Modelling, Volume 4, Issue 4 (December 2023) – 13 articles

The prediction of the underwater-radiated noise for a vessel is classically performed at a model scale and extrapolated by semi-empirical laws. The accuracy of such a method depends on many parameters. Among them, the acoustic propagation model used to estimate the noise measured at a model scale is important. The present study focuses on the impact of the presence of a source in the transverse plane. The scattering effect, often neglected in many studies, is here investigated. Applying different methods for computation, we perform several simulations of the acoustic pressure field to show the influence of the scattered field. We finally discuss the results and draw some conclusions about the scattering effect in our experimental configuration. Full article

Perforated steel members (PSMs) are now frequently used in building construction due to their beneficial features, including their proven blast-resistance abilities. To safeguard against structural failures from explosions and terrorist threats, perforated steel beams (PSBs) and perforated steel columns (PSCs) offer a viable alternative to traditional steel members. This is attributed to their impressive energy absorption potential, a result of their combined high strength and ductile behavior. In this study, numerical examinations of damage assessment under the combined effects of gravity and blast loads are carried out to mimic real-world scenarios of external explosions close to steel structures. The damage assessment for PSBs and PSCs considers not just the initial deformation from the blast, but also takes into account the residual capacities to formulate dependable damage metrics post-explosion. Comprehensive explicit finite element (FE) analyses are performed with the LSDYNA software. The FE model, when compared against test results, aligns well across all resistance phases, from bending and softening to tension membrane regions. This validated numerical model offers a viable alternative to laboratory experiments for predicting the dynamic resistance of PSBs and PSCs. Moreover, it is advisable to use fully integrated solid elements, featuring eight integration points on the element surface, in the FE models for accurate predictions of PSBs’ and PSCs’ behavior under blast loading. A parametric study is presented to investigate the effect of web-opening shapes, retrofitting, and different blast scenarios. The results obtained from the analytical FE approaches are used to obtain the ductile responses of PSMs, and are considered an important key in comparisons among the studied cases. Full article

This article focuses on the interval estimation of the generalized Rayleigh distribution with scale and shape parameters. The generalized fiducial method is used to construct the fiducial point estimators as well as the fiducial confidence intervals, and then their performance is compared with other methods such as the maximum likelihood estimation, Bayesian estimation and parametric bootstrap method. Monte Carlo simulation studies are carried out to examine the efficiency of the methods in terms of the mean square error, coverage probability and average length. Finally, two real data sets are presented to demonstrate the applicability of the proposed method. Full article

Small bodies in the Solar System are appealing targets for scientific and technological space missions, owing to their diversity in intrinsic and extrinsic properties, besides orbit and other factors. Missions to small bodies pass through the critical onboard object detection phase, where the body’s light becomes visible to the spacecraft camera. The relative line-of-sight to the object is acquired and processed to feed relative guidance and navigation algorithms, therefore steering the spacecraft trajectory towards the target. This work assesses the distance of detection for each small body in the Solar System considering the target radiometric properties, three typical spacecraft camera setups, and the relative observation geometry by virtue of a radiometric model. Several uncertainties and noises are considered in the modelling of the detection process. The detection distances for each known small body are determined for small-, medium-, and large-class spacecraft. This proves useful for early mission design phases, where a waypoint for detection needs to be determined, allowing the shift from an absolute to a relative guidance and navigation phase. The work produces an extensive dataset that is freely accessible and useful for teams working on the design phases of space missions. Full article

Social media significantly influences business, politics, and society. Easy access and interaction among users allow information to spread rapidly across social networks. Understanding how information is disseminated through these new publishing methods is crucial for political and marketing purposes. However, modeling and predicting information diffusion is challenging due to the complex interactions between network users. This study proposes an analytical approach based on diffusion models to predict the number of social media users engaging in discussions on a topic. We develop a modified version of the susceptible–infected (SI) model that considers the heterogeneity of interactions between users in complex networks. Our model considers the network structure, abandons the assumption of homogeneous mixing, and focuses on information diffusion in scale-free networks. We provide explicit algorithms for modeling information propagation on different types of random graphs and real network structures. We compare our model with alternative approaches, both those considering network structure and those that do not. The accuracy of our model in predicting the number of informed nodes in simulated information diffusion networks demonstrates its effectiveness in describing and predicting information dissemination in social networks. This study highlights the potential of graph-based epidemic models in analyzing online discussion topics and understanding other phenomena spreading on social networks. Full article

We consider the problem of system-level balanced scheduling in a pediatric hospital setting. A hospital clinic has a queue for patients needing care. After being seen in clinic, many require follow-up surgery, for which they also wait in a queue. The rate-limiting factor is physician availability for both clinic visits and surgical cases. Although much existing work has been done to optimize clinic appointments, as well as to optimize surgical appointments, this novel approach models the entire patient journey at the system level, through both clinic and surgery, to optimize the total patient experience. A discrete-event simulation model of the system was built based on historic patient encounter data and validated. The system model was then optimized to determine the best allocation of physician resources across the system to minimize total patient wait time using machine learning. The results were then compared to baseline. Full article

Buried flexible pipes are allowed to deflect up to 2–5% of the pipe diameter, which can become problematic for the connected direct-bury, large-diameter butterfly valves. The complex behavior of the pipe–valve–soil system makes it difficult to predict the deflection of the pipe/valve system. In the absence of field/experimental studies, the application of finite element analysis (FEA) seems necessary to predict deflection and stresses and to avoid potential downtime associated with disruption of service. This paper described the FEA of a large-diameter pipe–valve system, with different backfills under gravity, overburden, and internal pressure loads. The effects of modeling different components of the system (e.g., flanges, bearing housing, gate disc, etc.) were described and investigated. The goal of this study was to provide insight into the design and installation of direct-bury pipe–valve systems and evaluate current installation methods in the absence of guidelines. In addition, the level of modeling details required for FEA to yield accurate results was discussed. Full article

The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of the Kalman filter used is presented for the case of uncertain model parameters, which should not or cannot be estimated due to a lack of necessary measurements. It is shown that incorrectly assumed probability density functions for present uncertainties adversely affect the model parameter to be estimated. Therefore, the problem is embedded in a multilayered uncertainty space consisting of the stochastic space, the interval space, and the fuzzy space. Then, we propose classifying all present uncertainties into aleatory and epistemic ones. Aleatorically uncertain parameters can be used directly within the EnKF without an increase in computational costs and without the necessity of additional methods for the output evaluation. Epistemically uncertain parameters cannot be integrated into the classical EnKF procedure, so a multilayered uncertainty space is defined, leading to inevitable higher computational costs. Various possibilities for uncertainty quantification based on probability and possibility theory are shown, and the influence on the results is analyzed in an academic example. Here, uncertainties in the initial conditions are of less importance compared to uncertainties in system parameters that continuously influence the system state and the model parameter estimation. Finally, the proposed extension using a multilayered uncertainty space is applied on a multi-degree-of-freedom (MDOF) laboratory structure: a beam made of stainless steel with synthetic data or real measured data of vertical accelerations. Young’s modulus as a model parameter can be estimated in a reasonable range, independently of the measurement data generation. Full article

This research brings a new analysis method for a continuous water supply distribution network. The number of house service connections in different story buildings, rather than the nodal peak demand, shall be accounted for in the analysis. This work aims to consider the flow when pipes are opened in the house plumbing systems. The approach deviates from a traditional peak demand-based analysis of the water distribution network. The analysis gives the flow rate at each nodal point that could be observed in the different story buildings. The methodology is applied to a hypothetical network and shows how much flow and nodal pressure can occur when different percentages of consumers are in an active state. This study indicates that the peak demand-based sizing of the supply pipes could have a deficient capacity under real scenarios. The proposed analysis method will help to understand the actual behavior of the network. Full article

Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization. To overcome this curse of dimensionality and to minimize computational resource requirements, this research demonstrates a user-friendly approach to formulating a reduced-dimension surrogate model that represents a high-dimensional, high-fidelity source model. This approach was developed specifically for a non-expert using commercially available tools. In this approach, the complex physical behavior of the high-fidelity source model is separated into individual, interacting physical behaviors. A separate reduced-dimension surrogate model is created for each behavior and then all are summed to formulate the reduced-dimension surrogate model representing the source model. In addition to a substantial reduction in computational resources and comparable accuracy, this method also provides a characterization of each individual behavior providing additional insight into the source model behavior. The approach encompasses experimental testing, finite element analysis, surrogate modeling, and sensitivity analysis and is demonstrated by formulating a reduced-dimension surrogate model for the damage tolerance of an aluminum plate reinforced with a co-cured bonded E-glass/epoxy composite laminate under four-point bending. It is concluded that this problem is difficult to characterize and breaking the problem into interacting mechanisms leads to improved information on influential parameters and efficient reduced-dimension surrogate modeling. The disbond damage at the interface between the resin and metal proved the most difficult mechanism for reduced-dimension surrogate modeling as it is only engaged in a small subspace of the full parameter space. A binary function was successful in engaging this damage mechanism when applicable based on the values of the most influential parameters. Full article

The global quasi-linearization (GQL) is used as a method to study and to reduce the complexity of mathematical models of mechanisms of chemical kinetics. Similar to standard methodologies, such as the quasi-steady-state assumption (QSSA), the GQL method defines the fast and slow invariant subspaces and uses slow manifolds to gain a reduced representation. It does not require empirical inputs and is based on the eigenvalue and eigenvector decomposition of a linear map approximating the nonlinear vector field of the original system. In the present work, the GQL-based slow/fast decomposition is applied for different combustion systems. The results are compared with the standard QSSA approach. For this, an implicit implementation strategy described by differential algebraic equations (DAEs) systems is suggested and used, which allows for treating both approaches within the same computational framework. Hydrogen–air (with 9 species) and ethanol–air (with 57 species) combustion systems are considered representative examples to illustrate and verify the GQL. The results show that 4D GQL for hydrogen–air and 14D GQL ethanol–air slow manifolds outperform the standard QSSA approach based on a DAE-based reduced computation model. Full article

The conventional ways to construct an open-to-circular hollow section (CHS) connection are either to directly weld the open section to the CHS column wall or to use local stiffeners (e.g., diaphragms) and gusset plates to connect the two structural components. These construction methods often subject the CHS to severe local distortions and/or require high welding quantities, hindering the real-life application of hollow sections. To overcome such difficulties, this study proposes two types of moment-resisting “passing-through” connection configurations, developed within the European research project “LASTEICON”. These configurations consist of main beams connected to the CHS column via either an I-section or individual steel plates passing through the CHS column. The passing-through system is implemented using laser cut and weld technology and efficiently avoids excessive use of stiffening plates, local damages on the CHS wall and premature flange failures. The proposed configurations are investigated experimentally and numerically under two different load cases in order to characterize their structural behaviour. Finite element models have been developed and calibrated with respect to the experimental force–displacement behaviour of the connections as well as their observed failure modes. The efficiency, benefits, and limitations of the modelling approach were discussed through a detailed comparison study between the experimental and numerical results. Full article

In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results in higher costs, and thus, a stochastic analysis considering uncertainty and spatial variability was developed to reduce costs. In the past few decades, machine learning has been greatly developed and extensively used in stochastic slope stability analysis, particularly used as surrogate models to improve computational efficiency. To better summarize the current application of machine learning and future research, this paper reviews 159 studies of supervised learning published in the past 20 years. The achievements of machine learning methods are summarized from two aspects—safety factor prediction and slope stability classification. Four potential research challenges and suggestions are also given. Full article