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Routine household tasks, such as laundry or dishwashing, are complex socio-technical systems in which a variety of factors and actors interact, including manufacturers, technologies, regulators, consumer practices, cultural norms and infrastructures. A deep understanding of these socio-technical systems is necessary to find the right recommendations on how to optimise this system in order to achieve relevant savings in resource consumption without requiring a complete renewal of the infrastructure installed. In our case, we focus on automatic dishwashing in the United Kingdom and try to find out what an optimised consumer use of the dishwashers installed can look like and how many savings can be achieved. Accordingly, we modelled the base of dishwashers installed and conducted a consumer survey on dishwasher usage behaviour in sufficient detail to understand the interplay between the various factors. By simulating a change in usage behaviour, we find a potential energy saving of 21.3 % simply by following basic rules for selecting the appropriate programmes without the risk of a loss of performance. [ABSTRACT FROM AUTHOR]

Aim: Sampling bias and gaps have a direct influence on the perceived patterns of biodiversity, hence limiting our ability to make well‐informed decisions about biodiversity conservation. Yet most methods either disregard or underestimate the effects of sampling bias and gaps in modelling biodiversity patterns. Our objective is to test the sensitivity of commonly used methods for modelling biodiversity dimensions (richness, endemism, and beta diversity) to sampling bias and collection gaps, and as a way to mitigate those effects we introduce a novel approach that employs the sampling effort to minimize the effects of collection bias and gaps in biodiversity models. Location: South America. Methods: Here, we use controlled simulations of virtual species distribution and sampling effort to test the sensitivity to sampling bias and collection gaps by commonly used methods, that is, species distribution models (SDMs), spatial interpolation (SI), and environmental prediction (EP), for estimating species richness, endemism, and beta diversity. Our research contributes to advancing biodiversity modelling by introducing a novel approach, named uniform sampling from sampling effort (USSE), that employs the sampling effort to minimize the effects of collection bias and gaps. Results and Main Conclusions: EP with USSE has proven effective in accurately predicting species richness, especially in scenarios in which the sampling effort does not coincide with the biodiversity niches. It outperformed SI and SDMs. The latter performed poorly, yielding the lowest predictive score. In estimating endemism and beta diversity, all methods yielded similar results, without statistically significant differences. For estimating beta diversity, the generalized dissimilarity model proved to be a robust method, even in face of biased sampling. Controlled simulations are key to testing biodiversity methods. These tests can isolate confounding factors inherent to real‐world data, enabling robust methodological assessments. Although fieldwork and curation of collections must remain indispensable, novel biodiversity methods could help overcome the limitations of sampling biases, helping expedite conservation actions much needed. [ABSTRACT FROM AUTHOR]

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids' dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region. [ABSTRACT FROM AUTHOR]

Simulation software like ANSYS, COMSOL, and SimScale excel at modeling heat transfer phenomena, but their extensive functionalities necessitate a deep understanding, making them less suitable and too expensive for use in educational settings below the post-secondary level in Singapore, where the current curriculum does not demand such advanced capabilities. To provide a more accessible and cost-effective solution, this work introduces a novel universal Python code designed to simplify the understanding of 2D steady-state heat transfer on irregular shapes, utilizing only Microsoft Excel and Python. The developed code employs the Gauss–Seidel iteration method within a full multigrid framework, applying the relevant nodal finite-difference equations based on the node type within a 2D irregular shape delineated by a 65 × 65 mesh in Excel. The generated contour plots from these simulations are meticulously compared with those produced by ANSYS to validate accuracy. The comparison reveals that the results from the Python code closely align with those from ANSYS, showing only minor differences. Consequently, the Python code emerges as a viable and simplified alternative for conducting 2D steady-state heat transfer simulations, making it a valuable tool for educational purposes, bridging the gap between complex simulation software and the educational needs of students in Singapore. [ABSTRACT FROM AUTHOR]

Elevator systems serve as the primary mode of transportation in tall buildings which consumes approximately 5–15% of a building's total energy demand. This research explores the potential for energy savings in elevator systems while maintaining passenger comfort through the implementation of green approaches. The research concentrates on a contemporary high-rise office building situated in the central business district of a developing economy. It employs a case study methodology involving traffic simulation to determine optimal elevator specifications for industry-standard service levels. By conducting the simulation, the researchers identified the optimal number of elevators, elevator capacity, speed, and the most suitable elevator management system. Following that, a range of green measures were implemented, including the incorporation of a regenerative system, to effectively reduce the electrical energy consumption of the elevator system. Subsequently, a passenger traffic simulation model was integrated with an energy calculation model to jointly simulate and calculate the elevator system's overall energy consumption and regeneration. The elevator energy requirements were optimized, while maintaining user-friendliness and requirements related to guidelines given in the standards. The results showed that 36% of the energy consumption was reduced by incorporating an energy regenerative option into the elevator system in addition to selecting efficient mechanical components and implementing an efficient elevator traffic management system. This research contributes to the limited body of literature on energy optimization in elevators and emphasizes the importance of balancing energy efficiency with service quality. These findings provide guidance for establishing benchmarks in reducing energy consumption, in relation to elevator systems. [ABSTRACT FROM AUTHOR]

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like "renewable energy sources", "optimization", and "simulation" are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

Bicycle simulators have been the subject of considerable research, however, few of these attempts have integrated direct balance control and realistic freedom of motion to deliver a real-world dynamic cycling experience. This study presents the BIKE (Bicycle Intrinsic Kinematics Emulator) system, a kinematic bicycle simulator, developed with the purpose of letting its users experience realistic steer, roll, yaw and sway motions. Motion is provided with Carvallo–Whipple bicycle model-based control of sway and yaw combined with passive steer and roll. This study validates the BIKE simulator by comparing cycling behaviour and subjective evaluation for the simulator with and without motion to outdoor tests with an instrumented bicycle. 15 participants of varying age and mass, performed straight-line cycling, at low ( $ 5\,\hbox {km/h} $ 5 km/h ) to high ( $ 40\,\hbox {km/h} $ 40 km/h ) velocities and zig-zag manoeuvres. Results show that users can successfully rely on existing cycling skills to use the simulator with motion. Objectively, in the kinematic sense, the simulator with motion performs similarly to an outdoor bicycle. Subjectively, the simulator performs better with motion and is experienced by riders as close to real outdoor cycling. [ABSTRACT FROM AUTHOR]

This article focuses on the investigation and analysis of vibrations transmitted to cargo during off-road transportation, with particular emphasis on the impact of vehicle and road surface interactions. The primary objective of the research is to quantify and characterize the amplitudes of vibrations generated by the interaction between heavyduty truck tires and rough terrain, and their subsequent transmission to cargo containers. To achieve this, a virtual model of a tactical transportation truck was created using TruckSim software. Two characteristic off-road tracks were simulated, based on driving conditions typically experienced by heavy-duty vehicles in demanding logistical scenarios. The experimental validation of the virtual model was conducted using a heavy-duty truck outfitted with a 20 ft (6096 mm) cargo container. The results of the work include recorded acceleration data, suspension behavior, and the maximum driving speed at which the vehicle remained stable on both tracks. Moreover, the work is a direct response to the needs of the automotive industry and the military. [ABSTRACT FROM AUTHOR]

This article focuses on the investigation and analysis of vibrations transmitted to cargo during off-road transportation, with particular emphasis on the impact of vehicle and road surface interactions. The primary objective of the research is to quantify and characterize the amplitudes of vibrations generated by the interaction between heavy-duty truck tires and rough terrain, and their subsequent transmission to cargo containers. To achieve this, a virtual model of a tactical transportation truck was created using TruckSim software. Two characteristic off-road tracks were simulated, based on driving conditions typically experienced by heavy-duty vehicles in demanding logistical scenarios. The experimental validation of the virtual model was conducted using a heavy-duty truck outfitted with a 20 ft (6096 mm) cargo container. The results of the work include recorded acceleration data, suspension behavior, and the maximum driving speed at which the vehicle remained stable on both tracks. Moreover, the work is a direct response to the needs of the automotive industry and the military.

Power plants are part of industrial facilities used to produce and generate electricity from various power sources; one of those is PLTGU (Pembangkit Listrik Tenaga Gas dan Uap or Gas and Steam Power Plant). PLTGU is a combined cycle between PLTG and PLTU. It is necessary to achieve a high-capacity target for the PLTGU to increase the generator's efficiency. One way to increase the efficiency of gas and steam power plants is by optimizing heat transfer in the Heat Recovery Steam Generator (HRSG). HRSG has several modules such as superheater, evaporator, economizer, and preheater. One that plays an essential role in absorbed high-temperature steam from the gas turbine is the superheater. The function of the superheater is to utilize the heat from the flue gas to reheat the fluid needed for the steam turbine. In this research, improvements of superheater were made with several fin variations at HRSG PLTGU. Variable of superheater refer to data on the layout of the HRSG PLTGU. Autodesk Inventor is used to modeling flue gas domain, tube, and fin. Additional of the fin has the purpose of optimizing heat transfer distribution in steam through a tube, such as an outlet temperature and efficiency of steam in tube superheater. The use of CFD (Computational Fluid Dynamic) with ANSYS Fluent could use to determine the temperature distribution of the superheater. The most optimal efficiency and outlet temperature of variation fin is the annular fin variation compared to the rectangular and straight fin variations.

The results of computer modelling of an injection moulding process with microcellular foaming (MuCell®) were presented in this work. The process is based on the dissolving nitrogen in a liquid polymer which is possible when nitrogen is in supercritical fluid state (SCF). After pressure drop of the melt in the injection mould the intensive nucleation of pores occurs and, as the result, the material with high concentration of small pores is created. The pores obtained in this way are of much smaller size than in a conventional foaming process. The pore size in the cross-section of an exemplary injection moulded part was calculated in the computer modelling and compared to the results of microscopical investigation made on the real injection moulded part. It was found that the size of the pores depends on the flow length inside the injection mould and on the position in the part’s cross-section.

Rotational moulding is a unique low-shear process used to manufacture hollow parts. The process is an excellent process method for batch processing, minimal waste and stress-free parts. However, the process has drawbacks such as long cycle times, gas dependency and a limited palette of materials relative to other process methods. This review aimed to shed light on the current state-of-the-art research contributing towards sustainability in rotational moulding. The scope of this review broadly assessed all areas of the process such as material development, process adaptations and development, modelling, simulation and contributions towards applications carving a more sustainable society. The PRISMA literature review method was adopted, finding that the majority of publications focus on material development, specifically on the use of waste, fillers, fibres and composites as a way to improve sustainability. Significant focus on biocomposites and natural fibres highlighted the strong research interest, while recyclate studies appeared to be less explored to date. Other research paths are process modification, modelling and simulation, motivated to increase energy efficiency, reduction in scrap and attempts to reduce cycle time with models. An emerging research interest in rotational moulding is the contribution towards the hydrogen economy, particularly type IV hydrogen vessels. [ABSTRACT FROM AUTHOR]

Simple Summary: Simple Summary: The global demand for livestock products is increasing, and to meet this demand there is a need for more research efforts to increase livestock production. Despite varieties of simulation models developed and used in the beef production sector, an overview of available research is lacking. A systematic literature review was conducted to provide an overview of available beef production simulation modelling research and to describe simulation approaches used for livestock management. A growing research interest in simulating beef production systems was noted, with the main characteristics of the studies being biophysical and bioeconomic study types, deterministic and dynamic simulation approaches, whole-farm scenarios, and a focus area of productivity and economy. We recognized the need for improving the availability of information related to model validation techniques and type of software or programming languages used, which could facilitate the further research extension and/or adoption of simulation modelling studies in livestock management. Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making. [ABSTRACT FROM AUTHOR]

The presented article deals with the issue of inventory management optimization through modelling and simulation in the software Tecnomatix Plant Simulation. Based on an in-depth analysis of the supply chain with an emphasis on inventory management, a simulation model was developed. It was optimized using the inventory theory methodology in combination with a discrete-event simulation approach. Dynamic simulation made it possible to perform experiments and optimize the existing state. The solution to the problem is elaborated on a case study to achieve the efficiency of the supply process of a specific operation of a clothing retail chain, which is struggling with the problems of excessive and inaccurate supply. [ABSTRACT FROM AUTHOR]

The production of materials with a high affinity for oxidation using the direct metal deposition (DMD) process requires an extended process examination that goes beyond the usual, purely energetic consideration, with the aim of providing sufficient energy to melt the substrate and the powder material supplied. This is because the DMD process does not allow any conclusions to be drawn as to whether it and its respective selected parameters result in an oxidation critical process. To assess this, a superposition of the temperature field with the existing spatial oxygen concentration is required. This work uses this approach to develop an oxidation model that reduces oxidation during the DMD process. In addition to the numerical model, an analytical model is derived, with which the temperature of a material element can be calculated analytically and the resulting boundary oxygen concentration calculated using Fick's 2nd law. The model also takes into account two-stage oxidation kinetics for Ti alloys. The effect of too high a travel speed (with the same specific energy of the other experiments) is shown visually in the numerical calculation of the temperature field. However, if the process model is taken into account, the components do fulfil the specified requirements. Finally, the effect of oxidation on the microstructure, microhardness, ultimate strength, yield strength and elongation at failure of Ti6Al4V structures produced using DMD is also investigated, and further supports our conclusions regarding the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

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The direct torque control (DTC) strategy was proposed more than 25 years ago. It is one of the most successful and reliable techniques for AC motors. This paper presents the application of DTC using a four-switch inverter, as one of the non-conventional economic topologies suitable for low power ranges. The experimental prototype proves the validity and effectiveness of the investigated configuration. In addition, for comparison purposes, the DTC technique has been implemented using a conventional six-switch inverter. According to the experimental results, the DTC-based four-switch inverter would be competitive with the conventional topology in low power ranges to achieve an economic AC drive with a satisfactory transient and steady-state performance at moderate costs. [ABSTRACT FROM AUTHOR]

This article explores the significance of simulation-based analysis in understanding the effectiveness of material handling strategies. By utilizing simulation models, businesses can optimize production processes, streamline flows, and enhance overall logistics efficiency. In today's competitive market landscape, the significance of product manipulation cannot be overstated. It directly influences consumer perception and plays a pivotal role in gaining a competitive advantage. Simulation-based analysis has emerged as a powerful tool for optimizing production processes and enhancing logistics efficiency. Robotics sorting and loading offer increased accuracy, speed, and efficiency over manual processes. Their implementation boost productivity, cuts costs, and enhances working conditions. In today's competitive market, effective product handling shapes consumer perception and competitiveness. VR simulation-based analysis optimizes manufacturing, logistics, and robotics, driving efficiency. Through advanced VR simulation models, businesses streamline operations, adapt to market dynamics, and embrace automation, enhancing competitiveness. [ABSTRACT FROM AUTHOR]

Introduction: The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore corticocortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection--source-informed reconstruction approach (SSP--SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP--SIR. Methods: We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS--EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP--SIR, and subsequent comparisons were made with the ground truth data. Results: ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP--SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP--SIR being better when difference between topographies was larger. Overall, SSP--SIR performed better than ICA across the tested conditions. Based on these simulations, SSP--SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials. Discussion: Selecting between ICA and SSP--SIR should be guided by the characteristics of the artifacts. SSP--SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP--SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse. [ABSTRACT FROM AUTHOR]

We present a detailed analysis based on both experimental and 3D modelling approaches of the unique silicon nitride precipitation sequence observed in ferritic Fe-Si alloys upon nitriding. At 570 °C, Si3N4 silicon nitride was shown to form as an amorphous phase into α-Fe ferrite matrix, which is morphologically unstable over time. Precipitates nucleated with a spheroidal shape, then developed a cuboidal shape for intermediate sizes and octapod-like morphology for a longer time. Using transmission electron microscopy, we show that the transition between spheroid and cuboid morphology depended on particle size and resulted from competition between interfacial energy and elastic strain energy. The resulting morphology was then shown to be a cuboid shape whose faces were always parallel to the {100} planes of the α-Fe; the <100> directions of the matrix corresponded to the elastically soft directions. There was a critical size of around 45 nm for which the transition between the cuboid shape and the octapod-like morphology took place. This was characterised by a transformation of quasi-flat facets into concave ones and the development of lobes in the <111> directions of the bcc crystal. To better assess the kinetic effects of diffusion fields and internal stresses on the morphological instability observed, an original 3D model that explicitly coupled phase transformations and mechanical fields was developed and applied. The latter, validated on the basis of model cases, was shown to be able to describe the time-evolution of both chemical and mechanical fields and their interactions in diffusive mass transport. Using a model system, it was shown that the concentration field around the precipitates and the internal stresses played opposing roles in the cuboid to octapod-like morphological instability. This work gives some clarification regarding the morphological evolution of amorphous Si3N4 precipitates, an important point for controlling the mechanical properties of nitrogen steels. [ABSTRACT FROM AUTHOR]

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.

This study introduces and evaluates a methodology to define optimal integrated short and long-term air pollution control measures, to support policy formulation by Local Authorities. The approach utilized in this methodology is based on a receding horizon strategy. In this approach, an autoregressive model provides the dynamic characteristics of air quality within a designated time period. The model is established using daily observed data on pollutant concentration, meteorological variables, and estimated emission data in the study area. The model is the core of a model predictive control based on the solution, at each time step, of the resulting optimization problem. The effectiveness of the overall control has been assessed in the context of controlling NO2 concentrations within the city of Milan. The outcomes of the study demonstrate that this control system can serve as a valuable tool to assist Local Authorities in making informed decisions regarding appropriate air quality management strategies.

TRIP (Transformation Induced Plasticity) steels belong to the group of advanced high-strength steels. Their main advantage is their excellent strength combined with high ductility, which makes them ideal for deep drawing processes. The forming of TRIP steels in the deep drawing process enables the production of a thin-walled final product with superior mechanical properties. For this reason, this study presents comprehensive research into the deep drawing of cylindrical cups made from TRIP steel. The research focuses on three main aspects of the deep drawing process, namely the sheet metal thinning, the maximum force value and the ear height as a result of the anisotropic material behaviour. Artificial neural networks (ANNs) were built to predict all the mentioned output parameters of the part or the process itself. The ANNs were trained using data obtained from a sufficient number of simulations based on the finite element method (FEM). The ANN models were developed based on variable material properties, including anisotropic parameters, blank holding force, blank diameter, and friction coefficient. A good agreement between simulation, ANN and experimental results is evident. [ABSTRACT FROM AUTHOR]

In fuel‐bearing components, particularly in automotive applications operating at elevated temperatures, the durability of light metals is significantly influenced by their susceptibility to alkoxide corrosion. Alkoxide corrosion is characterized by its spontaneous nature and exceptionally rapid degradation of materials once initiated. This study presents an innovative high‐pressure and high‐temperature micro‐reactor, which enables precise measurements with superior sensitivity for determining the exact initiation times and reaction rates of pitting corrosion. Exemplified tests of surface roughness and water content effect on pitting initiation times were conducted and data was generated for a numerical phase field model to demonstrate the reactor capabilities. Experimental findings suggest that impurities present on both the material surface and in the fuel exhibit a significant influence on corrosivity, thereby affecting the reliability of the components. Moreover, the experimental data points have been utilized to extract the corrosion kinetics and calibrate the numerical model. The initial findings successfully demonstrate the ability to replicate corrosion kinetics and accurately represent pit morphologies and estimate reaction‐related parameters in a predictive manner. [ABSTRACT FROM AUTHOR]

To improve the operational time of electronic devices and the driving spectra of electric cars, multiple investigation missions regarding batteries are focussed on refining the energy capacity of batteries. However, this pursuit of better performance introduces potential risks, such as fire incidents, due to the use of dynamic materials in lithium‐ion batteries (LIBs) to enhance their electrochemical performance. These fire incidents have resulted in financial losses and raised safety concerns. One common type of battery failure, called thermal runaway (TR), takes place when the rate of heat discharge from internal chain reactions accelerates the level of external cooling. This study aims to model and comprehensively analyse the phenomenon of TR in LIBs by investigating its underlying physics. To conduct this research systematically, a mathematical framework of heat transfer from existing literature has been employed as a foundation to come up with a novel framework specifically for LIBs built on the ideas of Semenov's equation. The established model associates both thermal and mass balance aspects, with the consideration of response kinetics including other heat transfer modes (convective and radiation). Range–Kutta technique is used to unravel the model's equations instantaneously using MATLAB. Consequently, the impact of model factors on the thermal performance of LIBs has been evaluated. The outcomes of the study reveal that when an increase in activation temperature occurs, a decrease in peak temperature is followed whilst constant circumstances are ensured. This implies that more time is needed in order for the temperature to reach its maximum. Furthermore, the simulation demonstrated that any growth in the body capacity of the battery for absorbing heat during the reaction results in a higher maximum temperature. Heat convection is found to have a greater impact on the occurrence of TR in LIBs compared to heat radiation. For improved accuracy in calculating LIBs TR, an accurate kinetic model could be utilised in future research endeavours. [ABSTRACT FROM AUTHOR]

The paper proposes a feedback force and velocity control of an arm exoskeleton to assist user motion. The original published control so-called feedback hybrid force and position control was based on the force and position control and was designed to assist user motion. This original control was successful at providing assist for the user's arm. This article presents an improved control scheme called the feedback force and velocity control. The proposed control is designed to regulate the velocities of joints of the exoskeleton and the feedback forces on links to assist user motion. The design and optimization of the feedback force and velocity control are realized by the Balancing Composite Motion Optimization (BCMO). The numerical method is realized in the paper to show that the proposed control is better than the original control in terms of less oscillation and fast response. [ABSTRACT FROM AUTHOR]

Pressure swing adsorption(PSA) based gas separation technology is widely applied in chemical industries, especially in hydrogen purification. Its complex mechanism and non-steady state feature restrict the separation performance adjustment and application on efficient gas utilization. Process simulation, machine learning and modelling combined innovating gas management methodology is proposed in this paper to perform accurate gas separation for natural gas based syngas mixture. VB script program is employed to drive Aspen adsorption simulation, and then classical and regression tree(CART), polynomial regression(PNR) and surrogate model are comprehensively used to perform machine learning. Consequently, the coefficient matrix is obtained to quantify the separation performance. This method holds the potential for broader application in refineries and SPCs hybrid hydrogen network, facilitating the comprehensive utilization of hydrogen containing gases. Case study results show this methodology can conserve natural gas and gas productivity by 21.3 %, as well as carbon emission by 8211.6 Nm3/h. This work highlights the promising application of digital twin technology within the chemical industry, showcasing how process simulation, machine learning, and modeling can collectively revolutionize gas management and resource conservation. [Display omitted] • Novel PSA based gas allocation instead of gas separation technology. • Simulation, modelling and machine learning combined gas accommodating methodology. • An insight technology for integration of refinery and SPC. • A novel gas allocation method for zero waste of hydrogen. • An insight application of digital twin technology for gas efficient utilization. [ABSTRACT FROM AUTHOR]

Climate change and land-use changes are projected to make wildfires more frequent and intense, with a global increase of extreme fires of up to 14 % by 2030, 30 % by the end of 2050 and 50 % by the end of the century (Sullivan et al., 2022). This latest information has increased interest of how the large scale, often catastrophic, events can be reduced and more effectively managed. One critical area revolves around real-time fire line prediction and how resources can be better deployed to reduce the propagation of wildfires. This paper explores mathematical models for fire propagation on a fully configurable grid using the Irregular Grid Software (IGS) developed. The configurable grid allows cross comparison of both regular grids such as square, hexagonal, triangular, and irregular grids such as a randomly seeded Voronoi diagram and a flammable resolution grid (FRG). The FRG is adapted to focus attention on areas of higher importance which provides greater precision at the cost of extra computing time. The irregular grid approach and ForeFire, an existing industry standard program were compared. The comparison included simulations of wildfires located in the Wicklow Mountains, in Ireland, a region used by the fire services for exercises. The performance of the grid-based techniques was examined using a set of experiments to characterise the model's response to key factors such as wind, elevation, and fuel type. The results show that the IGS runs on average 34 times quicker than ForeFire while retaining an average result similarity of 80% with ForeFire. In this paper sections 1 and 2 will give an overview on existing research on wildfires and wildfire modelling. Section 3 will describe the resources that were necessary to model wildfire propagation. Section 4 explains how these resources were used to build the IGS. Section 5 compares different gird types produced using the IGS, while section 6 compares the IGS to ForeFire. Sections 7 and 8 discus these results. [ABSTRACT FROM AUTHOR]

In DWI, multi-exponential signal analysis can be used to determine signal underlying diffusion components. However, the approach is very complex due to the inherent low SNR, the limited number of signal decay data points, and the absence of appropriate acquisition parameters and standardized analysis methods. Within the scope of this work, different methods for multi-exponential analysis of the diffusion signal in the kidney were compared. To assess the impact of fitting parameters, a simulation was conducted comparing the free non-negative (NNLS) and rigid non-linear least square (NLLS) fitting methods. The simulation demonstrated improved accuracy for NNLS in combination with area-under-curve estimation. Furthermore, the accuracy and stability of the results were further enhanced utilizing optimized parameters, namely 350 logarithmically spaced diffusion coefficients within [0.7, 300] × 10−3 mm2/s and a minimal SNR of 100. The NNLS approach shows an improvement over the rigid NLLS method. This becomes apparent not only in terms of accuracy and omitting prior knowledge, but also in better representation of renal tissue physiology. By employing the determined fitting parameters, it is expected that more stable and reliable results for diffusion imaging in the kidney can be achieved. This might enable more accurate DWI results for clinical utilization. [ABSTRACT FROM AUTHOR]

Mathematics provides us with tools to capture and explain phenomena in everyday biology, even at the nanoscale. The most regularly applied technique to biology is differential equations. In this article, we seek to present how differential equation models of biological phenomena, particularly the flow through ion channels, can be used to motivate and teach differential equations. Ion channels on the cell membrane allow the passage of ions from one side of the membrane to the other. The movement of these ions drives crucial processes such as the beating of our hearts. Using a system of two ordinary differential equations it is possible to capture the movement across ion channels that are opening and closing. Then using standard undergraduate techniques, we can predict how these channels behave in the long-term. In this work, we discuss how this example can be used to create tangible links to mathematical equations and motivate the teaching of techniques such as differentiation, integration, algebraic manipulation and equilibrium analysis. Furthermore, we show how a simple reformulation of this model into a stochastic setting using Gillespie's Stochastic Simulation Algorithm can allow us to capture the noise in ion channel flow. [ABSTRACT FROM AUTHOR]

Biomass as a whole offers a more diverse potential for valorisation than any other renewable energy source. As one of the stages in the separation of bio-oil involves a liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method was developed for their separation with a high purity level through an approach combining liquid–liquid extraction and distillation. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software was used. Sensitivity analyses were carried out to investigate the influence of the different parameters in the distillation columns, such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process, as well as the possibility of regenerating the extractant, was also studied. Tri-n-octylamine was accordingly selected as a separating agent that was fully recycled. The end result for separating an initial 48/52 wt% acetol/acetic acid liquid mixture was acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%. [ABSTRACT FROM AUTHOR]

This work intended to build a mathematical model for a designed, constructed and tested clean, transportable and comfortable advanced biomass cookstove. The system was subdivided into 31 isothermal subvolumes (nodes), interconnected to one another by thermal resistances corresponding to a particular type of heat transfer. A differential system of 31 equations governing the heat transfer in the ovens' configurations was obtained, and numerical simulations were carried out in order to appreciate the thermal behavior of the systems. A validated data acquisition device (device SAG) for controlling the thermal behavior of stoves or heating systems was used for experimental studies. The numerical results were in agreement with the experimental results and needed a low computational time. [ABSTRACT FROM AUTHOR]

Communities are often exposed to high health risks via the presence of most heavy metals in water bodies used by their residents. Chromium, an abundant heavy metal in industrial effluents, contaminates our environment and water sources, endangering human and animal consumption. Designing materials for Cr removal from potential effluent discharges is crucial. Bio-based sorbents have been explored, but the role of functional groups remains unclear. This research investigates the influence of functional groups in removing dissolved chromium heavy metals to enhance water quality. In this study, we look at how well functional groups like carboxylate, carbonyl, nitrile, aldehyde, alcohol, and carboxylic acid bind to Cr (III) metal. We look at stable Cr metal cluster configurations using Spartan software and density functional theory (DFT) for quantum chemical calculations. We measure energies and thermodynamic properties during interactions. Our findings demonstrate that sorbents with carbonyl and carboxylate groups exhibit high sensitivity to Cr, making them effective for removal. The relative adsorption energies align with experimental results. This study confirms the potential of computational methods to predict sorbents' selectivity in removing various heavy metals from water, offering a promising avenue for water treatment and environmental protection. [ABSTRACT FROM AUTHOR]

The results of computer modelling of an injection moulding process with microcellular foaming (MuCell®) were presented in this work. The process is based on the dissolving nitrogen in a liquid polymer which is possible when nitrogen is in supercritical fluid state (SCF). After pressure drop of the melt in the injection mould the intensive nucleation of pores occurs and, as the result, the material with high concentration of small pores is created. The pores obtained in this way are of much smaller size than in a conventional foaming process. The pore size in the cross-section of an exemplary injection moulded part was calculated in the computer modelling and compared to the results of microscopical investigation made on the real injection moulded part. It was found that the size of the pores depends on the flow length inside the injection mould and on the position in the part's cross-section. [ABSTRACT FROM AUTHOR]

Miniaturized microelectromechanical system (MEMS) microspeakers are currently trending in the development of acoustic transducers. When a transducer is scaled down to fit on a microelectronic chip, its physics differ from the macroscopic world, and some common modeling assumptions become invalid. One of the effects observed in MEMS microspeakers is nonlinear squeeze film damping. Understanding this effect is crucial, as non-linearities in the speaker can result in perceptible harmonic distortions, which are undesirable in audio applications. In this study, we analyze the influence of squeeze film damping on harmonic distortions using a lumped parameter model of a MEMS microspeaker. This leads to a nonlinear ordinary differential equation, and an approximate analytical solution for moderate non-linearities is obtained using homotopy. We present our solution strategy, including the resulting closed-form expression, and verify our findings against numerical solutions. [ABSTRACT FROM AUTHOR]

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke plane through elliptical fittings, up to detecting and isolating the fault depending on the characteristics of the signature deviation from the nominal one. As a rough approximation, as typically proposed in the literature, the fault of one out of six power switches implies that, at constant speed operation, the phasor trajectory deviates from the nominal circular path up to a semi-circular "D-shape" signature, the inclination of which depends on the failed converter leg. However, this evolution can significantly deviate in practical cases, due to the dynamics related to the transition of motor phase connections from failed to active switches. The study demonstrates that an online ellipse fitting of the current signature can be effective for diagnosis, through correlating the ellipse centre to the location of the failed switch. The performances of the proposed monitoring technique are here assessed via the nonlinear simulation of a PMSM employed for the propulsion of a lightweight fixed-wing Unmanned Aerial Vehicle (UAV), by quantifying the fault latencies and the related transients. [ABSTRACT FROM AUTHOR]

Farm-level benchmarking and comparative analysis provide a basis for orientation on national, regional and global level. In the agri benchmark global network, we compare beef and sheep farms (as well as other branches) from more than 30 countries. In a partnership approach with researchers, producers and local experts, we collect and validated farm-level data using a standard operating procedure and a farm-level simulation model. In cow-calf farms, winter housing is associated with higher live weight production. Profitability is mainly driven by weaner prices and their variation and to a lesser extent by costs. In the EU, government payments play a crucial role in determining profitability. In sheep farming, performance and economic framework conditions differ across production systems and countries. Sheep profitability shows large variations with higher profits being achieved outside of Europe. The global outlook for beef and sheep production remains positive, with declining numbers in Europe and challenges related to workforce and environmental regulations. [ABSTRACT FROM AUTHOR]

This paper compares different uniaxial and biaxial graded designs of auxetic reentrant honeycomb structures to enhance their mechanical properties, especially the specific energy absorption under compressive load. The lattice structures were 3D printed using the vat photopolymerization masked-stereolithography technique from two different materials – tough (OR) and flexible (FR). The results were evaluated from a material and structural point of view, investigating the effect of porosity, cell number, size, graded design, and fracture mode. The universally best energy-absorbing performance was found in a biaxially graded structure with a center-wise location of the highest local porosity. Depending on the used resin, its energy absorption capacity was up to 2–3 times enhanced compared to a reference uniform-porosity auxetic design. The presented data constitutes a fundamental understanding of auxetic structures and identifies practical approaches for tuning the auxetic structures’ performance regarding their mechanical response. Finally, this study demonstrates the potential of shape versatility offered by 3D printing and other additive manufacturing techniques.

The depletion of conventional energy resources has created a bunyanesque energy problem in the current world order. In this article, the byzantine issues of optimisation i.e. modelling, simulations, sustainability assessment and economic analysis examples are discussed briefly. Life span analysis and associated capital costs for various solar drying systems are elucidated with comparative graphics for insights into the economic benchmarking of different models. This compendium work highlights the suitable combinations of numerical modelling and simulation softwares frequently used. In addition, the recent studies on environmental assistance are reviewed which is a crucial parameter for future studies on solar dryer optimisation as they should not only limited to exergy-sustainability criteria but to expand the studies on social impact as well.

Simulation software like ANSYS, COMSOL, and SimScale excel at modeling heat transfer phenomena, but their extensive functionalities necessitate a deep understanding, making them less suitable and too expensive for use in educational settings below the post-secondary level in Singapore, where the current curriculum does not demand such advanced capabilities. To provide a more accessible and cost-effective solution, this work introduces a novel universal Python code designed to simplify the understanding of 2D steady-state heat transfer on irregular shapes, utilizing only Microsoft Excel and Python. The developed code employs the Gauss–Seidel iteration method within a full multigrid framework, applying the relevant nodal finite-difference equations based on the node type within a 2D irregular shape delineated by a 65 × 65 mesh in Excel. The generated contour plots from these simulations are meticulously compared with those produced by ANSYS to validate accuracy. The comparison reveals that the results from the Python code closely align with those from ANSYS, showing only minor differences. Consequently, the Python code emerges as a viable and simplified alternative for conducting 2D steady-state heat transfer simulations, making it a valuable tool for educational purposes, bridging the gap between complex simulation software and the educational needs of students in Singapore.