Simulation-based Methodology Research Articles

Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study.

Recovery strategies for end-of-life (EoL) and end-of-use (EoU) products, such as reuse, remanufacturing, and recycling, present opportunities to enhance profitability and lower emissions while fostering new business prospects. However, significant uncertainties and stochasticity affect the quantity and quality of returned waste tires, costs associated with used tires and the retreading process, and the selling prices driven by second-hand market dynamics. This paper investigates the economic and environmental performance of a closed-loop supply chain (CLSC) for waste tire retreading and recycling considering the impact of these uncertainties using a Monte Carlo simulation-based methodology. The proposed methodology is implemented on an industrial case study in the United Arab Emirates (UAE) for quantifying the impact of uncertainties involved in the CLSC for tire retreading and recycling large tires of the 12R24 type, which are extensively used by buses and trucks in the UAE. The findings reveal that for an average of 13,200 tires reaching their EoL, the processes of retreading and recycling yield an average profit of $ 487 K. Furthermore, these initiatives are expected to result in the reduction of an average of 1457 tons of CO2 emissions. A scenario analysis is conducted to evaluate the impact of improvements in key operational parameters on the CLSC performance. Consequently, all stakeholders engaged in tire retreading would benefit by reducing costs, generating higher profits, and reducing emissions while creating new business opportunities.

A dynamic simulation-based methodology for systematic assessment of workability on floating wind turbines

Abstract Floating offshore wind technology experiences significant motion responses when exposed to environmental wave and wind loads, possibly interfering with technicians conducting maintenance work. Industrial interest is rising in the assessment of workability, as impairments will decrease the availability of the asset and possibly affect the business case for the wind farm project. Quantification of impairments are formed from three workability indicators: Nordforsk Seakeeping Criteria, ISO 2631-1, and ISO 6897. The present work shows a likely workability decrease, quantified to 2.4% for the Nordforsk Seakeeping Criteria, for the UMaine VolturnUS-S reference platform and the IEA 15MW reference wind turbine. Peak wave period and wave heading direction are found to affect the results and indicate the importance of conducting the study in site-representative conditions. In addition, varying results for different indicators and methodological approaches indicate the need for common rules and standards in the floating wind industry to enable transparency during project development.

Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation

This study proposes a simulation-based methodology for estimating the energy saving achievable through the implementation of a just-in-time morning ramp-up procedure in a warehouse (equipped with a heat pump). In this methodology, the operation of the heating supply unit each day is initiated at a different time, aiming at achieving the desired setpoint upon (and not before) the expected arrival of the occupants. It requires the estimation of the ramp-up duration (the time it takes the heating system to bring the indoor temperature to the desired setpoint), which can be provided by machine learning-based models. To justify the corresponding required deployment investment, an accurate estimation of the resulting achievable energy saving is needed. Accordingly, physics-based energy behavior simulations are first performed. Next, various ML algorithms are employed to estimate the ramp-up duration using the simulated time-series data of indoor temperature, setpoints, and weather conditions. It is shown that the proposed pipelines can estimate the ramp-up duration with a mean absolute error of about 3 min in all indoor spaces. To assess the resulting potential energy saving, a re-simulation is conducted using ML-based ramp-up estimations for each day, resulting in an energy savings of approximately 10%.

Efficient disposal of low lunar orbiters on the lunar surface

Realistic numerical simulations are conducted to explore safe and efficient strategies for End-of-Life lunar spacecraft disposal on the lunar surface. Disposal from three different near-polar, low-altitude, and near-circular orbit types was analyzed to determine if impact locations that minimize the risk to protected regions of the lunar surface can be achieved consistently for low fuel costs using the Moon’s non-spherical gravity field. A total of 300,000 objects were propagated following retrograde disposal burns using a high-fidelity lunar trajectory model, and the disposal burn locations and magnitudes were compared against the resulting likelihoods of decay and lunar surface impact locations. The results identified disposal strategies that could achieve impact in safe locations for 10 m/s of ΔV or less for all orbit types, much lower than the ΔV cost to lower the perilune entirely to the lunar surface. A similar simulation-based methodology could be applied to operational lunar satellites to identify disposal strategies based on specific mission conditions. The results of this study support the development of safe and efficient End-of-Life disposal strategies that minimize the accumulation of debris in lunar orbit and extend the operational lifetimes of lunar missions.

Simulation-based Methodology to Investigate the Impact of Material Type and Compressive Speed Variation on Effective Strain Rate and Springback

To obtain the desired results in the manufacturing process, especially the bending process, the occurrence of springback must be reduced. The effective strain rate must be increased to predict the increase in material. This study uses a simulation method to determine the influence of material type and compressive speed variation on spring back and effective strain rate. This study uses 3 kinds of materials: JIS SPHD, JIS A1100 BE, and JIS SN400A, and speed variations, namely: 90 mm/s, 105 mm/s, and 120 mm/s. This study shows that JIS SN400A material has the smallest springback value compared to JIS A1100 BE and JIS SPHD materials because the nitrogen content in JIS SN400A makes it more plastic. At a compressive speed of 105 mm/s, springbacks tend to decrease in JIS SN400A, JIS SPHD, and JIS A1100 BE materials caused by residual stress. The average effective strain in JIS SN400A material increases in line with the increase in compressive speed, because JIS SN400A material has the highest melting temperature compared to JIS SPHD and JIS A1100 BE materials to reduce the risk of residual stress, the nitrogen content in JIS SN400A material also plays a role in increasing the effective strain value.

Reconciling retrofitting with IEQ to maintain energy, environmental and economic performance: A methodological approach for generic office buildings

Office building retrofitting may instigate significant reductions in energy consumption and there is growing recognition that buildings’ energy, economic and environmental performances (the 3Es) must be considered in tandem. This study aims at maintaining the 3Es when retrofitting office buildings towards nZEB standards. A simulation-based methodology was designed consisting of three generic building forms describing common office building geometries constructed between 1952 and 1980 in Egypt; linear, square with courtyard and L-shaped. Each accommodated for open, closed and semi-closed office layouts as recommended in architectural manuals, and was oriented in four directions, giving 36 base cases. A combined retrofitting strategy was applied and both base cases and retrofitted cases were simulated in DesignBuilder using Cairo weather data, which was validated indirectly through real measurements using preliminary simulations that retrieved statistical significance.Results were analyzed for each of the 3Es by comparing base cases to post-retrofitting performances. Results revealed that not all retrofitted cases that demonstrate acceptable environmental performance and/or energy-efficiency are cost-optimal. However, the following patterns were deduced. Square courtyard buildings yield superior performances, and L-shapes tend to perform outside acceptable ranges. Closed plans demonstrate superior energy and environmental performance but poorer economic performance. 17 out of 36 retrofitted cases achieved nZEB, illustrating the potential of implementing nZEB in Egypt. This work may be considered prototypical, encouraging a national office building retrofitting process toward nZEB. However, implications extend beyond the national level, as results may be used to support a kick-off retrofitting programme based on basic office building geometries.

Impact of delivery variations on 3D dose distributions for volumetric modulated arc therapy plans of various complexity.

Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

Quantifying the decarbonization potential of mobile heat battery in low-temperature district heating

This research assesses the potential of a mobile thermochemical storage system, the mobile heat battery (M-HB), for decarbonizing a low-temperature district heating (DH) system in the Netherlands. The assessment is built on a case study where the M-HB is used to transport waste heat from different sources to a neighborhood interface of a DH system. This case study utilizes a simulation-based methodology to calculate the emissions from grid electricity, DH, and M-HB transport and charging. Building performance simulation is used as the main experimental method in combination with both empirical data and theoretical assumptions. Various system operational strategies and uncertain factors are explored, and waste heat sources are screened by different decarbonization targets. Findings indicate that using the M-HB can reduce the operational carbon emissions by up to 80 %, from approximately 60–70 KgCO2/GJ of the system without M-HB to around 13 KgCO2/GJ in optimal scenarios. Emissions from M-HB transport and charging are identified as more influential to the decarbonization potential than other considered factors, which addresses the significance of choosing proper waste heat sources. Despite some limitations from data availability and assumptions, this work identifies both opportunities and challenges for using M-HB to decarbonize DH systems.

Robust two-degree-of-freedom control: A simulation-based approach for adults population with T1D

Diabetes Mellitus (DM) is emerging as a global concern, affecting people from early ages to adulthood and positioning itself as one of the leading causes of mortality worldwide. The post-COVID-19 era has been characterized by notable progress in the domains of medicine, science, and engineering, creating a favorable atmosphere for the investigation of innovative solutions. The present manuscript aims to investigate the problem of glucose regulation in adults diagnosed with Type 1 Diabetes mellitus (T1D) using a two-degree-of-freedom robust controller technique. A physiological mathematical model involving individuals with T1D serves as the foundation for the proposed closed-loop technique. The control algorithm approach successfully maintains safe glycemic levels in a virtual population of 10 adults using a sophisticated, well-known physiological T1D simulator. We evaluated this with continuous 24-hour simulations. Simulation results support the practicality of the presented approach, and we further validate it using a statistical analysis that incorporates a probability density function and control variability grid analysis (CVGA). Despite being a theoretical and simulation-based methodology, the results of this study demonstrate promise, offering promising opportunities for developments in the field of artificial pancreas technology using Mexican technology.

Fully Bayesian forecasts with evidence networks

Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment’s ability to distinguish between competing models. We thus propose a novel simulation-based methodology capable of providing expedient and rigorous Bayesian model comparison forecasts without relying on restrictive assumptions. Published by the American Physical Society 2024

Flow Simulation-Based Methodology for Reducing The Risk of Fuel Fire In An Aircraft’s Fuel System Enclosure

ABSTRACT This study applies advanced computational fluid dynamics (CFD) simulations to enhance the safety of an aircraft’s fuel system by striving to minimize the risk of on-board fire hazards. Employing a detailed flow simulation model, this research assesses the efficacy of newly designed measures within a fuel system, designed for a modified radial engine on a test-bed AN 2 airplane. The model simulates internal airflow, droplet particle flow, the formation of Eulerian Wall Film, and the vaporization process from the fuel film within the enclosure of the aircraft’s equipment bay. By exploring both the original and modified geometries of this fuel system enclosure, the simulations provide insights into the flowpaths of leaking fuel, the spatial and temporal distribution of fuel vapor concentrations, and the overall effectiveness of design modifications aimed at rapid removal of hazardous substances. Structural improvements, including the addition of strategic ventilation inlets and outlets, are proposed based on the simulation results to ensure rapid dispersion of vapors and minimal residual fuel, effectively reducing the potential for vapor ignition. This study thus underscores the potential of precise CFD modelling in identifying risks and developing robust fire hazard mitigation strategies in aviation fuel systems.

Impacts of Photovoltaic Façades on the Urban Thermal Microclimate and Outdoor Thermal Comfort: Simulation-Based Analysis

Cities face the consequences of climate change, specifically the urban heat island (UHI) effect, which detrimentally affects human health. In this regard, deploying PV modules in urban locales prompts inquiry into the impact of energy-active building components on the adjacent thermal microclimate and human thermal comfort. A twofold simulation-based methodology addresses this subject: First, the implications of façade-integrated photovoltaics on the urban thermal microclimate are investigated using a case study in Munich, Germany. Secondly, a parameter study allows us to gain further insights into the relevance of several parameters on the microthermal impact. The simulation results show a daytime heating effect of photovoltaics on the mean radiant temperature of up to +5.47 K in summer and +6.72 K in winter. The increased mean radiant temperature leads to an elevation of the Universal Thermal Climate Index of up to +1.46 K in summer and +2.21 K in winter. During night-time, no increase in both metrics is identified—hence, nocturnal recovery as a key element for human health is not affected. Despite extended human exposure to thermal heat stress in summer, PV façades improve the annual outdoor thermal comfort autonomy by 0.91% due to lower cold stress in winter. The higher PV efficiencies and lower albedo of the reference building surface lower the heating effect. However, with the current efficiencies, PV façades consistently lead to heating of the surrounding thermal microclimate in summer and lower the outdoor thermal comfort.

How does technological innovation impact the service time and the attraction of new customers in the financial sector? Evidence from an emerging economy

Due to safety perceptions, Colombian banking clients prefer to visit bank branch offices instead of other channels. Thus, there are long waiting for lines at branch offices. Considering the need for more tools for strengthening and streamlining client service, the number of financial clients tends to diminish. In this context, this paper aims to measure the impact of technological innovation on the clients’ waiting time and the attraction of new customers. We propose a simulation-based methodology to analyze customer behaviors and forecast the diffusion effect on mobile app adoption. Furthermore, our study provides managerial insights and future research lines.

A Combined Analytical and Simulation-Based Methodology for Quantifying the Noise-Power-Area Trade-Offs in Biomedical Amplifiers

A Combined Analytical and Simulation-Based Methodology for Quantifying the Noise-Power-Area Trade-Offs in Biomedical Amplifiers

Assessing flexibility in networked multi-energy systems: A modelling and simulation-based approach

In response to the uncertainty and volatility arising from renewable sources, there is a growing need for enhanced flexibility within the energy system to maintain a continuous balance between power generation and demand. In this context, interest is growing around the so-called Multi-Energy Systems (MES) where different energy vectors coexist and optimally interact through conversion technologies and energy networks, creating additional flexibility opportunities. Nevertheless, there exists a gap in research regarding the impact of the network on flexibility availability. Typically, these complex systems are treated as power nodes or energy hubs without comprehensive network considerations. For this reason, the paper aims to propose a methodology and a tool to evaluate flexibility in a Multi-Energy System, considering not only the individual devices in place and the users' demands but also their interactions with the physical energy network. In detail, a simulation-based methodology is developed and described, and finally tested on a Case Study. As a result, both the physical and operational flexibilities (UP-flex and DOWN-flex) of the system regarding the electrical vector were obtained analytically and graphically. Particular attention was given to the evolution of key temperatures within the district heating network and the thermal power produced by the central unit in various flexibility scenarios. The outcomes demonstrate the utility of this tool for defining flexibility boundaries and profiles, as well as for assessing whether the flexibility demanded by grid operators aligns with the physical constraints of the network.

Electric Vehicle Drivetrain Efficiency and the Multi-Speed Transmission Question

The availability of high-fidelity energy consumption estimates and the ability to evaluate drivetrain efficiency are crucial for effectively planning a large-scale transition to electric vehicles. For both new and retrofitted electric vehicles, a key question is the transmission type—single-speed or multi-speed—and the resulting impact on the vehicle’s overall efficiency. This paper presents a comprehensive simulation-based methodology for evaluating the impact of transmission selection on vehicle efficiency using high-fidelity driving cycle data. The method can be used for new vehicles and retrofit applications where a transmission is already present. The efficiency of a single-speed reduction gearbox was compared to that of a five-speed multi-speed transmission in a retrofitted vehicle, of which the impact of the manual transmission on the vehicle dynamics and efficiency was examined. The manual transmission proved to be more efficient for a perfect gear-shifting strategy.

Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. Doi: 10.28991/HIJ-2023-04-04-014 Full Text: PDF

RETRACTED: An innovative simulation-based methodology for evaluating cooling strategies in climate change-induced overheating

As global climate change progresses the built environment grapples with the increasing challenge of overheating. In response to these challenges, this study introduces an innovative simulation-based methodology aimed at enhancing the resilience and sustainability of cooling strategies. The proposed methodology utilizes weather and building data characterization, user clothing behavior, and cooling strategy selection. The methodology relies on three main indicators: Indoor Overheating Degree (IOD), Ambient Warmness Degree (AWD), and Climate Change Overheating Resistivity (CCOR). It also considers sub-indexes like analysis of greenhouse gas emissions, energy consumption, Exceedance Hours, and the average Predicted Percentage of Dissatisfaction. This comprehensive approach allows for a multi-zonal assessment of indoor overheating risk and resilience to climate change, to validate the methodology ‘s effectiveness, we conducted a thorough comparison, focusing on Packaged Terminal Heat Pumps (PTHP) (C01) and Fan Coil Units (FCU) (C02) in six selected reference cities with different climates. The case study employs a shoebox model to depict a double-zone office and administration building. In general, C01 demonstrates greater resistance to climate change-induced overheating compared to C02. The highest CCOR value, 58.16, is found in C01 in Vienna, while the lowest CCOR value, 12.7, is observed in C02 in Montreal, indicating the lowest resistance. Furthermore, this study underscores the pivotal importance of meticulously evaluating the susceptibility of cold cities to the imminent impacts of climate change and the urgency of implementing proactive strategies to bolster their resilience.

Solar radiation-based method for early design stages to balance daylight and thermal comfort in office buildings

There is a lack of facade design methods for early design stages to balance thermal comfort and daylight provision that consider the obstruction angle as an independent variable without using modeling and simulations. This paper aims to develop easy-to use solar radiation-based prediction method for the design of office building facades (i.e., design parameters: room size, window-to-floor ratio, and glazing thermal/optical properties) located in urban canyons to balance daylight provision according to the European standard EN 17037:2018 and thermal comfort through specific cooling capacity. We used a simulation-based methodology that includes correlation analyses between building performance metrics and design parameters, the development of design workflows, accuracy analysis, and validation through the application of the workflows to a new development office building facades located in Tallinn, Estonia. The validation showed that the mean percentage of right/conservative predictions of thermal comfort classes is 98.8% whereas for daylight provision, it is higher than 75.6%. The use of the proposed prediction method can help designers to work more efficiently during early design stages and to obtain optimal performative solutions in much shorter time: window sizing in 73,152 room combinations in 80 s.

Insights into transport in mucus barrier: Exploring particle penetration through the intestinal mucus layer

Nearly all pharmaceuticals and nutrients must cross numerous barriers to gain access and be eliminated from the body. The gastrointestinal mucosa, comprised of specialized epithelial cells layered with mucus, is among the most critical of these barriers. With its intricate structure, this mucus layer facilitates nutrient transport and impedes the transit of toxins and bacteria. The permeability of this mucus layer is key in determining the temporal availability and concentration of various compounds. Numerous methods for enhancing the permeability of select particles through this layer have been examined, employing diverse techniques to quantify the impact of these alterations. However, studies exploring mucus permeability have largely overlooked the potential effect of fluid flow on the mucus layer. In this research, we apply numerical methods to investigate the penetration of particles and drugs through the mucus layer towards the intestinal epithelium, comparing the benefits and distinctions of these methods.To simulate hydrodynamic effects within the mucus layer, we model intestinal mucus as a Herschel Bulkley fluid, solving the Navier-Stokes equations to simulate fluid flow. These equations are integrated with mass transfer equations to emulate particle penetration through the mucus layer. Our work utilized two different scenarios to simulate the penetration of Brilliant Blue FCF (BFC) into the mucus layer. In the first scenario, molecular diffusion is the sole mechanism responsible for mass transfer. In the second scenario, we also consider convection as an auxiliary mechanism for BFC penetration.By comparing our simulation outcomes with experimental observations, we demonstrate the necessity of incorporating the convection term to accurately mirror experimental findings. Moreover, we analyzed the effect of varying the diffusion coefficient and viscosity on penetration. The findings revealed that both parameters significantly influence BFC penetration. We also assessed the concentration of drug samples and particles of varying sizes and surface coatings at the epithelial layer. The simulation-based methodology developed in this study shows that internal fluid flow within the mucus layer can profoundly impact particle transport, necessitating the consideration of the convection mechanism for mass transfer in both numerical simulations and penetration analyses.