Numerical Case Research Articles

Heat transfer inside annular passages: A comparison of non-circular and fully circular passages using eco-friendly graphene nanofluid

This research investigates the thermal performance of an annular passage using eco-friendly nanofluids as substitutes for conventional working fluids. Specifically, the study focuses on the utilization of covalently functionalized Graphene Nanoplatelets (GNPs) dispersed in an aqueous medium, with Gallic acid as the functionalizing agent. Nanofluids were synthesized at different weight concentrations (0.1 wt%, 0.05 wt%, and 0.025 wt%) and exhibited an excellent long-term colloidal stability for 39 days, with a relative concentration of 94.4% at 0.025 wt%. Thermophysical property evaluations revealed significant enhancements of 18.25% and 8.23% in thermal conductivity and viscosity, respectively at 0.1 wt%. The nanofluids exhibited average enhancements of convection heat transfer coefficient evaluations indicating improved thermal performance for GaGNPs/DW nanofluids, with the highest average enhancement of approximately 12.11% achieved at 0.1 wt%. To investigate the impact of different annular geometries, a numerical study (CFD) was conducted. Various inner rod shapes, including circular, rectangular, square, triangular, and pentagonal, were considered. The pentagonal cross-section exhibited the highest average increase in convection heat transfer coefficient at 13.9%, followed by rectangular and triangular shapes. In a numerical case study with different thermal boundary conditions, the circular inner rod shape demonstrated the highest thermal performance at 0.1 wt%.

Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

Groundwater contamination source (GCS) parameter identification can help with controlling groundwater contamination. It is proverbial that groundwater contamination concentration observation errors have a significant impact on identification results, but few studies have adequately quantified the specific impact of the errors in contamination concentration observations on identification results. For this reason, this study developed a Bayesian-based integrated approach, which integrated Markov chain Monte Carlo (MCMC), relative entropy (RE), Multi-Layer Perceptron (MLP), and the surrogate model, to identify the unknown GCS parameters while quantifying the specific impact of the observation errors on identification results. Firstly, different contamination concentration observation error situations were set for subsequent research. Then, the Bayesian inversion approach based on MCMC was used for GCS parameter identification for different error situations. Finally, RE was applied to quantify the differences in the identification results of each GCS parameter under different error situations. Meanwhile, MLP was utilized to build a surrogate model to replace the original groundwater numerical simulation model in the GCS parameter identification processes of these error situations, which was to reduce the computational time and load. The developed approach was applied to two hypothetical numerical case studies involving homogeneous and heterogeneous cases. The results showed that RE could effectively quantify the differences caused by contamination concentration observation errors, and the changing trends of the RE values for GCS parameters were directly related to their sensitivity. The established MLP surrogate model could significantly reduce the computational load and time for GCS parameter identification. Overall, this study highlights that the developed approach represents a promising solution for GCS parameter identification considering observation errors.

Study on the attributes of transverse surface waves in a layered piezoelectric semi-space with surface elasticity theory and extended stroh formalism

Previous works on surface acoustic wave sensors have shown great limitations in selecting piezoelectric materials and the wave propagation direction. To eliminate such limitations in the technological revolution of SAW sensors, the current paper's main purpose is to explore how wave propagation orientation affects the performance of SAW macro- and nano-sensors. Based on Extended Stroh formalism, the theoretical forms are derived and exploited to present the wavenumber of transverse waves in an arbitrary direction of the piezoelectric medium. Furthermore, we consider surface elasticity theory to acquire the phase velocity equation on the basis of the expression of the wavenumber. More intuitively, a physical model is set up to obtain the horizontal shear stiffness of the surface and bulk layers. Then, the numerical case is carried out to determine the relationship between phase velocity and wave propagation orientation. By comparing the numerical study and the physical model, it can be found that the empirical formula of shear stiffness for bulk and surface layers offers a helpful route to precisely predict the mechanical attributes of SAW macro- and nano-sensors, respectively. The summaries of the current theoretical work benefit the manufacturing of surface acoustic wave sensors with improved performance.

The influence of cross‐sectional aspect ratio on ceiling temperature profile and mass flow rate of ceiling jet in tunnel fires

AbstractIn this paper, the influence of tunnel cross‐sectional aspect ratio on the ceiling temperature profile and mass flow rate (MFR) of ceiling jet is studied theoretically and numerically, and 13 tunnel cross sections with different aspect ratios () are considered. A total of 26 full‐scale numerical simulation cases are conducted using Fire Dynamics Simulator, and small‐scale experiments are used to verify the accuracy of the simulations. Results show that the maximum ceiling temperature is more sensitive to the tunnel height and decreases with increasing aspect ratio, which can be divided into two regions, <1 and ≥1. When ≥1, the maximum ceiling temperature varies more linearly. The initial locations of the one‐dimensional spread for the tunnel with different tunnel cross‐sectional aspect ratios are similar, which are concentrated at 15–20 m from the fire source when taking the MFR increase rate of 0.001 as the criterion. By introducing the sectional coefficient, the MFR model and temperature attenuation model of ceiling jet are developed for the tunnels with <1 and ≥1, respectively. The results of this paper could provide definite reference value for the smoke control in tunnel fires.

ProPTV: A probability-based particle tracking velocimetry framework

We present proPTV, a comprehensive framework for particle tracking velocimetry. The framework is an open-source software project and written in Python. It provides the user with all tools needed to process raw camera images of a particle-seeded flow in order to reconstruct the particle dynamics and to assimilate pressure fields. The advanced probabilistic tracking method enables accurate reconstruction of the most probable particle trajectories. Its performance is studied applying it to the numerical test case of turbulent Rayleigh-Bénard convection (Pr=6.9, Ra=1010) in a cubic cell generated by direct numerical simulation. For the highest tracer particle density of about 0.125ppp of this test case, 83% of the reconstructed tracks are correct. To check the performance also for experimental data, proPTV is additionally applied to particle measurements of turbulent Rayleigh-Bénard convection in a water-filled cell for similar Ra- and Pr-numbers as the numerical test case. Thereby, a tracer particle density of about 0.02 ppp is estimated. The obtained results are then compared with those obtained using LaVision's commercial particle tracking software DaVis (v10.2.1). Both frameworks provide velocity fields that have small deviations. However, the particle tracks generated by proPTV are on average 5 times longer than those generated with DaVis. proPTV including the numerical test case is available at: https://github.com/RobinBarta/proPTV.

Convex-up Style of Deformation within Grabens in Regions of Incomplete Crossing Conjugate Normal Faults: A Numerical Simulation Investigation and Case Study

Convex-up Style of Deformation within Grabens in Regions of Incomplete Crossing Conjugate Normal Faults: A Numerical Simulation Investigation and Case Study

Different type of pay later facility for green product with selling price dependent demand using grey wolf optimizer

The use of trade credit finance is becoming more widely acknowledged as a crucial approach to improving inventory system profitability. We review an inventory model with depending on permitted payment delays for which, if the retailer place an orders higher than or equal to a predefined quantity S1, then the supplier will provide a fully pay in later facility of ξ periods (i.e., there will be no charge of interest until ξ). On the other hand the retailer need to pay a partial amount of payment to the supplier if the order quantity is less than S1, and the remaining amount may be deferred for up to ξ periods. Main objective of this study is to investigate the inventory model with different situations under delayed payment facility. In addition, determining the product's demand also involves taking into account the item's greenness and selling price. We have also considered the fact that the cost of buying is influenced by the product's degree of greenness. We employ the meta heuristic algorithm Grey Wolf Optimizar (GWO) to assist us in solving the problem, and we compare the outcomes with the aid of a few other algorithms (Whale optimisation algorithm (WOA) and Artificial electric field algorithm (AEFA)). In the end, we resolve several numerical cases to support the model. The concavity of the desired function is graphically displayed using MATLAB software.

Deep learning the efficient frontier of convex vector optimization problems

In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater’s condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of CVOPs. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.

Preventive maintenance policy optimization for a weighted k-out-of-n: G system using the survival signature

This paper presents a novel preventive maintenance (PM) policy for a weighted k-out-of-n: G system. The system consists of critical components with high weight and other low-weight components, while the critical ones significantly impact the system's total weight. The lifetime distribution of critical and other components follows the Weibull distribution with different shape and scale parameters. Different maintenance actions are performed on the component according to the number of failed critical components and the total weight of the working components. The expected cost per renewal cycle and system availability are analyzed based on survival signature. The optimal PM cycle and the inspection period are derived by minimizing the expected cost per renewal cycle or maximizing availability. Finally, the effectiveness of the presented strategy is illustrated by a numerical case.

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

Reliability evaluation of a multi-state system (MSS) with dependent components makes much practical sense because the independent identical assumption (i.i.d.) assumption between different components is sometimes impractical in the context of real engineering cases. The task becomes more challenging if imprecision gets involved due to the pervasive uncertainty. The loss of monotony resulting from the introduction of imprecise parameters makes many analytical reliability methods not applied. To address this challenge, in this paper, we develop a survival signature-based reliability framework for an MSS taking into account both dependence and uncertainty. In our framework, the survival function is derived through some unique structural reliability treatments. Vine copula and imprecise probability are integrated and embedded within the framework to address the case that dependence and imprecision simultaneously appear. Implementation-wise, two numerical simulation algorithms are developed to address some complicated cases in which the analytical solution is not available. For demonstration and validation, both the numerical case and application examples are presented. The results show the superiority of the proposed method and its potential in real engineering use.

Mathematical Formulation of Stoichiometric Subspaces Associated with Chemical Reaction Networks: The Linear Algebraic Foundations and Fundamentals

The article presents a consolidated review of the mathematical framework for construction of Stoichiometric subspaces associated with Chemical Reaction Networks, based on theoretically imposed constraints, mechanistic considerations and data driven mathematical modeling; A rigorous mathematical treatment of the problem is presented and is illustrated using an appropriate numerical case study example.

Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study

Abstract. Ice microphysics controls cloud electrification in thunderstorms, and the various secondary ice production (SIP) processes are vital in generating high ice concentrations. However, the role of SIP in cold-season thunderstorms is not well understood. In this study, the impacts of SIP on the electrification in a thunderstorm that occurred in late November are investigated using model simulations. The parameterizations of four SIP processes are implemented in the model, including the rime splintering, ice–ice collisional breakup, shattering of freezing drops, and sublimational breakup of ice. In addition, a noninductive charging parameterization and an inductive charging parameterization, as well as a bulk discharging model, are coupled with the spectral bin microphysics scheme. The macroscopic characteristics and the temporal evolution of this thunderstorm are well modeled. The radar reflectivity and flash rate obtained by adding four SIP processes are more consistent with the observations than those without SIP. Among the four SIP processes, the rime splintering has the strongest impact on the storm. The graupel and snow concentrations are enhanced while their sizes are suppressed due to the SIP. The changes in the ice microphysics result in substantial changes in the charge structure. The total charge density changes from an inverted tripole structure to a dipole structure (tripole structure at some locations) after four SIP processes are considered in the model, mainly due to the enhanced collision between graupel and ice. These changes lead to an enhancement of the vertical electric field, especially in the mature stage, which explains the improved modeling of flash rate. The results highlight that cold-season cloud electrification is very sensitive to the SIP processes.

Meta-learning-based continuous state-space models for rapid monitoring using heterogeneous grade sources with uneven sampling

In an industrial batch, it is common to produce products with different grades, so only a limited number of data are available for each grade of product. The data are also easily contaminated by external disturbances and noise. The sampling interval would be messy and irregular if contaminated samples were removed. Thus, it is difficult to build models for monitoring the corresponding process. This paper proposes a meta-learning-based functional continuous state space model for fast modeling and monitoring of multi-grade batch processes. It takes advantage of wavelet function properties to effectively handle the irregular sampling issue. In addition, the meta-learning method uses diverse historical multi-grade datasets for modeling. It rapidly establishes an efficient and reliable monitoring model for new running batches with a small number of batch data. The effectiveness of the proposed method is shown through a numerical case and an industrial case in the chemical batch process.

Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach

Dynamic reliability assessment has offered a new paradigm for engineered systems to integrate multi-level observations to update the reliability measures of a specific running system. The existent work on dynamic reliability assessment can only leverage precise observations and cannot be straightforwardly implemented in scenarios where observations are imprecise. However, the observations are inevitably imprecise owing to the limited accuracy of inspection techniques and vague judgments of the system state. In engineering scenarios, multi-state systems (MSSs) with a hierarchical structure are commonly existent, and the imprecise observations can be collected across multiple physical levels of the system. In this article, the uncertainty associated with imprecise observations is characterized by the evidence theory, and it can be therefore regarded as an evidential form. A dynamic Bayesian network (DBN) model is utilized to evaluate the reliability of hierarchical MSSs with multi-level evidential observations. Subsequently, the evidence theory is implemented to quantify epistemic uncertainties associated with imprecise observations. These observations, sourced from multiple physical levels, are merged by the DBN model using the Dempster rule of combination (DRC) to update the reliability of a specific running system in a dynamic fashion. The feasibility and correctness of the proposed method have been demonstrated through a numerical case and a real engineering case of a kerosene filling control system (KFCS), and the result indicates that the proposed DBN-based algorithm methods have high applicability and generalizability.

Time-frequency analysis of plate-shell coupled structures under moving stochastic load

Random vibration is of critical importance in understanding the dynamic behavior of engineering structures, especially the nonstationary responses for their coupled enclosers consisting of plates and shells subjected to moving stochastic loads. In this study, the pseudo excitation method (PEM) is integrated into the method of reverberation ray matrix (MRRM) to establish a full-domain stochastic dynamic system. With the help of rearrangement matrices and coupling spring connection technologies, the two plate-shell models of sequential and reinforced coupling are considered by employing the simple first-order shear deformation shell theory (S-FSDST) and Hamilton's principle. Then, the continuous movement of the excitation between each substructure is achieved through load coordinate normalization in nonhomogeneous equations, which are separated from the generalized solutions in the frequency domain. Therefore, it can be easily converted into a global pseudo-excitation and obtain the time-history responses relying on the inverse Laplace transform. After matching the present results with the finite element software and Monte Carlo simulation (MCS), the accuracy and efficiency of the proposed RRM-PEM are well-confirmed. Finally, by performing a series of numerical cases of power spectral density (PSD) and time-varying root mean square (RMS) under different loading velocities, frequency bands and system modal dampings, the time-frequency rules for different substructures of closed plate-shell coupled system under moving random loads are revealed for the first time.

An Incremental Interpolation Scheme With Discrete Cosine Series Expansion for Multimaterial Topology Optimization

Abstract Topology optimization is a powerful tool for structural design, while its computational cost is quite high due to the large number of design variables, especially for multilateral systems. Herein, an incremental interpolation approach with discrete cosine series expansion (DCSE) is established for multilateral topology optimization. A step function with shape coefficients (i.e., ensuring that no extra variables are required as the number of materials increases) and the use of the DCSE together reduces the number of variables (e.g., from 8400 to 120 for the optimization of the clamped–clamped beam with four materials). Remarkably, the proposed approach can effectively bypass the checkerboard problem without using any filter. The enhanced computational efficiency (e.g., a ∼89.2% reduction in computation time from 439.1 s to 47.4 s) of the proposed approach is validated via both 2D and 3D numerical cases.

A Developed Model and Fuzzy Multi-Criteria Decision-Making Method to Evaluate Supply Chain Nervousness Strategies

Nervousness is thought to be a source of confusion, instability, or uncertainty in SC systems due to disruptions and frequent changes in decisions. Nervousness persists even with consistent SCs, which arise from planning flexibility in response to changes, where responsiveness and customer satisfaction balance. Although the term “nervousness” is well known, to our knowledge no prior research has examined and explored supply chain nervousness strategies (SCNSs). This research explores supply chain nervousness strategies, factors, reduction methods, and recent trends in the supply chain’s relationship with nervousness. The main purpose of this research is to determine the comprehensive and relevant nervousness strategies in the supply chains, especially in light of the unprecedented development and change in business, economics, and technology and the fierce competition. SCN strategies are introduced in a developed model to designate SCN measurements and indicators, mitigation strategies and stages, and management strategies. The fuzzy PROMETHEE method is employed to rank the strategies based on their importance and order of implementation. The suggested method for managing nervousness is then presented with a numerical case, along with the results. The research outcomes indicate that the top five strategies for managing nervousness include planning continuity, utilizing technology, managing nervousness, improving the SC cyber system, and managing supplies. The findings assist decision makers, practitioners, and managers in focusing on SC improvement, resilience, and sustainability.

Inverse Scheme to Locally Determine Nonlinear Magnetic Material Properties: Numerical Case Study

We are interested in the determination of the local nonlinear magnetic material behaviour in electrical steel sheets due to cutting and punching effects. For this purpose, the inverse problem has to be solved, where the objective function, which penalises the difference between the measured and the simulated magnetic flux density, has to be minimised under a constraint defined according to the corresponding partial differential equation model. We use the adjoint method to efficiently obtain the gradients of the objective function with respect to the material parameters. The optimisation algorithm is low-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS), the forward and adjoint formulations are solved using the finite element (FE) method and the ill-posedness is handled via Tikhonov regularisation, in combination with the discrepancy principle. Realistic numerical case studies show promising results.

Reliability Assessment of Bridge Structure Using Bilal Distribution

Reliability assessments are pivotal in evaluating system quality and have found extensive application in manufacturing. This research delves into a system comprising five components, one of which is a bridge network. The components are presumed to follow a Bilal lifetime distribution with a failure rate that changes over time. Four distinct methods are employed to enhance the components within the system. This study involves the computation of δ-fractiles and reliability equivalence factors (REFs). Additionally, a numerical case study is provided to elucidate the theoretical findings.

An Enhanced Mapping Interpolation ISPH–FVM Coupling Method for Simulating Two-Phase Flows with Complex Interfaces

In this paper, an enhanced mapping interpolation ISPH–FVM coupling method is developed to improve the accuracy and stability of two-phase flows simulation. Similar to the original ISPH–FVM coupling method, the coupling framework between the numerical schemes of ISPH and FVM is established at the overlapping region of the ISPH particles and the FVM grids. Although the original ISPH–FVM coupling method can effectively predict movement and deformation of the two-phase interface, it is difficult to ensure the mass conservation in the flow domain during the information transfer between ISPH particles and FVM grids, thus weakening the numerical accuracy of interaction between different fluid phases. To improve the mapping interpolation accuracy and ensure the mass conservation, a volume fraction correction scheme combined with the particle approximate interpolation technique is introduced to form an enhanced mapping interpolation ISPH–FVM coupling method. The effect of surface tension is assessed by the continuum surface force (CSF) model. Several 2D/3D numerical cases are given to verify the efficiency and robustness of the present ISPH–FVM coupling method. Numerical results demonstrate that the enhanced mapping interpolation developed in the ISPH–FVM coupling method can reproduce the two-phase flow with complex interfaces effectively.