Discrete Modeling Approach Research Articles

Integration of a choice modeling approach with immersive virtual environments for accurate space utilization prediction

Optimizing space utilization in buildings is necessary to achieve a sustainable built environment. To this end, accurate space utilization prediction, considering the spatial preference of occupants, is required to design well-utilized spaces in architecture, engineering, and construction projects. However, there is no existing method for quantitatively estimating occupants’ spatial preferences during space planning and design phases to optimize post-occupancy space utilization. Thus, in this study, an immersive choice modeling approach (ICMA) was developed to estimate the spatial preferences of occupants by integrating a discrete choice modeling approach with an immersive virtual environment simulation. The ICMA quantitatively estimates the spatial preferences of occupants in three steps: 1) discrete choice experiment design, 2) immersive virtual environment choice simulation creation, and 3) spatial preference modeling. The ICMA was successfully demonstrated by estimating the spatial preferences of 45 participants for an office. The application of an ICMA was then illustrated through a case example, highlighting its ability to predict space utilization in space planning and design phases.

A two-surface contact model for DEM and its application to model fatigue crack growth in cemented materials

This paper proposes a modelling approach that combines the discrete element method (DEM) and a novel bonded contact model to characterise the fatigue response of cemented materials. While DEM is commonly used to simulate bonded materials undergoing cracking, the centrepiece of the present method is the development of the novel bonded fatigue model. This new model couples damage mechanics and bounding surface plasticity theory to capture fatigue crack growth in cement bridges between aggregates. Thanks to the incorporation of the bounding surface plasticity, the proposed model provides a smooth transition from static to fatigue damages and vice-versa in a unified manner, making it more flexible to capture damage responses of cemented materials under different loading conditions (i.e. monotonic and cyclic loadings). Moreover, the proposed approach automatically captures the hysteretic response in cement bridges between aggregates under fatigue loadings without ad-hoc treatments. More importantly, by removing the direct dependence of the fatigue damage variable on the number of loading cycles, the modelling approach can be applied to simulate the fatigue behaviour of cemented materials under cyclic variable load amplitudes. The proposed modelling approach is evaluated against several strength tests to examine its predictive capability. Satisfactory agreements with fatigue experiments are achieved for flexural modulus degradations, lifetimes and sensitivity of stress levels under constant and variable amplitude cycles. This result suggests that the proposed discrete modelling approach can be used to conduct numerical experiments for insights into the fatigue behaviour of cemented materials.

A textile architecture-based discrete modeling approach to simulating fabric draping processes

Fabric draping, which is referred to as the process of forming of textile reinforcements over a 3D mold, is a critical stage in composites manufacturing since it determines the fiber orientation that affects subsequent infusion and curing processes and the resulting structural performance. The goal of this study is to predict the fabric deformation during the draping process and develop in-depth understanding of fabric deformation through an architecture-based discrete Finite Element Analysis (FEA). A new, efficient discrete fabric modeling approach is proposed by representing textile architecture using virtual fiber tows modeled as Timoshenko beams and connected by the springs and dashpots at the intersections of the interlaced tows. Both picture frame and cantilever beam bending tests were carried out to characterize input model parameters. The predictive capability of the proposed modeling approach is demonstrated by predicting the deformation and shear angles of a fabric subject to hemisphere draping. Key deformation modes, including bending and shearing, are successfully captured using the proposed model. The development of the virtual fiber tow model provides an efficient method to illustrate individual tow deformation during draping while achieving computational efficiency in large-scale fabric draping simulations. Discrete fabric architecture and the inter-tow interactions are considered in the proposed model, promoting a deep understanding of fiber tow deformation modes and their contribution to the overall fabric deformation responses.

An investigation of consumers' preference and willingness to pay for fish welfare in Denmark: A discrete choice modeling approach

Farmed fish welfare has become an important factor in consumers' purchasing decisions. The objective of this study is twofold: (1) to investigate consumers' preference and willingness to pay for farmed fish welfare and (2) to measure the effect of social desirability on consumers' responses. A self-administered online survey was developed and distributed to an Internet panel in Denmark. Discrete choice experiment was used to elicit consumers' preference and willingness to pay for fish welfare. Direct and indirect questioning were used when an individual was asked to make a decision in the discrete choice experiment. Respondents were asked to make their own purchase decision in direct questioning (i.e., the fish product s/he would like to buy), while in indirect questioning, they were asked to make decisions for others (i.e., the most sold product in the market). The results revealed that attributes such as the form of fish, labeling, price, and premium had an influence on consumers' choice of fish. Specifically, consumers were generally willing to pay more in premium for fish that had a “fish welfare guaranteed” label. Consumer heterogeneity was disclosed in consumer preference and willingness to pay. Results also showed that consumers believe other consumers would be likely to pay less for fish welfare than themselves. Thus, the results revealed that social desirability could create bias when valuing fish welfare.

The preferences of transport mode of certain travelers in the age of autonomous vehicle

Individuals choose transport modes to travel based on their preferences and journey characteristics. The availability of autonomous vehicles (AVs) on the market will affect the traditional mode choice models. In this regard, this research studies three transport modes: conventional car, privately-owned autonomous vehicle (PAV), and shared autonomous vehicle (SAV). The potential changes in transport mode choice are evaluated, where the impact of changes of some sociodemographic and travel variables on choices are estimated. A discrete choice modeling approach is applied in this study to develop a transport choice model. A stated preference (SP) approach is used, where 306 answers are collected in Hungary. A discrete choice experiment (DCE) is designed to collect the choices of people. Multinomial Logit (MNL) model is applied to the data to develop a transport mode choice model, where differentiations between some parameters across transport modes are presented. People are varied in their willingness to use a transport mode across groups, such as the income, the family size, and the current transport mode. The results demonstrate that people are more likely to use a conventional car than PAV, while they are more likely to use PAV than SAV. The output of this study can be used to support policy implications in the AV era.

Pore-scale morphology effects on colloid deposition by trajectory tracking simulations

The migration and deposition of colloids in natural porous media is a phenomenon of high importance in hydrocarbon recovery, wastewater treatment and contaminant hydrogeology. Commonly described by colloid filtration theory (CFT), and using a single representative collector element, the theoretical framework is not accounting for morphological effects and heterogeneity of realistic porous media, including the local variations of balance of forces or presence of hydraulically stagnant regions in the vicinity of grain-to-grain contacts. So far developed numerical models of colloidal transport in porous media accounting for all major interactions and pore structure were applied to 2D systems. Known 3D simulations are limited to particulate flow with a reduced set of interactions.We propose a coupled colloid transport model combining computational fluid dynamics (CFD) and discrete element model (DEM) approaches, where the former provides the velocity field at a given time step, while the second calculates the updated colloid positions accounting for a set of forces acting on each colloid. Relationships between colloid retention and morphological characteristics, such as the pore coordination number and surface to volume ratio of a random sphere pack are investigated by pore partitioning of a digitized representation. The role of stagnation zones is quantified by expressing the capture probability of colloids as a function of distance to the nearest grain-to-grain contact measured along the curved surface.The analysis of pore space morphology of the collector and the simulated filtration process reveals a strong relation between colloid retention and pore connectivity. The simulations successfully mimic the expected effects of ionic strength of the carrier fluid, such as the increasing role of grain-to-grain contacts in colloid retention at low ionic strength.

Effect of transient breathing cycle on the deposition of micro and nanoparticles on respiratory walls

Background and objectiveFrom various perspectives (e.g. inhalation exposure and drug delivery), it is important to provide insights into the behavior of inhaled particles in the human respiratory system. Although most of the experimental and numerical studies have relied on an assumption of steady inhalation, the transient breathing profile is a key factor in particle deposition in the respiratory tract. In this study, particle transportation and deposition were predicted in a realistic human airway model during a breathing cycle and the effects of steady-state and transient flows on the deposition fraction were observed using computational fluid dynamics. MethodsTwo transient breathing cycles with different respiratory durations were considered to evaluate the effects of respiration duration on particle transport and deposition characteristics. Two types of steady breathing conditions with corresponding steady-state respiratory volumes were reproduced. The Lagrangian discrete phase model approach was used to investigate particle transportation and deposition under transient breathing conditions. Additionally, the Eulerian approach was used to analyze the transport of nanoparticles in the gas phase. A total of >50,000 monodispersed particles with aerodynamic diameters ranging between 2 nm and 10 μm were selected for comprehensive deposition predictions for particle sizes ranging from the nano- to microscale. ResultsThe predicted results were compared with the experimental data. The particle deposition fraction in the nasal cavity and tracheal regions showed differences between the steady and transient simulations. In addition, particle analysis under steady inhalation conditions cannot accurately predict particle transportation and deposition in the lower airway. Furthermore, the breathing cycle had a significant effect on the deposition fraction of the particles and the behavior of the inhaled particles. ConclusionsTransient simulation mimicking the breathing cycle was observed to be an important factor in accurately predicting the transportation and deposition of particles in the respiratory tract.

Forecasting temperature of the Saudi Arabian Province of Makkah using a discrete state–space modeling approach

The maximum and minimum air temperature components (Tmax and Tmin) play a crucial role in science. This study proposes a discrete-time identified state-space modeling approach in which the temperature fluctuation was modelled as a state-space system with the temperature time series as inputs. We aim to provide a tool for projecting future scenarios of Tmax and Tmin. The current research employs a prediction-focused methodology to system identification, with the overarching goal of developing a realistic and dynamic system model. Data on the Tmin and Tmax recorded in the Saudi Arabian province of Makkah are used to test the accuracy and robustness of the proposed methodology. The proposed model was developed utilizing 120 years' (1901–2020) worth of historical monthly time series data on Tmax and Tmin. It was applied to anticipate future temperatures over the ensuing 60 years (up to 2080). For maximum temperature projections, the fit to the data or prediction focus was 87.04% and 85.14%, respectively for the identification (training) and validation phases of the model development. Akaike’s Final Prediction Error (FPE) and Mean Squared Error (MSE) values were observed to be 0.37 °C and 0.34 °C, respectively. The prediction focus during the identification and validation phases were 86.25% and 84.78%, respectively for the minimum temperature projection. The FPE and MSE values were 0.41 °C and 0.37 °C, respectively for this instance. The findings demonstrate that the recommended discrete state-space modeling approach may be utilized to predict temperature variations in the future.

Discrete and Continuum Modeling of Coulomb Friction Damping in Stone Blocks

Friction plays an important role in creating damping mechanism in structures.In this paper a study was conducted to analyze coulomb friction damping andfrictional energy dissipation mechanism with stone blocks in a single degreeof freedom system by discrete and continuum modeling in finite element-based software ABAQUS. Discrete and continuum modeling approach wasused in this study. It was found that both approaches gave similar results.

Well interference evaluation considering complex fracture networks through pressure and rate transient analysis in unconventional reservoirs

Severe well interference through complex fracture networks (CFNs) can be observed among multi-well pads in low permeability reservoirs. The well interference analysis between multi-fractured horizontal wells (MFHWs) is vitally important for reservoir effective development. Well interference has been historically investigated by pressure transient analysis, while it has shown that rate transient analysis has great potential in well interference diagnosis. However, the impact of complex fracture networks (CFNs) on rate transient behavior of parent well and child well in unconventional reservoirs is still not clear. To further investigate, this paper develops an integrated approach combining pressure and rate transient analysis for well interference diagnosis considering CFNs. To perform multi-well simulation considering CFNs, non-intrusive embedded discrete fracture model approach was applied for coupling fracture with reservoir models. The impact of CFN including natural fractures and frac-hits on pressure and rate transient behavior in multi-well system was investigated. On a log–log plot, interference flow and compound linear flow are two new flow regimes caused by nearby producers. When both NFs and frac-hits are present in the reservoir, frac-hits have a greater impact on well #1 which contains frac-hits, and NFs have greater impact on well #3 which does not have frac-hits. For all well producing circumstances, it might be challenging to see divergence during pseudosteady state flow brought on by frac-hits on the log–log plot. Besides, when NFs occur, reservoir depletion becomes noticeable in comparison to frac-hits in pressure distribution. Application of this integrated approach demonstrates that it works well to characterize the well interference among different multi-fractured horizontal wells in a well pad. Better reservoir evaluation can be acquired based on the new features observed in the novel model, demonstrating the practicability of the proposed approach. The findings of this study can help for better evaluating well interference degree in multi-well systems combing PTA and RTA, which can reduce the uncertainty and improve the accuracy of the well interference analysis based on both field pressure and rate data.

Consumer Choice Decision for Airport Taxi at I Gusti Ngurah Rai International Airport

A taxi service provided by the I Gusti Ngurah Rai International Airport plays an important role in meeting the transportation needs of travelers, as it provides a convenient and effective means for people to go from the airport to their destination. However, in today’s competitive industry, airport taxi service may find it difficult to attract and retain a growing number of passengers. This research aims to identify strategies that can be used by airport taxi service, where this research will focus on identifying significant attributes that influence passengers’ decisions to use airport taxi services and determine the most effective strategies in an effort to increase the number of passengers. The research analysis was conducted using a discrete choice model approach to determine individual behavior. Multinomial logistic regression (MNL) model was used for the three alternative mode choices observed between Taxi, Minivan, and Grab. The model uses influential attributes such as waiting time, travel time, price, safety, convenience, capacity, and promotion. The research data was obtained quantitatively with data collection techniques using questionnaires with respondents spread across major cities in Indonesia. Based on the results of the analysis, it shows that the most significant attributes affecting taxi selection are safety, price, convenience, travel time, and capacity.

Intra-household gender differences in perception of climate change and eventual impacts: Empirical evidence from groundwater depleted zones of Bangladesh

Given the importance of gender in household and agricultural decision-making including climate change adaptation and mitigation decisions, understanding “within” household gender differences in perception of climate change and eventual impacts is crucial to formulate and design relevant policies and programs. However, studies on climate change, particularly perception studies, overlooked gender aspects. Therefore, aiming at a more nuanced gender analysis, this study attempted to measure “within” household gender differences in perception of climate change, its impact and explored the factors affecting such perceptions. We drew on empirical data obtained from farm household surveys conducted on 360 co-dwelling couples from three dissimilar drought-prone areas of northwest Bangladesh. Findings reveal that the significant difference in spouses' perceptions of unexpected rainfall and thunderstorms was perceived more by wives than husbands, nevertheless, increasing temperature was perceived more by husbands than wives. Similarly, spouses perceived that climate change has negative effects on agriculture and livelihoods. There were significant spatial differences in the responses across husbands and wives. The discrete choice modelling approaches employed to explore the determinants of perceptions by husbands and wives illustrated that wives inhabited in high water scarce areas (HSA) and medium water scarce areas (MSA), with higher education and age were more likely to perceive climate change and eventual impacts, whereas husbands' age and education were negatively allied with their perceptions. Intra-household gender differentiated perceptions suggest that policy interventions should provide gender disaggregated targeted human capital development and extend institutional facilities and support appropriate farm-level spatial program design and policy formulation.

A Simulation Modelling Study of Referral Distribution Policies in a Centralized Intake System for Surgical Consultation.

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

Influence of Type of Sleeper–Ballast Interface on the Shear Behaviour of Railway Ballast: An Experimental and Numerical Study

The shear resistance at the sleeper–ballast interface of a ballasted track is an important contributor in maintaining track stability under faster and heavier axle loads where the ballast undergoes significant lateral sliding. Different types of sleeper–ballast interfaces based on the type of sleeper arrangements, such as concrete sleepers, timber sleepers, and under sleeper pads (USPs) attached to the concrete sleepers influence the lateral stability of railway tracks. Therefore, in this study the shear and degradation behaviour of ballast at concrete–ballast, timber–ballast, and USP–ballast interfaces were examined in the laboratory using large-scale direct shear tests under 60 kPa normal stress. The use of waste materials in the construction of civil infrastructure is gaining a lot of interest in the engineering community. Therefore, in addition to commercial USPs manufactured using raw materials, recycled USPs manufactured from granulates of end-of-life rubber tyres were also tested in this study. The discrete element modelling (DEM) approach was used to predict the shear behaviour of ballast at 30, 90, 120, 150, and 180 kPa normal stresses. The bonded particle model (BPM) was adopted in the DEM to simulate the effects of particle breakage during shearing. The results exhibited that both commercial and recycled USPs significantly improve the shear resistance at the sleeper–ballast interface while reducing particle degradation compared to concrete and timber sleeper interfaces.

Evaluating the long-term operational performance of a large-scale inland terminal: A discrete event simulation-based modeling approach.

Inland terminals, or dry ports, have played an important role in multimodal transportation networks as transportation hubs that provide connections between seaports and hinterland economies. While important, evaluating the operational performance of a dry port is especially challenging since it depends not only on internal factors, such as the variety and number of container handling equipment (CHE) deployed, but also on other external factors, including changes in transportation policies and container demands experienced by a dry port. To properly evaluate the holistic performance of a dry port while considering all the aforementioned factors, a discrete event simulation (DES) framework is herein developed and applied to the Ladkrabang Inland Container Depot (LICD)-one of the largest dry ports in Southeast Asia-under various operational settings. Despite complicated internal operations, the devised DES framework has shown itself useful in the analyses of LICD, due largely to its flexibility that allows users to include sophisticated operational rules into models. According to our computational results, the current LICD operation is markedly ineffective as the usage rates of all CHE types are relatively low and varied across gate operators-especially the yard truck whose values range between 2.46% and 11.15% on yearly average. We also find that, by redesigning the LICD and its internal operations, the LICD's performance could be substantially enhanced-even with fewer numbers of CHE. Regarding the four CHE types, the reach stacker seems to limit LICD's capability, as its utilization tends to first reach the maximum allowable rate of 75%, while the rubber tyred gantry crane could help boost the usage rate of yard trucks, which, in turn, results in reduced container dwelling times. Nonetheless, the modified LICD could accommodate up to 140% of the current container demand before it experiences operational difficulties induced by the saturation of container flow from rail transportation.

Control-oriented hybrid model of a small-scale refrigerated truck chamber

Refrigerated last-mile transport of goods has faced rapidly growing importance in recent years. Although hardware components underwent comprehensive improvements to fulfill increased efficiency demands, little was done regarding control strategies. Simple algorithms or restricted consideration of system parts for control design harshly limit attainable economic and ecological flexibilities provided by state-of-the-art hardware. Sophisticated model-based control schemes constitute a promising remedy but lack detailed and computationally reasonable models of the overall cooling application. This article introduces an appropriate dynamic, low-order model to overcome this issue. The mathematical description relies on first principles, features lumped parameters, and comprises temperature evolution and electrical power consumption, allowing for the assessment of system efficiency. Besides the explicit consideration of door openings, incorporating thermal capacities of the insulation walls and the cooling unit’s secondary storage loop stands out. Component’s switching behavior is embedded in the chosen discrete hybrid automaton modeling approach, for which 18.4h of real-world measurement data drive parameterization and validation. An overall fit to validation data of 86.2% and 88.7% for the most significant temperature quantity and total power consumption illustrates the model’s high performance and practicality for control applications, soft-sensors, and fault detection.

Simulation of the Calendering Process of NMC‐622 Cathodes for Lithium‐Ion Batteries

Calendering is the final step in electrode production during the manufacturing of lithium‐ion batteries. It is a crucial process that significantly influences the electrodes’ mechanical and electrochemical properties and is decisive in defining their volumetric energy density and performance. Herein, a discrete element method modeling approach is proposed to predict the process parameters of the calendering process. In particular, the roll gap width is required for a given target porosity. For this purpose, a particle bed of 9 mm in length is compacted using a roll section enabling a deeper look into the compaction behavior of the microstructure. Six NMC‐622 electrodes with different thicknesses are produced and compacted to different porosities. In the experimental investigation, the roll gap width is set and measured allowing a simulative replica of the process. With the process simulation, the force propagation within the electrode can be observed on a particle level. Furthermore, nanoindentation measurements with NMC‐622 cathodes provide information on the densification behavior of cathodes and support the parameterization of the particle bed. The particle‐based simulation of the compaction process is experimentally validated using nanoindentation measurements on NMC‐622 cathodes.

Noise-induced switching in an oscillator with pulse delayed feedback: A discrete stochastic modeling approach.

We study the dynamics of an oscillatory system with pulse delayed feedback and noise of two types: (i) phase noise acting on the oscillator and (ii) stochastic fluctuations of the feedback delay. Using an event-based approach, we reduce the system dynamics to a stochastic discrete map. For weak noise, we find that the oscillator fluctuates around a deterministic state, and we derive an autoregressive model describing the system dynamics. For stronger noise, the oscillator demonstrates noise-induced switching between various deterministic states; our theory provides a good estimate of the switching statistics in the linear limit. We show that the robustness of the system toward this switching is strikingly different depending on the type of noise. We compare the analytical results for linear coupling to numerical simulations of nonlinear coupling and find that the linear model also provides a qualitative explanation for the differences in robustness to both types of noise. Moreover, phase noise drives the system toward higher frequencies, while stochastic delays do not, and we relate this effect to our theoretical results.

Accounting for homeowners’ decisions to insulate: A discrete choice model approach in Spain

This paper assesses Spanish households’ willingness to thermally insulate their homes and the drivers that influence such a decision-making process. Stated preference data were collected through a discrete choice experiment (DCE). The final sample of 191 respondents and 1,145 observations was analysed by the use of a mixed logit model, weighing the factors that encourage homeowners to carry out façade energy renovations or not. The model enables the quantitative estimation of renovation adoption rates depending on the households’ characteristics and public support instruments in place. The results show that homeowners are extremely interested in increasing the thermal insulation of their homes. The actual investment cost required in the existing building stock is lower than the obtained willingness-to-pay. Furthermore, it was found a relevant effect of a variety of household features on renovation choice (income, age, heating system, etc.), which should be contemplated in the energy efficiency policy design. Additionally, a case analysis is performed which comprises 3 household categories. The results reveal that the required subsidy level is different in each case, sometimes even unnecessary, although all of them lower than the grants set by existing aid programs. Thus, to reduce the free-riding effect, a closer perspective would enable targeted support mechanisms towards each household category. Moreover, the policy performance can be improved by combining subsidies with other measures such as low-interest loans or increased tax rebates, which could contribute to improving the cost effectiveness of the public expense associated with direct grants. Overall, an increased tax rebate is preferred to soft financing, although the influence of the latter increases in low-income households.

How people get to work at night. A discrete choice model approach towards the influence of nighttime on the choice of transport mode for commuting to work

Nighttime mobility differs in many respects from typical mobility during the daytime. Poorer lighting conditions, the reduced availability of public transport, and an increased feeling of insecurity are just some of the factors potentially influencing and complicating the planning and organizing of nighttime trips. Importantly, the act of commuting to and from work during the night tends to be more significantly impacted by the above factors. In contrast to leisure or errand trips at night, commutes to work are often associated with fixed working hours and can therefore be arranged less freely and flexibly. The aim of the current study is to determine to what extent and under what conditions nighttime influences the choice of transport mode for commuting to work. For this purpose, several multinomial logit models including interaction terms were constructed based on a representative dataset from the nationwide survey, Mobility in Germany 2017. The resultant analysis demonstrated that nighttime significantly increased the likelihood of choosing the car instead of another mode of transport. In addition, the results of the interaction terms demonstrated that this nighttime effect is significantly related to gender, the degree of urbanity of the commuter's place of residence, and the weather. Study results thus confirm that nighttime is a significant factor influencing the choice of means of transport for commuting to work. The insights gained in this paper may help in both optimizing future transportation choice models and in improving the overall work of transportation and urban planners.