Parameter Sensitivity Study Research Articles

Numerical prediction of potential 3-dimensional seabed trench profiles considering complex motions of mooring line

Seabed trenches near deep-sea anchors bring risks to the mooring system's safety. The prediction of 3-dimensional (3-D) trench profiles is a key task to reduce the risk. However, the trench width related to the trench wall collapse and lateral movement of the mooring line has not been well studied. This paper first proposes a 3-D mooring line-seabed dynamics model to consider the mooring line lateral movement in the soil. Then, a seabed trenching model to simplify 3-D trench profiles is developed based on the field data. By combining the above models, a novel method to predict 3-D trench profiles around embedded mooring lines is established by considering trench wall collapse and integrating dynamics of mooring line. Based on the verified method, both static and dynamic responses of mooring lines are investigated, then a series of parametric sensitivity studies are carried out. The simulation results reveal that dynamic movement of mooring lines at fairlead may result in remarkable seabed trenches, and lateral motion of mooring lines enlarges the disturbed zone near the mooring line touchdown area. The proposed method in this paper provides a reasonable assessment of the 3-D trench profiles and reduces risks in the operation of mooring systems.

Parametric study of N170 sensitivity to diagnostic facial information during face identification

Parametric study of N170 sensitivity to diagnostic facial information during face identification

Using an Internal State Variable Model Framework to Investigate the Influence of Microstructure and Mechanical Properties on Ballistic Performance of Steel Alloys

An internal state variable (ISV)-based constitutive model has been used within a Lagrangian finite element analysis (FEA) framework to simulate ballistic impact of monolithic rolled homogenous armor (RHA) steel plates by RHA steel spheres and cylinders. The ISV model predictions demonstrate good agreement with experimental impact data for spherical projectiles. A simulation-based parametric sensitivity study was performed to determine the influence of a variety of microstructural and mechanical properties on ballistic performance. The sensitivity analysis shows that the lattice hydrogen concentration, material hardness, and initial void volume fraction are dominant factors influencing ballistic performance. Finite element simulations show that variation of microstructure properties could explain the reduced ballistic performance of high hardness materials previously documented in the literature. The FEA framework presented in this work can be used to determine material properties conducive to ballistic-impact resistance.

A micromechanical cyclic damage model for high cycle fatigue failure of short fiber reinforced composites

This paper presents a micromechanical high cycle fatigue damage model for short fiber reinforced composite materials. Because the damage processes within such materials are influenced strongly by their microstructure, we use a mean field homogenization framework to compute the macroscopic behavior of the composite as well as the microscopic stresses and strains in the constituent phases. This allows us to account for damage phenomena related to both the fiber phase as well as in the matrix material. Fiber and fiber-interface damage is modeled using a Tsai–Wu and Weibull-based approach and the progressive damage due to cyclic loading is described by a progressive matrix damage model. The latter includes a novel coupling term in which the matrix damage progression is linked to the damage state of the reinforcing fibers. A cycle-based numerical formulation is used to overcome the computational limits of such load cases in the time domain. While the approaches are in principle applicable to different types of fiber–matrix composite, a short glass fiber reinforced polyamide 6.6 is used as an example material, for which microstructural analyses as well as tensile and fatigue tests are reported. The model’s capabilities with regard to complex fiber orientations and different fiber fractions are studied using different grades of this material class. Furthermore, the model is analyzed via benchmarks of the numerical schemes and by parameter sensitivity studies. The results show that the approach is capable of modeling the complex and microstructure-dependent fatigue damage and fatigue limits for the different material grades with limitations only becoming visible at very high fiber fractions.

Evaluation of fault stability and seismic potential for Hutubi underground gas storage due to seasonal injection and extraction

The Hutubi gas field was put into production in 1998 and then converted into an underground gas storage (UGS) facility in 2013, and since then a cluster of earthquakes associated with seasonal injection and extraction activities have been recorded nearby. To evaluate the fault stability and seismic potential, we established a pseudo-3D geomechanical model to simulate the process of seasonal injection and extraction. Reservoir pore pressures from 1998 to 2019 were obtained through multiphase reservoir simulation and validated by history matching the field injection and production data. We then imported pore pressures into the geomechanical model to simulate the poroelastic perturbation on faults for over 20 years. The fidelity of this model was validated by comparing the simulated surface deformation with global positioning system (GPS) measured data. We used Coulomb failure stress (CFS) as the indicator for the likelihood of fault slippage. The simulation results show that the location of the induced earthquake cluster was within the positive Coulomb stress perturbation (ΔCFS) area, in which fault slippage was promoted. In addition, ΔCFS at the earthquake location kept increasing after the injection began. These findings could explain the induced earthquakes with the Coulomb failure stress theory. Furthermore, we conducted a parameter sensitivity study on the dominant factors such as the maximum operating pressure (MOP), frictional coefficient, and dip angle of the pre-existing fault. The results indicate that the magnitude of ΔCFS caused by seasonal injection and extraction decreases with distance; MOPs are constrained to 32.9, 36.2, and 39.5 MPa according to different ΔCFS thresholds; the critical dip angle ranges are 0–20° and 80°–100°; and strengthening the fault friction can either increase or decrease the seismic potential. This study can help determine the MOP for Hutubi underground gas storage (HTB UGS) and provide a framework for simulating the potential causes of induced seismicity for other sites.

Numerical Modeling and Studies of the Acid Stimulation Process in Dolomite Carbonate Rocks

Summary Extensive studies have been conducted to capture the acid stimulation process in calcite rocks. The related studies on the dolomite system are far more limited in number, and this work is motivated to improve the understanding of the acid stimulation process in dolomite carbonate rocks. The two-scale continuum model is improved to comprehensively consider the mass, momentum, and energy change within the acidizing system. Simulations are performed using the finite volume method. The reliability of the developed numerical model is confirmed and validated by commercial software and previously reported experimental results. Comparison results have shown good agreement. Based on this model, a general reaction and diffusion constitute model is used to conduct a parameter sensitivity study to examine the effect of acid solution physical parameters on the acid stimulation results. A comprehensive rheology model is also developed to describe the different viscosity constitutive relationships of acid fluids, including the Newtonian, non-Newtonian, and diversion mechanisms. The simulation results are further analyzed to reveal the working mechanisms for using different acid systems and to clarify the optimization principle for acid stimulation.

Impact of microannulus on the efficiency of heat transfer in the bottomhole

To ensure feasible power generation from closed-loop geothermal wells, deeper wells are required to reach higher temperature zones. However, weak bonding between cement and casing or cement and formation may allow formation of a small gap (known as microannulus), which could have a negative effect on the heat extraction rate and consequently compromises the entire investment. Previous projects have reported that the output temperatures were significantly lower than the expected values, and the cause is believed to be cement debonding. This study aims to develop a reliable simulation model to demonstrate the impact of microannulus in closed-loop geothermal systems. Multi-physics finite element analysis is used to construct models with and without microannulus. The microannulus is modeled based on real cement evaluation logs, with gaps varying between a few micrometers to few millimeters. In extreme cases, the presence of microannulus is found to decrease the geothermal power by more than 35%. Furthermore, the possibility of heat loss containment is investigated by a sensitivity study of wellbore parameters. These sensitivity analyses demonstrate that cement and geothermal fluids with higher thermal conductivity can improve but cannot compensate the presence of microannuli. The results also highlight the importance of proper cementing design to ensure wellbore integrity and avoid geothermal power loss.

Deriving Lidar Sensor Requirements for Use in Wake Impact Alleviation Functions

The paper complements previous works of the authors on lidar-based active wake impact alleviation control functions by investigating the lidar sensor requirements for such systems. This control system can increase the safety during wake-vortex encounters by preventing a dangerous sudden (in particular rolling) motion, which often characterizes wake-vortex encounters. Such technology could provide an opportunity to relax the current wake-vortex-based separation minima and thus lead to a capacity gain for airports and in the terminal area. The performance of this system obviously strongly depends on the performance of the lidar sensor, however in a nontrivial way. The direct problem, in other words, determining the system performance obtained with a given sensor configuration, already requires setting up a fully coupled simulation environment. The inverse problem, in other words, determining the best-suited set of sensor parameters, is approached in this work through parameter variations and sensitivity studies. Key performance parameters for assessing the overall system performance and providing trends for sensor requirements are defined. As for most technical systems, the best configurations are characterized by a good tradeoff of the relevant parameters, in this case field of view, measurement range, spatial resolution, and measurement noise. The coupled simulations revealed that, contrary to the authors’ expectations, very good results were already obtained with small ranges of 60 m and lateral fields of view of .

Sensitivity Study of Highway Tunnel Light Environment Parameters Based on Pupil Change Experiments and CNN Judging Method

There is a sparsity of research regarding the nonlinear relationship between the sensitivity of the light environment parameters in the middle section of the tunnel under multi-factor conditions in multiple samples. Due to the lack of research, the present study was conducted in order to investigate said relationship. To determine the parameters of the eye-movement characteristics required for the convolutional neural network prediction evaluation, a tunnel simulation model was established using DIALux10 simulation software and a series of dynamic driving tests were conducted based on an indoor simulation experimental platform. Further, through employing the residual network ResNet to extract data features and the pyramidal pooling network module, a convolutional neural network judging model with adaptive learning capabilities was established for investigating the nonlinear relationship of sensitivity of light environment parameters. Following the test, the degree of influence on the diameter of the pupil for the different levels of each factor were: the optimal configuration of the staggered layout on either side of the lamp arrangement, the optimal 3 m height under the different sidewall painting layout height conditions, the optimal green painting color under the different sidewall painting color conditions, and the optimal 6500 k under different LED light source color temperature conditions. The results of the present study serve to expand the use of the convolutional neural network model in tunnel light environment research and provide a new path for evaluating the quality of tunnel light environment.

Process optimization and simulation of biodiesel synthesis from waste cooking oil through supercritical transesterification reaction without catalyst

This study reports optimization and simulation of biodiesel synthesis from waste cooking oil through supercritical transesterification reaction without the use of any catalyst. Although the catalyst enhances the reaction rate but due to the presence of water contents in waste cooking oil, the use of catalyst could cause a negative impact on the biodiesel yield. The transesterification reaction without catalyst also offers the advantage of the reduction of pretreatment cost. This study comprises of two steps; first step involves the development and validation of process simulation scheme. The second step involves the optimization using Response Surface Methodology. Face-centered central composite design of experiments is used for experimental matrix development and subsequent statistical analysis of the results. Analysis of variance is employed for optimization purpose. In addition, a sensitivity study of the process parameters including pressure, temperature, and molar ration of oil-to-methanol was conducted. The statistical analysis reveals that temperature is the most influential process parameter as compared to pressure and oil-to-methanol molar ratio. The optimization study results in the maximum biodiesel yield (94.16%) at an optimum temperature of 274.8 °C, 7.02 bar pressure, and an oil-to-methanol molar ratio of 12.43.

Sensitivity study of parameters important to Molten Salt Reactor Safety

This paper presents a molten salt reactor (MSR) design parameter sensitivity study using a nodal dynamic modelling methodology with explicitly modified point kinetics equation and Mann’s model for heat transfer. Six parameters that can impact MSR safety are evaluated. A MATLAB-Simulink model inspired by Thorcon’s 550MWth MSR is used for parameter evaluations. A safety envelope was formed to encapsulate power, maximum and minimum temperature, and temperature-induced reactivity feedback. The parameters are perturbed by ±30%. The parameters were then ranked by their subsequent impact on the considered safety envelope, which ranks acceptable parameter uncertainty. The model is openly available on GitHub.

Three-dimensional stability analyses and sensitivity studies of the input parameters in a global failure of an open pit slope: a case study

Three-dimensional stability analyses and sensitivity studies of the input parameters in a global failure of an open pit slope: a case study

On the Probabilistic Endurance Prediction Approach for Turbomachinery Blades and Vanes

Abstract The design and aeromechanical assessments of turbomachinery blades and vanes comprise a wide range of complex processes that tend to be based on conventional deterministic methods. These processes often provide a “snapshot” evaluation of the new component designs at the nominal operating conditions. While the deterministic methods can predict the high cycle fatigue (HCF) endurance with reasonable accuracy, they assume that the conservative safety factors applied to cover for the parametric variations, uncertainties, and unknowns will not change during the product life cycle. This approach is intended to be conservative and in some cases may overlook the lack of robustness. The present paper proposes a robust design analysis approach based on probabilistic methodology for the aeromechanical assessment of rotor blades and stator vanes of turbomachinery. The robust design approach can account explicitly for the effects of design and manufacturing variability. This methodology can reduce the levels of conservatism in the deterministic approach and can provide a more thorough risk assessment. This paper offers a generalized aeromechanical analysis formulation based on probabilistic methods to evaluate the HCF capability of turbomachinery components. Herein, this methodology is demonstrated using a typical stator vane of an aero engine compressor and it is based on Monte-Carlo and design of experiment (DOE) simulations. The methodology consists of parametric sensitivity studies and identification of the most influential parameters that control the HCF endurance. Future ideas and roadmap of the aeromechanical probabilistic analysis capability development are also discussed.

Low-density polyethylene simulated production in high pressure tubular reactor

The importance of the LDPE polymerization process has spawned a slew of LDPE tubular reactor modelling and simulation projects. Through adjusting input parameters, the sensitivity study of input parameters in an industrial low density polyethylene tube reactor was addressed in this paper. This work's innovation was highlighted with the incorporation of the LDPE melt flow index in the reactor output. The highest temperature peak and monomer conversion were obtained when initiator concentration were increased by 1.06 factor, whereas the highest melt flow index was obtained when the inlet temperature was increased by 1.07 factor. The monomer flow rate, initiator flow rate, and solvent flow rate all had a substantial impact on the LDPE process performance in a tubular reactor.

Inventory model for instantaneous deteriorating items with time sensitive demand for post COVID-19 recovery

The Covid-19 epidemic has caused substantial obstacles to the supply network globally. Hence there is urgency and necessity to build a model for cash flow in the chain of demand and supply system. This research suggests an inventory model to assist retailers in determining the optimal ordering quantity and replenishment cycle to reduce the total cost in different payment cases. The current study looks toward a partial advance and delays in the payment system considering time-sensitive demand, shortage, and partial backlogging for instantaneous deteriorating items. During the financial crisis, the partial advance and delay-in-payment strategy is planned to keep orders flowing from retailers to suppliers and customers to retailers. The impact of advanced and delayed payments on the total cost of a retailer is examined. To exemplify the model?s application, numerical examples are used. A sensitivity study of critical parameters has been done to identify more sensitive parameters which reveal the clear depiction of present problems.

Study of a novel ternary second-order viscosity model on Al2O3-water nanofluid

In terms of heat dissipation, nanofluids with strong thermal conductivity are becoming more and more common, attracting more and more research and attention to date.In this study, Al2O3-H2O nanofluids were studied using molecular dynamics simulations and model parameterization. The dynamic viscosity distribution patterns were obtained at various temperatures(290K~360K), nanoparticle volume fractions(1.24%~6.2%), and particle sphericity(0.69~1.0), and an efficient ternary second-order polynomial viscosity prediction model was proposed on the basis of these results.The findings demonstrate the model's goodness-of-fit with a coefficient of determination over 0.96 and a root mean square error under 0.05, as well as its high predictive ability with a maximum relative error between simulated and predicted values under 9%. Using this viscosity prediction model, a subsequent parametric sensitivity study showsthat the volume fraction had the most significant impact on viscosity, exhibiting not just a second order effect but also an interacting effect with temperature and sphericity. The relative nanofluid viscosity, which is the ratio of nanofluid viscosity to aqueous base fluid viscosity, exhibits a convex parabolic growth at constant temperature and sphericity and increases more quickly at the same volume fraction the higher the temperature. The viscosity of the nanofluid increases by up to 34% when the volume fraction is equal to 6.28% and the particle sphericity is equal to 1.An efficient viscosity prediction model makes it easier to control important variables to reduce energy consumption during flow and increase its capacity to dissipate heat.

Parameter Sensitivity Study on Static and Dynamic Mechanical Properties of the Spatial Y-shaped Tied Arch Bridge

Parameter Sensitivity Study on Static and Dynamic Mechanical Properties of the Spatial Y-shaped Tied Arch Bridge

Influence of Rock Properties on Structural Failure Probability—Caprock Shale Examples from the Horda Platform, Offshore Norway

In any geological subsurface fluid injection, a viable top seal is required to contain the vertical movement of the injected fluid plume. However, seal integrity assessment is challenging because of the uncertainties possessed by various parameters. A probabilistic solution might be more appropriate when significant uncertainties are present. In this study, we evaluate Drake shale caprock structural reliability using a stochastic method. Drake shale is the primary top seal in the Aurora CO2 storage site, located in the Horda Platform area in the northern North Sea. Based on the lithological variations, Drake caprock shale is divided into two parts designated by upper and lower units. Six model scenarios from the upper and lower Drake units have been tested. The probabilistic structural failures of varying model scenarios are estimated using the First-Order Reliability Method (FORM). Drake Formation shale shows a considerably low probability of failure (~0) with a high reliability index in the initial stress-state condition and after-injection scenarios. Moreover, the parameter sensitivity study indicates that horizontal stress and cohesion are the most influential input parameters during reliability estimation. Comparative analysis between the caprock properties and failure probability reveals that rock strength properties such as cohesion and friction angle strongly dictate the probability of failure estimation. Moreover, comparing two caprock shale formations indicates that the structural failure values are not correlatable; hence, a formation-specific failure assessment is recommended.

Fluid dynamics analysis and optimized throttling design of L-shaped multi-stage high pressure reducing valve

Traditional high-pressure pressure reducing valves are mostly linear, occupying a lot of space and costing a lot. In this study, a L-shaped multi-stage high pressure reducing valve (L-HPRV) is newly designed, it can be widely used in space constrained pipelines, reducing floor space and thus lower costs. Then, the steam flow in L-HPRV is investigated with an experimental validated numerical method. Next, a parametric sensitivity study is conducted to investigate the effects of typical throttling components on fluid dynamics of the newly designed L-HPRV. Finally, based on the parametric sensitivity study, the optimized throttling design of L-HPRV is achieved. The results show that the rhombus-shaped perforated plates is considered to be the first choice for better fluid dynamics. Larger perforated plate diameter (D1/D0) and perforated sleeve diameter (D2/D0) relates to better aerodynamic performance. Chamfer radius has limited effects on fluid dynamics. N-stage adjustable perforated plate effectively disperses the pressure drop and velocity rising gradient in the throttling components at all levels. The preferred level ranking with better fluid dynamics for L-HPRV is obtained.

A physics-based life prediction model of HP40Nb heat-resistant alloy in a coupled creep-carburisation environment

This paper reports the development of a physics-based life prediction model that incorporates three micro-damage processes. Both the particle area fraction and size, as determined using scanning electron microscopy, are used as the model input to predict the precipitate coarsening damage. Moreover, the carbon concentration profile, as measured using electron probe micro-analyser, are used to derive the carburisation damage distribution index. Compared to the Kowalewski-Dyson model, the present model can predict the stress dependent creep fracture strain and rupture time. The model, compiled into finite element via the user subroutine, can predict the heterogeneous damage accumulation of the cracking furnace tube made of HP40Nb alloy in a coupled creep-carburisation environment. The predicted total damage is comprised of precipitate coarsening and carburisation as the predominance, while the creep cavitation contributing much less due to the low internal pressure. The distribution of the precipitate coarsening is relatively homogeneous through the thickness, whereas the carburisation causes a steep increased damage value close to the inner surface. With regard to time, the precipitate coarsening damage is predominant in the initial stage, while the total damage is governed by the carburisation process with further exposure. Eventually, the total damage reaches the critical value at 59600 h, with the thickness of carburised layer exceeding 50% of the tube thickness, and the tube fails in the inner surface which agrees with the operational experience. The parametric sensitivity study reveals the importance of determining the Cr-rich carbide area fraction for the life prediction.