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In this review, we elaborate on how musculoskeletal (MSK) modeling combined with dynamic movement simulation is gradually evolving from a research tool to a promising in silico tool to assist medical doctors and physical therapists in decision making by providing parameters relating to dynamic MSK function and loading. This review primarily focuses on our own and related work to illustrate the framework and the interpretation of MSK model-based parameters in patients with 3 different conditions, that is, degenerative joint disease, cerebral palsy, and adult spinal deformities. By selecting these 3 clinical applications, we also aim to demonstrate the differing levels of clinical readiness of the different simulation frameworks introducing in silico model-based biomarkers of motor function to inform MSK rehabilitation and treatment, with the application for adult spinal deformities being the most recent of the 3. Based on these applications, barriers to clinical integration and positioning of these in silico technologies within standard clinical practice are discussed in the light of specific challenges related to model assumptions, required level of complexity and personalization, and clinical implementation. [ABSTRACT FROM AUTHOR]

Coke calcination is a process that involves heating green petroleum coke to purify it and eliminate volatile materials for subsequent processing. Due to the complexity of the rotary kiln used in this process, conducting experimental studies can be challenging and restricted. However, quantitative analyses based on developed models can provide a foundation for optimizing and controlling the process, which can significantly enhance the design efficiency. A three-dimensional simulation model of a rotary calcining kiln for petroleum coke was created using COMSOL Multiphysics in a steady-state mode. This model accounted for all relevant physical and chemical phenomena in the gas stream and coke bed flow, including heat transfer, combustion, and the evolution of volatile matter and coke dust. The mathematical modeling yielded distributions of temperature and mass fractions within the kiln, as well as the velocity field. The results revealed two distinct peak temperatures in the gas phase: 1,780 K near the primary air injection point and 1,605 K near the tertiary air injection points. The findings were analyzed, and the impact of key variables was explored. The simulation data indicated that for every decrease of 10–15 m/s in air flow, the gas peak temperature dropped by approximately 100°. Additionally, an increase in the input oxygen concentration led to enhanced combustion, resulting in higher peak concentrations of CO2. The developed simulation model has proven to be a valuable and promising tool for the design and optimization of petroleum coke rotary kilns. [ABSTRACT FROM AUTHOR]

Sustainable aviation fuels are a major candidate to reduce pollutant emissions in future aeronautical engines. Recently, the use of hydrogen as a fuel has gained a high interest partly because its combustion is free from carbon dioxide, a greenhouse gas, and produces few pollutants, mainly nitrogen oxides (NOx). Over the last decades, efforts on numerical methods for combustion simulation in aero-engines have largely been focused on kerosene-air combustion. However, the current transition may have a significant impact on the computational methodologies for combustor design. Hydrogen defines novel modeling issues and challenges the current state of art on numerical methodologies. The current study presents a numerical investigation of a hydrogen-air burner using large-eddy simulations (LES) with a focus on NOx prediction. The considered configuration is a two-staged combustor, similar to the well-known RQL (Rich-Quench-Lean) technology, supplied by a single coaxial injector characterized experimentally. Two combustion models are investigated: (i) tabulated chemistry based on premixed flamelets (ii) transported chemistry description by using a 21-species chemical scheme. Numerical results are compared with experimental data (NOx concentrations, temperature distributions, pressure losses). A focus on model predictions is carried out. Results show a good agreement to predict the main flow characteristics and the premixed flame position over different operating points and geometries for both frameworks. In contrast, NOx emissions are more sensitive: while the overall trend is well captured, the quantification is more scattered. Finally, an in-depth analysis is proposed to link NOx production with the nonpremixed flame size. [ABSTRACT FROM AUTHOR]

In modern electric traction, direct current traction motors have been replaced with asynchronous motors with a short-circuited rotor. The justification is that asynchronous motors are more reliable, cheaper, and have smaller weights and dimensions, so they are more sustainable. In order to start and adjust the speed required in traction, these motors are powered from the contact line using a transformer and a static voltage and frequency converter. As a result, you can use green electricity produced with wind power plants or solar energy converted with photovoltaic panels, increasing sustainability because the consumption of traditional fuels is reduced. This paper presents various simulations emphasizing the negative effects of the distorting regime, with concrete results. The quality of the simulations carried out is increased by using a mathematical model, which uses the variable parameters of the motor dependent on the modulation of the current and the magnetic saturation. In modern 1500 kW electric locomotives, the traction motors are powered by static converters, which means an increase in losses when operating at nominal load on the motors by 38.7 kW and 217.8 kVAR compared to the sinusoidal three-phase power supply. Thus, the research carried out provides qualitatively and quantitatively correct simulations of the non-sinusoidal regime related to the asynchronous traction motor in order to increase the sustainability of this traction system. [ABSTRACT FROM AUTHOR]

An understanding of the mechanical behavior of polymeric materials is crucial for making advancements in the applications and efficiency of nanocomposites, and encompasses their service life, load resistance, and overall reliability. The present study focused on the prediction of the mechanical behavior of biopolymeric nanocomposites with nano-clays as the nanoadditives, using a new modeling and simulation method based on Comsol Multiphysics software 6.1. This modeling considered the complex case of flake-shaped nano-clay additives that could form aggregates along the polymeric matrix, varying the nanoadditive thickness, and consequently affecting the resulting mechanical properties of the polymeric nanocomposite. The polymeric matrix investigated was biopolyamide 11 (BIOPA11). Several BIOPA11 samples reinforced with three different contents of nano-clays (0, 3, and 10 wt%), and with three different nano-clay dispersion grades (employing three different extrusion screw configurations) were obtained by the compounding extrusion process. The mechanical behavior of these samples was studied by the experimental tensile test. The experimental results indicate an enhancement of Young's modulus as the nano-clay content was increased from 0 to 10 wt% for the same dispersion grades. In addition, the Young's modulus value increased when the dispersion rate of the nano-clays was improved, showing the highest increase of around 93% for the nanocomposite with 10 wt% nano-clay. A comparison of the modeled mechanical properties and the experimental measurements values was performed to validate the modeling results. The simulated results fit well with the experimental values of Young's modulus. [ABSTRACT FROM AUTHOR]

The phase problem in the context of focusing synchrotron beams with X‐ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X‐ray lens, or another optical system, using only a few intensity images. [ABSTRACT FROM AUTHOR]

This work used a unified dynamic model of microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) that were combined to create MFC-powered MECs. This allowed for a completely sustainable and green system of hydrogen production and wastewater treatment. The main advantage that was envisioned for this coupled system was the elimination of the need for an external power source; the only feed to the system was the wastewater itself. We present the results of parameter estimation and model validation. The results showed the importance of minimizing internal resistance to increase hydrogen production. Better powering of MECs was achieved by setting MFCs in series rather than in parallel. [ABSTRACT FROM AUTHOR]

Bulk density is among the fundamental properties for defining quality attributes of dehydrated foods. Mathematical models offer elegant tools for predicting bulk density and therefore optimizing dehydration processes. In this contribution, three machine learning techniques, namely extreme learning machine (ELM), support vector machine (SVM), and evolutionary polynomial regression (EPR), were assessed for predicting bulk density. Comprehensive experimental data including eight variables (product, initial moisture content, initial bulk density, pretreatment, technology, temperature, pressure, and microwave power) from 191 datasets were used to feed the models. All possible combinations of input variables were examined with ELM to obtain the optimum models involving three to eight input variables. The latter were ranked from one to eight according to their impact on the performance of the models in predicting bulk density. The results showed that the product and drying technology are the essential variables for predicting bulk density. The minimum impact on predictions was observed for pretreatment and power. The initial moisture content was shown to be relevant for predicting the bulk density of freeze-dried products. In addition, the best models obtained by ELM were examined and compared with SVM and EPR models. The SVM showed the highest accuracy, but the EPR had the best performance when both the accuracy and simplicity of the models were considered simultaneously. The developed equations can be used as tools for predicting bulk density and exploited in optimizing dehydration processes without needing costly, time-consuming, and energy-demanding experiments. Opportunities and limitations of artificial intelligence have also been discussed. [ABSTRACT FROM AUTHOR]

La Banda Primitiva is one of Europe's most notable symphonic civic bands. Located in Llíria (València, Spain), part of the UNESCO creative cities network, its theater was designed by Joaquín Rieta, one of the most relevant Valencian architects of the twentieth century. This study analyses the current state of the theater, its relevance to the town's cultural heritage, and how it has evolved over the years in terms of its acoustic performance. The objective is to understand how the theater's acoustics have evolved over the years and to unveil the reasons behind the preference of the regular audience for specific areas of the venue, considering if these tendencies are influenced by tradition rather than the current auditory experience. The theater's acoustics were studied with objective and subjective parameters. The objective parameters were assessed by conducting on-site measurements and ray-tracing simulations. One hundred and three musicians answered a survey of auralizations to evaluate subjective acoustic parameters. Three musical pieces were recorded in an anechoic chamber and convoluted by the impulse response of the venue at different positions to obtain the auralizations. The results show that the objective acoustic parameters do not differ significantly. Overall, the reverberation time was longer before the renovation. Regarding subjective testing, the sample only shows a subtle tendency (57%) towards preferring seats on the second balcony. For that reason, it can be concluded that there is no evidence to support the claim that the seats located on the second balcony have better acoustics than those in the stalls. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions. [ABSTRACT FROM AUTHOR]

The potential of paper and paperboard as fiber-based materials capable of replacing conventional polymer-based materials has been widely investigated and evaluated. Due to paper's limited extensibility and inherent heterogeneity, local structural variations lead to unpredictable local mechanical behavior and instability during processing, such as mechanical forming. To gain a deeper understanding of the impact of mechanical behavior and heterogeneity on the paper forming process, the Finite Element Method (FEM) coupled with continuum modeling is being explored as a potential approach to enhance comprehension. To achieve this goal, utilizing experimentally derived material parameters alongside stochastic finite element methods allows for more precise modeling of material behavior, considering the local material properties. This work first introduces the approach of modeling heterogeneity or local material structure within continuum models, such as the Stochastic Finite Element Method (SFEM). A fundamental challenge lies in accurately measuring these local material properties. Experimental investigations are being conducted to numerically simulate mechanical behavior. An overview is provided of experimental methods for material characterization, as found in literature, with a specific focus on measuring local mechanical material structure. By doing so, it enables the characterization of the global material structure and mechanical behavior of paper and paperboard. [ABSTRACT FROM AUTHOR]

Circuit simulators are fundamentally used for solving electric circuit problems with different degrees of complexity in which node voltages and branch currents are the unknowns. This is fully understandable since they were originally created for this specific task. However, behind the curtains, powerful simulation engines based on a variety of numerical techniques operate so as to always comply with Kirchhoff's current and voltage laws. In this paper, it is shown how a simple circuital configuration, referred to as the elementary solver, consistent in two behavioral current sources in series, can be used to solve mathematical problems that go beyond electronics. Of course, the intention is not to substitute mathematical packages with well-proven calculus capacity but to increase the scope of circuit simulators for their application in other areas of research or simply for educational purposes. It is worth mentioning that no special programming skills are required (except a basic knowledge of the available tools and language) and, furthermore, that the user can operate exclusively in a graphical environment. It is shown, throughout a series of selected examples, how the method of elementary solvers (MES) works, providing a new and practical dimension to the applicability of circuit simulators. [ABSTRACT FROM AUTHOR]

This work used a unified dynamic model of microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) that were combined to create MFC-powered MECs. This allowed for a completely sustainable and green system of hydrogen production and wastewater treatment. The main advantage that was envisioned for this coupled system was the elimination of the need for an external power source; the only feed to the system was the wastewater itself. We present the results of parameter estimation and model validation. The results showed the importance of minimizing internal resistance to increase hydrogen production. Better powering of MECs was achieved by setting MFCs in series rather than in parallel.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

The potential of paper and paperboard as fiber-based materials capable of replacing conventional polymer-based materials has been widely investigated and evaluated. Due to paper’s limited extensibility and inherent heterogeneity, local structural variations lead to unpredictable local mechanical behavior and instability during processing, such as mechanical forming. To gain a deeper understanding of the impact of mechanical behavior and heterogeneity on the paper forming process, the Finite Element Method (FEM) coupled with continuum modeling is being explored as a potential approach to enhance comprehension. To achieve this goal, utilizing experimentally derived material parameters alongside stochastic finite element methods allows for more precise modeling of material behavior, considering the local material properties. This work first introduces the approach of modeling heterogeneity or local material structure within continuum models, such as the Stochastic Finite Element Method (SFEM). A fundamental challenge lies in accurately measuring these local material properties. Experimental investigations are being conducted to numerically simulate mechanical behavior. An overview is provided of experimental methods for material characterization, as found in literature, with a specific focus on measuring local mechanical material structure. By doing so, it enables the characterization of the global material structure and mechanical behavior of paper and paperboard.

The paper shows the results of computer modeling of the polymer injection molding process. The influence of the gating system design on the warpage and volumetric shrinkage of the molded part was investigated. Particular attention was paid to the location of the gate. It has been proven that the selection of the place where the material is brought to the molding cavity has a significant impact on the final shape and quality of the molded part. The degree of compliance of its shape with the assumptions and dimensional accuracy was adopted as the criteria for assessing the quality of the molded part. The specialized software package Autodesk Moldflow Insight was used for numerical calculations. The obtained modeling results were related to the design recommendations of gating systems known from the literature.

Low-Speed Pre-Ignition (LSPI) is a destructive combustion event associated primarily with new, ultra-efficient, downsized gasoline engines, which provide efficiency benefits in general operation. Biofuels, specifically bio-gasoline, are an alternative fuel that attempts to reduce the harmful emissions output by modern Internal Combustion Engines (ICEs). This study attempts to understand the effect of biofuel use on LSPI, through the use of a numerical simulation tool developed in Ricardo Wave. Development of the tool includes the integration of RFlame, an extension capable of modeling autoignition within a 1D domain. Use of the tool highlights the impact of five ethanol blends, E10, E20, E30, E50 and E85, with clear impacts on both the severity and frequency of LSPI events correlated with chemical properties, such as the enthalpy of vaporization (HoV) and octane number. E30 is highlighted as the critical blend for LSPI severity, with both increased severity and intensity seen with a 30% concentration, and a greater sensitivity to effects such as the start of ignition (SOI). Higher-concentration biofuels, such as E50 and E85 bio-gasoline, show much more favorable behaviors, such as a vast reduction in end-gas knock events, but are limited in their use due to their deployment being both cost-prohibitive and potentially damaging in current hardware. Future work on this topic will surround the further development of the simulation tool to integrate 3D solving elements, understand the role of fluid interactions in LSPI, and study optimal fuel characteristics for future use in ICEs.

This study addresses the need for a more environmentally friendly and sustainable alternative to hexane, the conventional solvent for oil extraction. Hexane, while efficient in dissolving oil, poses neurotoxicity risks and stems from nonrenewable sources. In contrast, ethanol, produced via biotechnological methods, offers a promising alternative due to its minimal environmental impact, cost-effectiveness, and safety profile. The objective of this work is to compare the performance of hexane and ethanol in the extraction of soybean oil, employing simulation techniques rather than traditional laboratory experiments. The research develops a mathematical model for a countercurrent multistage solid–liquid extraction process, specifically tailored for soybean oil extraction, and simulates the process using MATLAB/SIMULINK. The results reveal that hexane exhibits a higher initial extraction rate, especially over a 90-min simulation period, though ethanol demonstrates comparable efficiency. Moreover, ethanol consistently showcases higher extraction efficiency relative to hexane when considering solvent-to-solid mass ratios. In conclusion, both hexane and ethanol extraction prove practical with ethanol holding advantages in terms of safety and extraction efficiency. These simulation findings serve as a valuable foundation for subsequent laboratory experiments which can contribute to the validation and refinement of the simulation model. [ABSTRACT FROM AUTHOR]

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives. [ABSTRACT FROM AUTHOR]

Hemodialysis treatment in specialized clinics within the same hospital significantly impacts environmental water health due to contaminated wastewater. The issues observed included changes in electrical conductivity, the presence of dangerous bacterial loads, toxicity from heavy metals, total cyanide content, and helminth parasite eggs. The level of damage is dependent on the patient's health under treatment. This research will use a modular system that employs electrocoagulation and electro‐oxidation processes at the laboratory and pilot levels to treat hemodialysis wastewater using synthetically prepared and real samples extracted from local clinics. The results showed that these hybrid systems improved various physicochemical parameters. Specifically, decreases in electrical conductivity of 49 %, total suspended solids of 27–100 %, chemical oxygen demand of 49 %, biochemical oxygen demand of 49 %, and cation and anion loading were observed at 96–100 % and pH 8.13 UpH in accordance with the established standards. With these results and the experimental conditions used, the proposed treatment system was modeled using the GPS‐X program, and it was concluded that the modular system used and the electrocoagulation/electro‐oxidation/activated carbon configuration is suitable for treating wastewater from hemodialysis and that scaling up this process to facilities that have dialysate machines more advanced than those considered in this work is possible. [ABSTRACT FROM AUTHOR]

A comprehensive approach to creating a control system for the shape of the radio-reflecting surface of a large-size space reflector is considered. Large-aperture space antennas are used for satellite communications and space exploration. The design of these structures is one of the most promising areas. Ensuring the specified characteristics of the reflector is directly related to maintaining the specified shape of the radio-reflective netting. Developing a control system for the shape of the active antenna surface is an urgent task. Optimizing this process makes it possible to accomplish the mission during the operational period effectively. The paper evaluates the effects of perturbations on a large-scale structure operating in outer space. A method for adjusting the geometry of the frontal network is proposed. This allows for improved communication between spacecraft and ground stations, which is very important for the safety of space flights. The conductive parts of the reflector are considered to transmit energy and control signals to the actuators. The application results of various control algorithms of the executive device making it possible to correct the netting considering the minimization of energy costs and oscillations of the structure are shown. According to the simulation results, the optimal algorithm of the target criteria hierarchy showed greater stability in terms of convergence in the presence of measurement noise and disturbances, which allows it to be applied in real time. This algorithm is supposed to be used in the future when creating a prototype of the system for controlling the shape of the radio-reflective surface of a large-size space reflector using its current-conducting structural parts. • The design of a system for setting up and maintaining a radio-reflective network fabric has been developed. • Algorithms are developed to minimize energy when setting up a radio-reflective mesh. • The optimal algorithm of the control allows solving the problem in real time. • The method for transmitting energy and control signals through the use of conductive structural parts of the reflector is proposed. [ABSTRACT FROM AUTHOR]

For pharmacists to fulfil their roles in the provision of pharmaceutical healthcare, it is necessary to ensure the effective organization of the work of individual healthcare professionals. We performed measurements at a selected pharmacy, which, among other things, allowed us to determine the time between people arriving and the average number of pharmaceuticals purchased. Using the available measurement data, we created a queueing system that can simulate the operation of a pharmacy. The system can save time for other pharmacists to fully devote themselves to other activities related to the provision of pharmaceutical healthcare, such as consultation, preparation of medicines in the laboratory, back-checking of prescriptions, or stock control. [ABSTRACT FROM AUTHOR]

Low-Speed Pre-Ignition (LSPI) is a destructive combustion event associated primarily with new, ultra-efficient, downsized gasoline engines, which provide efficiency benefits in general operation. Biofuels, specifically bio-gasoline, are an alternative fuel that attempts to reduce the harmful emissions output by modern Internal Combustion Engines (ICEs). This study attempts to understand the effect of biofuel use on LSPI, through the use of a numerical simulation tool developed in Ricardo Wave. Development of the tool includes the integration of RFlame, an extension capable of modeling autoignition within a 1D domain. Use of the tool highlights the impact of five ethanol blends, E10, E20, E30, E50 and E85, with clear impacts on both the severity and frequency of LSPI events correlated with chemical properties, such as the enthalpy of vaporization (HoV) and octane number. E30 is highlighted as the critical blend for LSPI severity, with both increased severity and intensity seen with a 30% concentration, and a greater sensitivity to effects such as the start of ignition (SOI). Higher-concentration biofuels, such as E50 and E85 bio-gasoline, show much more favorable behaviors, such as a vast reduction in end-gas knock events, but are limited in their use due to their deployment being both cost-prohibitive and potentially damaging in current hardware. Future work on this topic will surround the further development of the simulation tool to integrate 3D solving elements, understand the role of fluid interactions in LSPI, and study optimal fuel characteristics for future use in ICEs. [ABSTRACT FROM AUTHOR]

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives. [ABSTRACT FROM AUTHOR]

Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants' well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study. [ABSTRACT FROM AUTHOR]

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Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor's Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future. [ABSTRACT FROM AUTHOR]

Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become more vulnerable to hazards including erosion, floods, landslides and debris flows. In the present study, the well-known 2017 wildfire and subsequent 2018 debris flows in the Montecito area of California are examined. It is found that the post-wildfire debris flows were initiated from erosion and entrainment processes and triggered by intense rainfall. The significant debris deposition in four major creeks in this area is investigated. Numerical modeling of the post-wildfire debris flows is performed by employing a multi-phase mass flow model to simulate the growth in the debris flows and eventual debris deposition. The debris-flow-affected areas estimated from the numerical simulations fairly represent those observed in the field. Overall, the simulated debris deposits are within 7% error of those estimated based on field observations. A similar simulation of the pre-wildfire scenario indicates that the debris would be much less significant. The present study shows that proper numerical simulations can be a promising tool for estimating post-wildfire erosion and the debris-affected areas for hazard assessment and mitigation. [ABSTRACT FROM AUTHOR]

Supercritical drying is the only effective method for producing aerogels. Aerogels are innovative materials that found various applications in the range of industries. At the same time, supercritical drying is a technologically complex and expensive process, and an important goal is to optimize the production process to reduce costs and energy consumption. In this paper, the following aspects are considered: Investigation of the influence of parameters on the supercritical drying of silica aerogels on a laboratory scale; Modeling and simulation of the supercritical drying process; Developing a methodology for calculating the cost of manufacturing of silica aerogels; Creating a software module for calculating the cost of manufacturing of silica aerogels and performing techno-economic optimization of the supercritical drying process; Techno-economical optimization of the supercritical drying of silica aerogels on a pilot scale. With the aid of the developed software, techno-economic optimization of the pilot plant of silica aerogels was carried out. The developed mathematical model, optimization approach, and studies of the intensification of the supercritical drying process make it possible to reduce the cost of manufacturing in the existing production of aerogels by 26.4%. [ABSTRACT FROM AUTHOR]

The paper shows the results of computer modeling of the polymer injection molding process. The influence of the gating system design on the warpage and volumetric shrinkage of the molded part was investigated. Particular attention was paid to the location of the gate. It has been proven that the selection of the place where the material is brought to the molding cavity has a significant impact on the final shape and quality of the molded part. The degree of compliance of its shape with the assumptions and dimensional accuracy was adopted as the criteria for assessing the quality of the molded part. The specialized software package Autodesk Moldflow Insight was used for numerical calculations. The obtained modeling results were related to the design recommendations of gating systems known from the literature. [ABSTRACT FROM AUTHOR]

Research over the last 70 years has led to a better understanding of fixed-abrasive machining processes. This knowledge is often expressed in the form of physical and empirical models that cover forces, power, specific energy, wheel/workpiece topography, wear, thermal aspects, cooling, dressing, and more. This paper first examines the established models that continue to constitute the fundamental knowledge base in fixed-abrasive technology. Special attention is given to geometry, kinematics, and thermomechanical modeling. Recent advances in process monitoring and big data analytics provide new opportunities to further strengthen the state of the art in modeling through data-driven approaches. In addition, examples on how models – implemented in simulation software – can be used to predict and optimize industrial operations have been demonstrated. This is illustrated by several use cases from real production, including bearing, creep-feed form, gear, camshaft, crankshaft, and centerless grinding, along with diamond-wheel truing. [ABSTRACT FROM AUTHOR]

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers. [ABSTRACT FROM AUTHOR]

The vehicle's windshield plays an important role in the safety and comfort of drivers and passengers, so the windshield is solicited to many loads, such as the deformation of the vehicle body, the wind force, the crash between vehicles and vehicles, and a pedestrian. The primary goal of this study is to examine the mechanical and structural design of laminated glass for windshields in the scenario of an adult dummy head impact. The head impactor is represented by a spherical form covered in a rubber hull. The pedestrian head's Finite Element Method (FEM) is created per the requirements of Global Technical Regulations (GTRs). It weighs approximately 4 kg and strikes a windshield at a speed of 11.11 mm/ms. Furthermore, the windshield model is designed with two layers of glass with a layer of Polyvinyl Butyral (PVB). Our study's windshield Finite Element model is modeled with shell elements. The mathematical models of the PVB are described in a systematic manner. The model accurately represents the windshield's behavior, with critical fracture stress occurring in the impacted zone and maximum linear acceleration of the dummy head. The obtained results showed good agreement in energy absorption and maximum stress due to the impact. [ABSTRACT FROM AUTHOR]