide a reference for the selection of the penetration method of pipe piles in actual projects.

Sensitivity analysis of a dense discrete phase model for 3D simulations of a tapered fluidized bed

This study aims to conduct a sensitivity analysis of closure models and modeling parameters for the Dense Discrete Phase Modeling (DDPM) approach in order to investigate the hydrodynamics of a 3D lab-scale Tapered Fluidized Bed (TFB). The closure models and model parameters under investigation include the gas-solid drag force, viscous models, particle-particle interaction models, restitution coefficient, specularity coefficient, and rebound coefficient. The primary objective of this sensitivity analysis is to optimize the numerical model's performance. The numerical results, in terms of axial and lateral Solid Volume Fraction (SVF) profiles obtained from the sensitivity analysis, indicate that the drag force and restitution coefficient significantly influence the hydrodynamics of the TFB. Properly selecting these parameters could result in the improved performance of the numerical model. However, the sensitivity of turbulence models, particle-particle interaction models, specularity coefficient, and rebound coefficient has a lesser impact on the hydrodynamics results. This work concludes with the recommendation of a set of closure models and modeling parameters that offer the most accurate prediction of the hydrodynamics of the TFB.

Abstract Mo057: Computational Modeling Reveals Enhanced Ca 2+ -Driven Arrhythmias in Female Patients with Atrial Fibrillation

Background and Aim: Substantial sex-based differences have been reported in atrial fibrillation (AF), with female patients experiencing worse symptoms, more complications from treatment, and greater risk of stroke and mortality. Recent studies revealed sex-specific alterations in AF-associated Ca 2+ dysregulation, whereby female myocytes more frequently exhibit proarrhythmic Ca 2+ -driven instabilities compared to male myocytes. Here, we aim to obtain a mechanistic understanding of the Ca 2+ -handling disturbances and Ca 2+ -driven arrhythmogenic events in males vs females and establish their responses to Ca 2+ -targeted interventions. Methods and Results: We incorporated known sex differences and AF-associated changes in the expression and phosphorylation of key Ca 2+ -handling proteins and in ultrastructural properties and dimensions of atrial myocytes into our recently developed 3D atrial myocyte model that couples electrophysiology with spatially detailed Ca 2+ -handling processes. Simulations show increased incidence of Ca 2+ sparks in female vs male myocytes in AF, in agreement with previous experimental reports. Additionally, our female model exhibited elevated propensity to developing pacing-induced spontaneous Ca 2+ releases (SCRs, Figure 1A,B ) and augmented beat-to-beat variability in action potential (AP)-elicited Ca 2+ transients compared with the male model ( Figure 1C ). Parameter sensitivity analysis uncovered precise arrhythmogenic contributions of each component that was implicated in sex and/or AF alterations ( Figure 2 ). Specifically, increased ryanodine receptor phosphorylation in female AF myocytes emerged as the major SCR contributor, while reduced L-type Ca 2+ current was protective against SCRs for male AF myocytes. Furthermore, simulations of Ca 2+ -targeted interventions identified potential strategies to attenuate Ca 2+ -driven arrhythmogenic events in female atria (e.g., t-tubule restoration, and inhibition of ryanodine receptor and sarcoplasmic/endoplasmic reticulum Ca 2+ -ATPase), and revealed enhanced efficacy when applied in combination. Conclusions: Our sex-specific computational models of human atrial myocytes uncover the increased female propensity to Ca 2+ -driven arrhythmogenic events vs male in AF, and identify combined Ca 2+ -targeted interventions as promising approaches to treat AF in female patients. Our study establishes that AF treatment may benefit from sex-dependent strategies informed by sex-specific mechanisms.

Inverse design of V-shape feed spacer for batch and semi-batch reverse osmosis

In recent years, batch and semi-batch reverse osmosis (RO) processes have received wide attention because of their internal staging feature. However their performance is severely compromised by salt retention induced by incomplete flushing, which leads to elevated salt concentration, uplifted peak pressure, and extra power consumption. Herein we adopt transient three-dimensional computational fluid dynamics to systematically analyze the effect of the V-shape feed spacer geometries on hydrodynamics and mass transfer characteristics. After a sensitivity analysis of all geometric parameters, a heuristic optimization framework is proposed for inverse design of the feed spacer. Using the optimized feed spacer, flushing efficacies after one displacement volume are 0.936, 0.962, 0.969 and 0.974 respectively for one, three, five and seven membrane elements connected in series (whereas 0.884, 0.926, 0.942 and 0.952 for commercial spacers), which help to mitigate membrane scaling and fouling. Moreover, the optimized spacer has a pressure drop per meter 73 % lower than that of the commercial spacer. If used in conjunction with high permeability membranes, batch RO and semi-batch RO could save 29.9 % and 24.1 % specific energy consumption relative to conventional one-stage seawater RO.

Creep–fatigue life prediction of a titanium alloy deep-sea submersible using a continuum damage mechanics-informed BP neural network model

Deep-sea submersibles are subjected to trapezoidal wave loading during operation, which can cause creep–fatigue failure of pressure hull structures. To ensure structural safety, it is necessary to develop accurate and efficient creep–fatigue life prediction methods. This paper proposes a continuum damage mechanics-informed BPNN approach for predicting the creep–fatigue life of titanium alloy submersibles. To obtain a reliable sample set, the creep–fatigue life is calculated based on the titanium alloy room-temperature creep–fatigue damage model and numerical analysis methods proposed by the authors. The operating duration, operating pressure, axial stress, and circumferential stress were selected as the input variables, whereas the number of cycles and actual operating duration were chosen as the output variables. The proposed model fills the current gap of a lack of research on the creep‒fatigue life prediction of titanium alloy deep-sea submersibles via machine learning methods. The research results show that the neural network model established in this paper can quickly and accurately predict the creep–fatigue life of titanium alloy deep-sea submersibles. A sensitivity analysis of the input parameters revealed that the creep–fatigue life is more sensitive to the operational pressure than to the dwell time.

Optimal control analysis for the Nipah infection with constant and time-varying vaccination and treatment under real data application

In the last two decades, Nipah virus (NiV) has emerged as a significant paramyxovirus transmitted by bats, causing severe respiratory illness and encephalitis in humans. NiV has been included in the World Health Organization’s Blueprint list of priority pathogens due its potential for human-to-human transmission and zoonotic characteristics. In this paper, a mathematical model is formulated to analyze the dynamics and optimal control of NiV. In formulation of the model we consider two modes of transmission: human-to-human and food-borne. Further, the impact of contact with an infected corpse as a potential route for virus transmission is also consider in the model. The analysis identifies the model with constant controls has three equilibrium states: the NiV-free equilibrium, the infected flying foxes-free equilibrium, and the NiV-endemic equilibrium state. Furthermore, a theoretical analysis is conducted to presents the stability of the model equilibria. The model fitting to the reported cases in Bangladesh from 2001 to 2015, and the estimation of parameters are performed using the standard least squares technique. Sensitivity analysis of the model-embedded parameters is provided to set the optimal time-dependent controls for the disease eradication. The necessary optimality conditions are derived using Pontryagin’s maximum principle. Finally, numerical simulation is performed to determine the most effective strategy for disease eradication and to confirm the theoretical results.