Modeling Methodology Research Articles

Comparison of modelling methodologies for large porous absorbers hanging in a volume

Porous material simulation methodologies are well established. However, most methods, like the Transfer-Matrix Method (TMM) make the assumption that there is a source side and a receiver side. In practice, this works for the majority of industrial noise control treatments. For architectural applications, this is not always the case. Hanging absorbers and curtains are common materials that add absorption to a space, but do not have a boundary condition at a wall or a well-defined source and receiver side. To evaluate the validity of existing simulation methods for this condition, a reverb room test was simulated with a large cube of melamine foam inside the volume. The effective absorption of the cube in the space was then calculated using various simulation methodologies, and compared to results from a measured reverb room absorption test.

Improving Spatio-Temporal Topic Modeling with Swarm Intelligence

This study introduces innovative methodologies for spatiotemporal topic modeling applied to the TripAdvisor forum of Morocco, leveraging the diverse and geographically tagged user-generated content. We develop and evaluate two schemas integrating Latent Dirichlet Allocation (LDA) with advanced clustering techniques, including a hybrid K-Means algorithm that incorporates Genetic Algorithms and the Artificial Bee Colony method. The first schema independently processes user threads, publication times, and locations using LDA, followed by clustering, while the second schema combines these dimensions into a unified vector for holistic LDA application, facilitating direct comparisons of clustering efficacy. Our findings demonstrate that swarm intelligence significantly boosts clustering performance, especially for larger clusters, and enhances the visualization of complex data relationships. These insights offer actionable intelligence for tourism stakeholders and underscore the practical benefits of advanced computational techniques in harnessing user-generated content for strategic decision-making.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

A CFD-based compartmental modelling approach for long-term dynamic simulation of water resource recovery facilities

ABSTRACT This article presents a methodology for compartmental model (CM) creation for long-term simulation of water resource recovery facilities (WRRFs). CMs are often focused on reproducing with a lower computational cost, previously simulated scenarios. In contrast, the methodology presented here can represent variable hydraulic conditions, based on the interpolation of data gathered from a set of computational fluid dynamics (CFD) simulations that reproduce representative hydraulic scenarios. This is achieved by modelling with bidirectional flows the exchange flows between fixed compartments, which are defined based on the geometry of the reactors. The resultant hydraulic surrogate model can be implemented in commercial water treatment software to solve biochemical kinetics. The methodology was applied to simulate in WEST®-DHI a WRRF in Vila-Real, Spain. In this contribution, the CM was validated with real plant data. The developed CM provided a quick response simulation with a high level of hydraulic and biochemical detail. This allowed observation of a spatial distribution of component concentration, which could help with sensor location or plant optimisation. The methodology presented here could also be a useful enabler of digital twins to be implemented in WRRF.

Effect of Carbon on Void Nucleation in Iron.

The study reports the significance of carbon presence in affecting void nucleation in Fe. Without carbon, void nucleation rates decrease gradually at high temperatures but remain significantly high and almost saturated at low temperatures. With carbon present, even at 1 atomic parts per million, void nucleation rates show a low-temperature cutoff. With higher carbon levels, the nucleation temperature window becomes narrower, the maximum nucleation rate becomes lower, and the temperature of maximum void nucleation shifts to a higher temperature. Fundamentally, this is caused by the change in effective vacancy diffusivity due to the formation of carbon-vacancy complexes. The high sensitivity of void nucleation to carbon comes from the high sensitivity of void nucleation to the vacancy arrival rate in a void. The void nucleation is calculated by first obtaining the effective vacancy diffusivity considering the carbon effect, then calculating the defect concentration and defect flux change considering both carbon effects and pre-existing dislocations, and finally calculating the void nucleation rate based on the recently corrected homogeneous void nucleation theory. The study is important not only in the fundamental understanding of impurity effects in ion/neutron irradiation but also in alloy engineering for judiciously introducing impurities to increase swelling resistance, as well as in the development of simulation and modeling methodologies applicable to other metals.

The Demand for Real Money Balances in Zimbabwe: An Error Correction Estimation

Purpose: The purpose of the study is to examine the stability of the money demand function in Zimbabwe. Understanding, the money demand function is a prerequisite for effective monetary policy formulation and understanding the monetary transmission process and shocks propagation in the economy. Materials and Methods: The study employs the error correction modeling methodology to investigate the money demand function for Zimbabwe using quarterly data from 2017q2 – 2023q2. The analysis is expanded to characterize the monetary transmission mechanism following a shock to the price level and how the demand for real money balances responds to both single period and multiple shocks. Findings: The findings confirm a stable long run money demand function that is subject to short run dynamics. In addition, real money demand responds positively to real GDP (Scale factor) and inversely related to the price level (inflation). Short term dynamics (particularly inflation expectations) compound real money demand collapse in response to rising inflation. Implications to Theory, Practice and Policy: Monetary Policy must aim to collapse inflation expectations through a tight monetary control program and a functioning interbank market for foreign exchange to avoid surging parallel market activity.

Methodology for modeling and optimizing liquid cooling plates in the design of electronic equipment

Problem formulation. When solving the problem of excessive heat generation, which occurs when operating radio-electronic devices with a high density of electronic components and increased power, it is necessary to use cooling. Otherwise, this problem can lead to overheating of the electronics, which in turn can cause malfunctions of the devices or even their failure. Purpose. Development of a technique that will improve the cooling process of radioelectronic devices due to op-thymization of the parameters of liquid cooling plates. Results. A mathematical statement was formed, a modeling technique was proposed and procedures for optimizing liquid cooling plates were performed. Based on the developed 3D model of the liquid cooling plate, multivariate analysis procedures have been implemented using modern automated design tools. The liquid cooling system was optimized using modern modeling software, in particular the Solidworks Simulation program. Practical significance. The presented simulation results can be used in designing and creating cooling systems for various devices and devices.

Design of cold-formed high strength steel irregular octagonal hollow section columns

This paper presents experimental numerical investigations on the structural behaviour of cold-formed high strength steel irregular octagonal hollow section (OctHS) columns with both non-slender and slender cross-sections. An experimental programme, including material tests, measurements of initial global geometric imperfection, and pin-ended column tests, was first conducted. The key findings from the experimental programme are presented and discussed. Following the validation against the experimental results, the proposed finite element modelling methodology was established to replicate the test observations, and then was adopted to generate a boarder range of numerical results through parametric studies. The collected experimental and numerical data were used to evaluate the applicability of current structural steel design methods on the column buckling strength for irregular OctHS columns. Assessment results demonstrate that the design method adopted in Chinese code can provide accurate prediction, whilst modified design method adopted in Eurocode 3, considering the effect of steel grade on the column strength, has been proposed and shown to greatly enhance the prediction accuracy with improved consistency. The corresponding assessments on the design approaches adopted in American and Australian codes were also discussed.

Experiment and modeling of metal jet emission in magnetic pulse welding of Cu/Ti tubular joint

Magnetic pulse welding of Cu/Ti tubular joint is performed by creating intense transient magnetic fields that drive the metal components together at high velocity. This leads to interfacial waviness due to Kevin–Helmholtz instability and emission of a metal jet. The process is simulated using a a two-stages modeling methodology in which magnetic pressure at copper tube is calculated from an electromagnetic code and fed to a meshless hydrocode. Key features of the interfacial morphology and jet emission are reproduced. Jet is found to be dominated by titanium and a linear velocity profile observed.

Land Finance, Local Government Debt and Economic Green Transformation

As economic development continues to advance globally, countries are increasingly focused on the green transformation of their economies. This paper employs a data envelopment analysis (DEA) model and entropy weighting methodology to construct and assess an indicator system for economic green transformation, taking into account environmental pollution. The analysis is based on panel data from 215 prefecture-level cities in China between 2015 and 2019. The two-way fixed effects model and moderating effect model are employed to investigate the influence of land finance on economic green transformation and to ascertain the moderating effect mechanism of local government debt. The study’s conclusions are as follows: (1) Land finance impedes the transition to a green economy. (2) Local government debt is a major factor that restricts the influence of land finance on the transition to a green economy; as local government debt levels rise, land finance’s role in impeding the transition to a green economy rises, and a positive moderating effect occurs. (3) Depending on the urban geographic location, the size of the economy and other factors, the impact of local government debt and land finance on the economic green transition varies. This empirical study demonstrates that the more local government debt there is in an eastern region or city with a bigger economic output scale, the more of an inhibitory influence land finance has on the economic transition to a green economy. In light of this, the paper suggests that the land finance policy be updated at the appropriate time, that the land market be regulated, that the government’s reliance on land finance be gradually decreased, that the nature and amount of public debt be actively optimised, that the industrial infrastructure be enhanced to facilitate the transition towards a more environmentally sustainable economy, and that other suggestions be made.

Semantic clustering method using integration of advanced LDA algorithm and BERT algorithm

The subject of the study is an in-depth semantic data analysis based on the modification of the Latent Dirichlet Allocation (LDA) methodology and its integration with the bidirectional encoding representation of transformers (BERT). Relevance. Latent Dirichlet Allocation (LDA) is a fundamental topic modeling technique that is widely used in a variety of text analysis applications. Although its usefulness is widely recognized, traditional LDA models often face limitations, such as a rigid distribution of topics and inadequate representation of semantic nuances inherent in natural language. The purpose and main idea of the study is to improve the adequacy and accuracy of semantic analysis by improving the basic LDA mechanism that integrates adaptive Dirichlet priorities and exploits the deep semantic capabilities of BERT embeddings. Research methods: 1) selection of textual datasets; 2) data preprocessing steps; 3) improvement of the LDA algorithm; 4) integration with BERT Embeddings; 5) comparative analysis. Research objectives: 1) theoretical substantiation of LDA modification; 2) implementation of integration with BERT; 3) evaluation of the method efficiency; 4) comparative analysis; 5) development of an architectural solution. The results of the research are that, first of all, the theoretical foundations of both the standard and modified LDA models are outlined, and their extended formula is presented in detail. Through a series of experiments on text datasets characterized by different emotional states, we emphasize the key advantages of the proposed approach. Based on a comparative analysis of such indicators as intra- and inter-cluster distances and silhouette coefficient, we prove the increased coherence, interpretability, and adaptability of the modified LDA model. An architectural solution for implementing the method is proposed. Conclusions. The empirical results indicate a significant improvement in the detection of subtle complexities and thematic structures in textual data, which is a step in the evolutionary development of thematic modeling methodologies. In addition, the results of the research not only open up the possibility of applying LDA to more complex linguistic scenarios, but also outline ways to further improve them for unsupervised text analysis.

Parameterization of H2SO4 and organic contributions to volatile PM in aircraft plumes at ground idle

ABSTRACT Volatile Particulate Matter (vPM) emissions are challenging to measure and quantify, since they are not present in the condensed form at the engine exit plane and they evolve to first form in the aircraft plume and then continue to grow and change as they mix and dilute in the ambient atmosphere. To better understand the issues associated with the initial formation and growth of vPM, a modeling study has been undertaken to examine several key parameters that affect the formation and properties of the vPM that is created in the initial cooling and dilution of the aircraft exhaust. A modeling tool (Aerosol Dynamic Simulation Code, ADSC) that was developed and enhanced over a series of past research projects supported by NASA, DoD’s SERDP/ESTCP, and FAA was used to perform a parametric analysis of vPM. The parameters of fuel sulfur content (FSC), emitted condensable hydrocarbon (HC) concentrations, and the species profile of the HCs were used to construct a computational matrix that framed a wide range of expected parameter values. This computational matrix was executed for two representative commercial aircraft engines at ground idle and results were obtained for distances of 250 m and 1000 m downstream. From prior results, the most significant vPM emissions occur at the lowest power settings, so an engine power condition of 7% rated thrust was used. A primary goal of the parametric study is to develop an updated vPM modeling methodology and also to help interpret data collected in experimental campaigns. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. The aim is to provide additional understanding on how the vPM properties vary with fuel and engine parameters to increase the utility of vPM predictions. Implications: Volatile Particulate Matter (vPM) is an important contribution to the total PM emitted by aviation engines. While vPM is not currently a part of engine emissions certification regulations, vPM is used in aviation environmental impact assessments and for air quality modeling in and around airports. Current methods in use, such as FOA, were developed before many recent advances in experimental data acquisition and in understanding of vPM processes. The parameterization proposed here allows the vPM emission composition and particle numbers to be estimated in greater detail than current methods. These estimates can be used to develop inventories and provide a better estimate of total emission for most aviation engines. Its use in international regulatory tools can inform possible future regulatory actions regarding vPM.

Model‐Based Cybertronics Systems Engineering (MBCSE)

AbstractThe cybertronics challenges evolve around the complexity of systems and increase in detail and data involved. Existing methods and tools can master some of the rising challenges, but a method being able to handle the challenges was not introduced yet. MBSE is a potential approach to solve these problems, but the available methods were developed for modeling physical systems and software, not cybertronics. Because of the diversity of implementation domains participating in cybertronics systems, the modeling methodology must support the holistic management of requirements, properties, and design data. This paper introduces some promising emerging technologies like Arcadia, SysML V2, SysMD and PMM, that have the potential to interact with other methods and tools, closing the gaps in requirements tracing, system modeling, documentation, and verification. One potential methodology is introduced through a design example from requirements to implementation interface.

Computer modeling of temperature distribution in the course of sintering of glass microspheres

Composite materials obtained by sintering glass microspheres are considered the promising ones for the underwater technology. The high indicators of the hydrostatic strength combined with the heat and sound insulation properties are due to the microspheres maintaining their shapes as close to the spherical as possible. The computer model of the temperature distribution is developed based on the physical and chemical conceptions of the glass composites structure formation by sintering the glass microspheres of sodium borosilicate composition and processing the experimental results of the temperature measurements. The modeling methodology is based on the principles of decomposition of the task by the researcher and ranking the temperature and time parameters. The ratio of the hydrostatic strength to the density of the samples is taken as a functional criterion and the software application is written in the Python language. The experimental setup that is used for sintering the glass microspheres enables to study the temperature fields and thermal deformation processes in real-time conditions. The coefficients of thermal conductivity of sintering are determined by the regular mode method. The results obtained are aimed at solving one of the most important scientific and practical issues of developing technologies for manufacturing glass composites for strategic purpose.

Excuse me Sir/Madam, which Model?

AbstractDevelopment of complex systems through the application of Model‐Based Systems Engineering (MBSE) will require the creation and maintenance of multiple models, created using multiple languages. Hence, structuring the models such that there is efficient support for incremental development requires some foresight. Having models with multiple or unclear purposes may introduce situations where parts of the organisation will ask for modification for representing a desired future system state, whereas other parts of the organisation require it to remain unchanged in order to represent the present state. This paper introduces a structure with associated terms and characteristics of the different model types in order to provide guidance to an organisation. In particular, it extends previous work on model tenses, capturing different time perspectives/purposes on the models, to also include model usage for keeping track of the actual state of a realised system instance. For example, the concepts of Digital Twin and Digital Shadow are discussed and conceptualised. As such, the contributions made in the paper allows an organisation to reason about the completeness of their modelling methodology and its applicability to support incremental development. Above all, the paper spells out clearly that for a complex heterogeneous system, any request for an update to “the model” will prompt the response “Excuse me Sir/Madam, which model?”.

Methodology for phenoclimatic observations and modelling of Castanea sativa Mill. traditional cultivars and revitalizing chestnut growers’ local knowledge in France, ROC-CHA project

Methodology for phenoclimatic observations and modelling of <i>Castanea sativa</i> Mill. traditional cultivars and revitalizing chestnut growers’ local knowledge in France, ROC-CHA project

Modifications and Optimization of an Autologous Intracerebral Hemorrhage Rat Model

Objective: To date, no novel drug therapies have proven effective in improving outcomes after intracerebral hemorrhage (ICH). This has been slowed, in part, by a lack of reliable preclinical models that recapitulate the pathophysiological consequences of ICH. Limited descriptions of the methodology of preclinical models may play a role in this failed translation. The current study aimed to improve the autologous blood injection model of ICH and provide a comprehensive methodology to facilitate a reliable preclinical model that bridges the translational research gap. Methods: A modified single-injection protocol for an autologous-blood ICH model was developed and tested. This rodent ICH model produces injury in the basal ganglia of adult rats via a stereotaxically-assisted right-sided single-injection of fresh autologous whole blood. A comprehensive step-wise description of the methodology is presented and documented in a written and visual format with supporting pictorialmaterial, to prevent common mistakes that result in failed hematoma formation.Rates of successful hematoma formation using the single-injection protocol, outlined here, were compared to those using the traditional double-injection protocol.

Factors Influencing Residents’ Willingness to Pay for Mangrove Forest Environmental Services in Phu Long, Vietnam

The development of mangrove forest environmental services plays a crucial role in human health, economic development, and the security of countries worldwide. This study aims to establish and validate a model for assessing the factors influencing the willingness-to-pay for mangrove forest environmental services. The proposed model comprises four key factors: attitude, subjective norm, perceived behavior control, and environmental knowledge. Data were collected by surveying 235 individuals from various households in Phu Long commune, Vietnam. The Partial Least Squares—Structural Equation Modeling (PLS-SEM) methodology was employed for data analysis. The research findings reveal that perceived behavior control significantly impacted residents’ willingness-to-pay, followed by attitude, environmental knowledge, and subjective norms. Furthermore, certain demographic characteristics such as gender and family size mediate the relationship between independent and dependent variables. Based on the obtained results, several policy recommendations are put forward.

WITHDRAWN: The main threats facing the eel (Anguilla anguilla) and the sea lamprey (Petromyzon marinus) in Galicia (NW Spain) through Partial Least Squares Path Modelling

Migratory fish are very important species from an ecological and socioeconomic point of view, but they suffer the effects of many threats such as climate change, pollution, or overfishing, thus contributing to the decline of these species. To study the main factors influencing these species, Partial Least Squares Path Modelling (PLS-PM) methodology has been used to analyse and quantify the main threats facing two highly relevant migratory species: the eel (Anguilla anguilla) and the sea lamprey (Petromyzon marinus). Based on this statistical approach, two models have been developed for a total of 14 rivers located in the Autonomous Community of Galicia (NW Spain), one for the eel and the other for the lamprey. For the construction of the models, the influence of environmental factors, surface water quality and anthropogenic impacts on the population of these species has been studied. Two scenarios have also been simulated to assess how the application of corrective measures to reduce the anthropogenic impact implies important improvements to the eel and lamprey populations. The results of the models developed indicate that the variables analysed predict 69% of the eel "Population", with the weight of the measured variables (MV) 'Water treatment plants' having the most substantial weight (W=0.939) followed by the significant negative influence of 'Surface area of reservoirs and rivers' (W=-0.746). Similarly, in the lamprey model, an R2 of 0.58 has been obtained, where the negative influence of the MV "Agricultural nitrate discharge points" (-0.938) stands out substantially. In relation to the scenarios developed for both species, we highlight that the application of measures aimed at reducing anthropogenic pressure manages to mitigate the impact by 4.82% in the case of eel and by 1.37% in the case of lamprey. The set of models and scenarios proposed will make it possible to design preventive and corrective measures to mitigate the impacts affecting these populations, guaranteeing the integrated management of these species, and improving future decision-making, thus strengthening environmental governance.

Decision support for prioritizing critical societal services in optimal post-disaster critical lifeline recovery

AbstractCritical societal services, such as health care, education, law enforcement, and emergency response, are key to societal well-being and safety. Disruption to these services may arise from direct damage to the buildings from which the services are provided, or indirectly as a consequence of damage to supporting lifelines. The order in which lifeline elements are restored post-disaster affects not only the timing for restoring the lifeline services to its customers, but also the timing for restoring critical services that rely on these lifelines. In this paper, a mathematical formulation of the problem of prioritizing critical societal services in lifeline service restoration treated by multiple, specialized crews and exact algorithmic approach for its solution are proposed. Two approaches to accelerate the solution algorithm for use in realistic settings, where efficiency and scalability are essential, are presented. The developed techniques can be embedded in a decision support tool for real-time application with real-time information. To illustrate the proposed modeling and solution methodology and assess its efficiency for large, complex multi-lifeline applications, numerical experiments were run on a synthetic yet real-world network involving three key lifelines, including power, water, and transportation, as well as two hospitals that rely on them. The analysis of this case study shows that cross-lifeline collaboration in prioritization and scheduling in restoration action affects not only the return of services from the individual lifelines, but also of critical services on which, particularly in disaster settings, lives may depend.