Search

AbstractUsing cosmic muons allows for a noninvasive imaging approach to examine nuclear fuel in sealed dry storage casks. By assessing muons both before and after passing through the cask, one can infer details about the cask’s interior by analyzing scattering angle data. The effective scattering angles of muons depend on the characteristics of the interacting material, such as the atomic number (Z). This allows for the deduction of the material and geometric composition of the cask’s inventory. When employing simulations to forecast muon paths within the cask, it is essential to scrutinize the impact of modeling assumptions and simplifications on the scattering angle distribution.In this study, we examine the influence of modeling assumptions and simplifications on the effective scattering angle. Additionally, the significance of the number of particles used is shown. We evaluate four GEANT4 cask models of a CASTOR® V/19, each incorporating varying degrees of simplification, and analyze their impact on the projected muon scattering angle. These simplifications include both the simplification of individual geometric components of the cask and the complete exclusion of specific components. We assess and prioritize the various model simplifications in terms of their effect on the observed scattering angle. We recognize the importance of thoughtfully considering the degree of simplification used in the model to ensure accurate and reliable results for the scattering angle distribution. [ABSTRACT FROM AUTHOR]

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM. [ABSTRACT FROM AUTHOR]

In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building's air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings. [ABSTRACT FROM AUTHOR]

The accessibility of healthcare system is vulnerable to various types of hazards, where the failure of one system component may lead to a diffusion of the pressure and result in cascading failures. This study proposes a network-based simulation framework for robustness assessment of access to healthcare through integrating cascading failure mechanism. Weighted complex networks are constructed to model the accessible patient transfer under both general and elderly healthcare scenarios. The cascade failure mechanism is incorporated into the constructed networks, and several attack strategies (including random, initial degree (ID), initial betweenness (IB), recalculated degree (RD), and recalculated betweenness (RB) attack) are adopted to simulate the process of system robustness assessment. Results indicate that the proposed framework enables to discover the vulnerable nodes in the constructed healthcare accessibility networks, where the robustness metric combining network efficiency and relative size of the largest component acts as a benchmark; all the intentional attack strategies outperform the random attack strategy, which indicates the effectiveness of the detection of vulnerable healthcare facilities by the developed model; and the metrics of node degree and betweenness centrality make progress on identifying the vulnerable healthcare facility nodes, which should be taken heed of to optimize the management and operation of healthcare systems. [ABSTRACT FROM AUTHOR]

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

The spindle determines the performance of machine tools; hence, monitoring its health is essential to maintain machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location are correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyze the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III. [ABSTRACT FROM AUTHOR]

This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid-state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed AFSD-Nets includes a set of customized neural network approximators, which are used to model the coupled temperature evolution for the tool and build during multi-layer material deposition. Experiments are designed and performed using 7075 aluminum feedstock deposited on a substrate of the same material for 30 layers. A comparison of predictions and measurements shows that the proposed AFSD-Nets approach can accurately describe and predict the temperature evolution during the AFSD process. [ABSTRACT FROM AUTHOR]

This paper employed a scaling analysis to represent the processing parameters, affecting the melting process in the dimensionless numbers, identify the relationships of these dimensionless numbers, and develop semi-empirical correlations to predict the width and depth of the melt pool. To develop the correlations, Ti-6Al-4V powder was used to print 38 tracks at various processing conditions. The correlations were then fit into this experimental data using python code to find the constants of the correlations. The correlations were then used to predict the depth and width of the melt pools. It was found that the mean discrepancy between the predicted melt pool dimensions and the experiment is 7%. To evaluate the accuracy of the correlation in predicting the melt pool dimensions of the materials never used during the development of the correlations, the melt pool depth of some tracks made out of stainless steel 316L printed at various conditions was predicted using the model, and it was found that the mean discrepancy between the predicted melt pool depth and experiment is 11%. [ABSTRACT FROM AUTHOR]

The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network. [ABSTRACT FROM AUTHOR]

Cold spray additively manufactured (CSAM) aluminum 6061 components are characterized by heterogeneous compressive residual stresses induced during manufacturing. This heterogeneity is further compounded by spatial variations in microstructures and mechanical properties, leading to poor inter-particle (intersplat) bonding and significant marring of overall component performance. Thermal post-processing is a keenly researched method for recovering mechanical toughness by enhancing intersplat bonding and altering highly concentrated residual stress distributions. The current work incorporates a modified microscale–mesoscale material model into a viscoplastic self-consistent simulation framework to capture material response in the as-sprayed and post-processed states. The updated model incorporates physically informed parameters emphasizing residual stresses measured experimentally through X-ray diffraction. The model calibrated using experimental tests and published literature was able to predict the stress–strain response of CSAM parts at post-heat-treated conditions. Results of the parametric study showed the significance of intersplat boundary effects on the overall yield and strain hardening of the CSAM parts. Without any information on the processing conditions of CSAM parts, the modified plasticity model predicted the deformation response using information gathered from microstructure characterization. [ABSTRACT FROM AUTHOR]

This article presents the design, fabrication, and analysis of a textile-based band-stop frequency selective surface (FSS), in GSM, Wi-Fi, LTE, and WiMAX bands where the electromagnetic (EM) pollution is intense. The unit cell of the proposed FSS has been designed and simulated via a full-wave EM solver; CST Microwave Studio at the frequency of interest. In this study, embroidered textile-based FSSs were designed as an alternative to conventional FSSs. In the study, high textile detailed (from thread to embroidery direction) modeling was performed and the results of the modeling and experiments were compared. The results of this study showed that the embroidery direction has a dramatic effect on the electromagnetic behavior of the FSS. Both experimental and high textile detailed modeling results indicated that vertical or horizontal embroideries are not sufficient for band-stopping FSS. To overcome this problem, vertical-over-horizontal embroidery was used. The highly textile detailed model and its experimental results have proven that horizontal-over-vertical embroidery is successful in band-stopping FSS. [ABSTRACT FROM AUTHOR]

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations. [ABSTRACT FROM AUTHOR]

The electrical power distribution system (EPDS) is an essential component of the power system. Electricity and its services have risen enormously in the recent period, and the primary task of EPDS is to distribute uninterrupted electricity to the consumer. The EPDS comprises numerous intricate, unpredictable, and interlinked components that are frequently susceptible to disruption or malfunction. Faults on EPDS are intended to be appropriately recognized and categorized before being eliminated as quickly as feasible. An efficient fault recognition mechanism makes relaying operations realistic, fast, safe, and dependable. In this article, we introduce a MATLAB simulation model based on the Electrical Fault Detection and Classification (EFDC) algorithm for shunt fault analysis in the EPDS and distinguish among heterogeneous types of shunt fault based on current and voltage waveform throughout the fault. The recommended fault scrutiny and isolation framework might aid in segregating malfunctioning sections from healthy EPDS. Numerous electrical shunt faults are modeled and simulated to detect such deficient behaviors. The suggested EFDC algorithm’s efficacy, including Digital Comparative Fault Classifier (DCFC), is examined by simulating different kinds of shunt faults throughout ring-main EPDS’s source and load regions to evaluate the proposed technique’s adaptability, as well as the outcomes are promising. [ABSTRACT FROM AUTHOR]

This paper explores the optimization of critical parameters in the Czochralski (CZ) crystal growth process, a key technique in semiconductor device manufacturing. The CZ system involves the melting of high-purity polycrystalline silicon and controlled growth of a single-crystal ingot. This process relies on key variables such as the thermal shield thickness, shield gap size, cooling jacket length, crucible dimensions, and rotation speed. A computational model was applied to simulate heat transfer, fluid flow, and radiation heat exchange within the CZ system. The model was validated using experimental data, which demonstrated its precision in predicting crystal-front deflection and heater power. The results highlight the substantial impact of increasing the thermal shield thickness on the crystal-pulling speed and uniformity. Greater cooling jacket lengths increased the cooling rate, which increased the pulling speed, although more heater power was required. The shield gap size had negligible effects, and variations in the heater power ratio showed minimal impact. The optimal combination of parameters led to significantly improved crystal-pulling speed and reduced power consumption, making it a compelling choice for enhanced CZ crystal growth in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

In this study, the structural, electronic, and optical properties of pristine graphene and graphene doped/co-doped with (N, Si) atoms are examined using a first-principles investigation. However, pristine graphene is characterized by a unique electronic structure known as the zero band gap (Eg = 0 eV), and this gap was opened up after the addition of N and Si substitutions, where it became 0.2, 0.21, and 1.38 eV for graphene doped with nitrogen, silicon and double doped with both (N, Si), respectively. For the band gap and the density of states, many parameters have been studied such as the complex dielectric function, conductivity, absorption spectra, loss function, and refractive index. The absorption curve shows two sharp peaks for all structures, where their intensities become lower and shift slightly towards lower energy after doping graphene with N, Si, and N-Si, indicating that this doping introduces additional energy states in the graphene band structure, making the transition between states easier to achieve. [ABSTRACT FROM AUTHOR]

We examine the potential of prompting a large language model-based chatbot, ChatGPT, to generate functional simulation model code from a prose-based narrative. The simple narrative describes how the mode of transportation for elementary school students changed due to the COVID-19 pandemic and related factors, including a lack of available bus drivers, lack of mask enforcement on buses, and inclement weather. We document the process of providing this narrative to ChatGPT and further prompting the chatbot to generate model code to represent the narrative and to make it executable. We test ChatGPT's ability to use prose descriptions of a phenomenon to generate simulation models using three paradigms: discrete event system, system dynamics, and agent-based modeling. Our findings reveal that ChatGPT could not produce concise or executable models, facing higher difficulty when asked to do so in programming languages it was less familiar with. This analysis underscores the strengths and limitations of the current state of this technology for modeling and simulation. Furthermore, we propose how future advancements in Large Language Models may aid the simulation modeling process, increasing transparency and participation in multidisciplinary team efforts. [ABSTRACT FROM AUTHOR]

As digitalization is permeating all sectors of society toward the concept of "smart everything," and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives. [ABSTRACT FROM AUTHOR]

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene–propylene–diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney–Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C 1 (T) and C 2 T . The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress–strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry. [ABSTRACT FROM AUTHOR]

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines. [ABSTRACT FROM AUTHOR]

In the face of a changing climate and a rising number of "food deserts" in both rural and urban areas, there is a demand to supply fresh produce year-round to communities at the end of the traditional agriculture supply chain. Vertical indoor farming is a promising mode of next-generation agriculture that boasts reduced water and pesticide usage, improved yields, more consistent quality, year-round cultivation, and cheaper transportation and harvesting costs. Indoor farms can rival industrial greenhouses in size, but small-scale "pod farms" can be deployed to smaller communities and areas where large swaths of land are either unavailable or too costly. These pods are often the size of shipping containers with their temperature, humidity, and plant nutrient supply carefully controlled. Plants inside the pods are grown hydroponically with light supplied by panels of LEDs and, thus, this mode of farming is fundamentally different from greenhouse farming. Many indoor farming pods have recently become commercially available claiming high energy efficiency, but little analysis and optimization work has been done to prove these claims. To drive innovation in the design of these physical systems, we have developed a digital-twin and genomic optimization framework for the optical design of vertical indoor farming pods. We model a completely enclosed indoor farming pod with plants in the three mutually-orthogonal planes and illuminated by LED "walls." We employ ray-tracing methods and a genetic algorithm to determine the LED source tube area size, beam aperture spread, and power requirements for maximal power absorption by the plants. [ABSTRACT FROM AUTHOR]

Warm forming holds significant potential in shaping challenging materials due to its comprehensive advantages compared to cold and hot forming. Effective heating is crucial for precise warm forming processes. This study explored an innovative warm air bending technique employing an online local contact heating method. Heat transfer during the preheating stage and the air bending of AZ31B sheets were investigated through experiments and finite element analysis at temperatures ranging from room temperature (24 °C) to 250 °C, considering the thermomechanical coupling effect. The results indicate a significant increase in the bending center angle, advancing from 69.8 deg to more than 119 deg as the heating temperature rises from room temperature to 250 °C. Fractures were effectively eliminated when the sheet heating temperatures exceeded 150 °C. Simultaneously, as the localized heating temperature increases, a corresponding decrease in the rebound angle is observed, reducing from 10.6 deg to 2.3 deg. The findings validate the feasibility of warm air bending using online local contact heating in industrial applications, with regard to forming quality and production efficiency. [ABSTRACT FROM AUTHOR]

Additive and subtractive (Add/Sub) manufacturing processes are increasingly being combined to produce complex parts with unique geometries and properties. However, the design of such combined processes is often challenging as it requires a deep understanding of the interaction between the different processes. On the other hand, digital twin (DT) technology has become a powerful tool in recent years for optimizing manufacturing processes. This article explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The article describes the integration of laser metal deposition (LMD) and micro-milling prediction models of resulting geometry (width and height), hardness, and surface roughness into the digital twin. This is then used for combined process planning to achieve different target values regarding resulting geometry and surface roughness. For the planning of this combined process chain, further criteria such as tool life, energy, and process time are considered in the optimization, showing the potential for sustainable and efficient production. Sensorless cutting force estimation is also used to detect small cutting forces, with the potential to use this as a soft sensor for roughness estimation. The measured width, height, and roughness as a result of the process parameters suggested by the optimization algorithms showed a mean absolute percentage error (MAPE) of 4, 17, and 16%, respectively. [ABSTRACT FROM AUTHOR]

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models. [ABSTRACT FROM AUTHOR]

The development of reliable joining techniques for ceramics and metals is crucial for energy applications, such as fuel cells, nuclear reactors, and high-temperature sensors, most especially for the sealing of hydrothermal sensors to study multiphase flows. However, during one-step active laser brazing it is a serious problem that a high thermal stress concentration can occur at the joint interfaces or on the ceramic side of the joint due to mismatches between the CTEs (coefficients of thermal expansion) and/or elastic constants. The uncontrolled thermal residual stress can lead to cracks and defects in the brazement. In the present work, an elastoplastic finite element method/numerical model was formulated to study the thermal residual stresses developed in the brazement between ceramics and austenitic stainless steel during cooling in active laser brazing. Calculations and comparison experiments were conducted to validate the simulated stress distribution in un-patterned ceramics. Stress analyses were conducted for planar and cylindrical specimen geometries (lab joints) relevant for miniaturized energy sensors. Laser interface patterning was employed to create micro-scale features on ceramic interfaces that reduce thermal stress concentrations. The optimization of the interface designing parameters including hatch size, structure width, pattern depth and metal/ceramic thickness ratio was performed using the Taguchi method with orthogonal arrays. The study suggests that laser interface structuring can modify thermal residual stresses in ceramic-to-metal brazements, thereby increasing the reliability of active brazing joints. [ABSTRACT FROM AUTHOR]

The quenching and partitioning (Q&P) process has been shown to be quite promising in the development of a third generation of Advanced High-Strength Steels. As in other metallurgical processes, the austenitic grain size (AGS) is crucial to obtain a microstructure that provides a great mechanical behavior. Hence, this work evaluated the effect of the austenitizing temperature on the AGS and, consequently, on the constituents formed under continuous cooling at different rates. Then, a Q&P modeling for a commercial CMnSi steel, predicting the microstructural evolution and the final phase fractions as a function of the AGS and other parameters, was performed. The AGS increased exponentially with the austenitizing temperature, in the way that the smaller the AGS the finer the martensitic structure formed after cooling. The decrease in the continuous cooling rate leaded to the formation of diffusional constituents prior to the martensitic transformation, which promoted the increase in Ms temperature for larger AGS. In conclusion it is possible to state that an austenitizing condition that provides the smallest possible AGS, followed by an optimized Q&P cycle, has the potential to generate an advanced steel with a final microstructure assisted by the TRIP effect due to the retained austenite content. [ABSTRACT FROM AUTHOR]

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux's completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. [ABSTRACT FROM AUTHOR]

This paper investigates the model performance of an industrial box used to remove ammonia from the air using a doped activated carbon. To this end, process models are developed and experimental measurements are carried out. The models are based on momentum and mass balance equations along with thermodynamic, hydrodynamic and adsorption kinetic relations. The measurements consist of pressure drops in the air purification box and breakthrough curves of ammonia on activated carbon. The models are implemented and solved using COMSOL Multiphysics®. Adsorption is investigated by means of a Fickian multi‐scale model, which demonstrated to efficiently predict the experimental data. The optimal values of the most estimable unknown parameters involved in each model, as well as their confidence intervals are determined from experimental measurements. The performance of the industrial air purification model is rigorously assessed using statistical criteria, including mean‐square error (RMSE = 0.0013) and Pearson correlation coefficient (r = 0.999). [ABSTRACT FROM AUTHOR]

This study describes the design of a parallel spatial manipulator with four degrees of freedom actuated with shape memory alloy (SMA) wire to validate the use of SMA in complicated mechatronics systems. The manipulator has a closed kinematic structure, which includes a fixed base and a moving square platform (end effector). The four arms of the manipulator are SMA wires fastened between the fixed base and the end effector. SMA wire-based actuators replace bulky conventional revolute actuators. This work spotlights the development of an actuator model, dimensional analysis, and design of cascade control strategies of various PID and sliding mode controller in their integral and fractional order configurations. Experimental evaluation of the actuator is performed through trajectory tracking to quantify the different controller configurations. The experimental results indicate that the parallel manipulator associated with SMA wire actuators is the best alternative to conventional motion stages for highly precise micro-positioning and tracking applications in the fields of 3D printing, intricate surgical operations, the medical and pharmaceutical industries, and flight and gaming simulators. [ABSTRACT FROM AUTHOR]

Simulation continues to be highly attractive and indispensable for studying networked systems. There remains the need for simulators that can support efficient execution in large-scale, complex models and can offer a theoretical underpinning with model abstractions. A qualified and effective simulator is still hard to find in education and research. To obtain a better solution in education and research, a collection of the parallel DEVS-based computer network model components is presented in this article. The Discrete Event System Specification (DEVS) is a mathematical modeling formalism for describing (discrete and continuous) dynamical systems. Among discrete event modeling approaches, it is well suited for formally describing concurrent processing and the event-driven nature of arbitrary configuration of nodes and links forming network systems. The suggested system is developed by emphasizing the Open Shortest Path First (OSPF) protocol. These expressive models are realized in the DEVS-Suite simulator as DEVS-Suite provides a robust environment with rich support for designing experiments and visualization. Visualization representation of a typical routing protocol logic with routing tables and control packet provides better support for active learning, particularly for distance education in COVID-19 disease. The resulting OSPF-DEVS simulation environment offers a model abstraction well-suited to educational environments. An exemplary computer network model is developed and evaluated in both simulators. The simulation models have proved very helpful in understanding the basics of the computer network's principles, designs, routing protocols, etc. [ABSTRACT FROM AUTHOR]

Early damage in metal materials is primarily characterized by microdefects, such as microcracks and micropores. In this study, we developed a novel approach for quantitatively assessing the microdefect damage degree in dual-phase bainite–ferrite (B + F) X80 pipeline steel using ultrasound nonlinear parameters. A finite element analysis model with micron-scale three-dimensional ellipsoidal defects was established based on the Gurson–Tvergaard–Needleman material damage theory. The propagation process of Lamb waves in the model was simulated to investigate the interaction mechanism between microdefects and Lamb waves and explore the quantitative relationship between ultrasound nonlinearity and microdefect damage degree, which can be characterized by the size and number of microdefects. Furthermore, to validate the simulation findings, an ultrasound testing experiment was designed to evaluate the microdefect damage in (B + F) X80 pipeline steel. The results demonstrate that when Lamb waves propagate through microdefects, their amplitude is attenuated, resulting in changes in ultrasound nonlinear parameters. Within a specific range, the nonlinear coefficient of Lamb waves increases with an increase in microdefect length and number of microdefects but decreases with an increase in microdefect width. This finding holds significant value for evaluating damage degree and predicting the service life of X80 pipeline steel. [ABSTRACT FROM AUTHOR]

Lead-free potassium titanium halide (K2TiI6) perovskite is considered a substantial alternative for highly efficient perovskite solar cells. This research consists of a computational study of Pb-free K2TiI6-based perovskite solar cell using SCAPS 1D. Primarily, the TiO2 and PEDOT:PSS are considered as ETL and HTL exhibit a device efficiency of 5.70%. Further, the impact of various ETL and HTL is explored. The optimization is mainly focused on considering its built-in potential relation with the open circuit voltage and its device efficiency. It is obtained from the numerical simulation, that to circumvent the VOC loss, the CB minimum of the PVSK(CBmin_PVSK) layer should be higher than the CB minimum of the ETL (CBmin_ETL), and the CB minimum of the ETL (CBmin_ETL) should either align or lie above than the FTO contact. In order to have proper hole transport from the perovskite, the VB maximum of the perovskite (PVSK) layer should be lower than the VB maximum of HTL and should either align or lie below the back contact. It is obtained from the simulation that the optimized ETL and HTLs are found as PCBM and Sr2Cu2O2 improve the device efficiency. Further, the impact of perovskite thickness is also investigated, which enhances the device efficiency up to 23.75%. The proposed structure shows remarkable PV parameters VOC = 1.32 V, JSC = 22.30 mA/cm2, FF = 81.07%, and ƞ = 24.01% which indicates that the K2TiI6-based perovskite solar cell is considered a remarkable candidate for Pb-free perovskite solar cell development. [ABSTRACT FROM AUTHOR]

Cooperative multiagent reinforcement learning approaches are increasingly being used to make decisions in contested and dynamic environments, which tend to be wildly different from the environments used to train them. As such, there is a need for a more in-depth understanding of their resilience and robustness in conditions such as network partitions, node failures, or attacks. In this article, we propose a modeling and simulation framework that explores the resilience of four c-MARL models when faced with different types of attacks, and the impact that training with different perturbations has on the effectiveness of these attacks. We show that c-MARL approaches are highly vulnerable to perturbations of observation, action reward, and communication, showing more than 80% drop in the performance from the baseline. We also show that appropriate training with perturbations can dramatically improve performance in some cases, however, can also result in overfitting, making the models less resilient against other attacks. This is a first step toward a more in-depth understanding of the resilience c-MARL models and the effect that contested environments can have on their behavior and toward resilience of complex systems in general. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) enables new possibilities for the design and manufacturing of complex metal architectures. Incorporating lattice structures into complex part geometries can enhance strength-to-weight and surface area-to-volume ratios for valuable components, particularly in industries such as medical devices and aerospace. However, lattice structures and their interconnections may result in unsupported down-skin surfaces, potentially limiting their manufacturability by metal AM technologies, such as laser powder bed fusion (LPBF). This study aimed to examine the correlation between down-skin surface area and the manufacturability of lattice structures fabricated using LPBF. Image processing algorithms were used to analyze down-skin surface areas of seven unique lattice designs and to devise quantitative metrics (such as down-skin surface area, discrete surface count, surface interconnectivity, down-skin ratio, over-print/under-print volumes, etc.) to evaluate LPBF manufacturability. The seven lattice designs were subsequently manufactured using maraging steel via LPBF and then examined using imaging using X-ray micro-computed tomography (XCT). The geometric accuracy of the lattice designs was compared with XCT scans of the manufactured lattices by employing a voxel-based image comparison technique. The results indicated a strong relationship between down-skin surface area, surface interconnectivity, and the manufacturability of a given lattice design. The digital manufacturability evaluation workflow was also applied to a medical device design, further affirming its potential industrial utility for complex geometries. [ABSTRACT FROM AUTHOR]

The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian-Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work. [ABSTRACT FROM AUTHOR]

Modeling multitrack laser-directed energy deposition (LDED) is different from single-track deposition. There is a temporal variation in the deposition geometry and integrity in a multitrack deposition, which is not well understood. This article employs an analytical model for power attenuation and powder catchment in the melt pool in conjunction with a robust fully coupled metallurgical-thermomechanical finite element (FE) model iteratively to simulate the multitrack deposition. The novel hybrid analytical-numerical approach incorporates the effect of preexisting tracks on melt pool formation, powder catchment, geometry evolution, dilution, residual stress, and defect generation. CPM 9V steel powder was deposited on the H13 tool steel substrate for validating the model. The deposition height is found to be a function of the track sequence but reaches a steady-state height after a finite number of tracks. The height variation determines the waviness of the deposited surface and, therefore, the effective layer height. The inter-track spacing (I) plays a vital role in steady-state height evolution. A larger value of I facilitates faster convergence to the steady-state height but increases the surface waviness. The FE model incorporates the effects of differential thermal contraction, volume dilation, and transformation-induced plasticity. It predicts the deposition geometry and integrity as a function of inter-track spacing and powder feed rate. The insufficient remelting of the substrate or the preceding track can induce defects. A method to predict and mitigate these defects has also been presented in this article. [ABSTRACT FROM AUTHOR]

In many countries, motorcycles have become a primary and popular mode of transportation, driven by increasing demand due to their convenience. However, as fossil fuel sources deplete, there's a pressing need to enhance engine performance, efficiency, fuel economy, and reduce emissions. Improving ignition systems is crucial in achieving these goals. This study compares the performance of the Honda Future FI 125cc engine between a laser ignition system (LIS) and a conventional ignition system (CIS) using simulation. CATIA software was utilized to design the engine's intake manifold, ANSYS Fluent software for simulating and determining the optimal swirl and tumble ratio, and Matlab/Simulink for modeling and simulating engine performance with both LIS and CIS. Detailed discussions and comparisons were made on parameters such as cylinder air mass, ignition energy, engine power and torque, specific fuel consumption (SFC), and mass fraction burned (MFB) between LIS and CIS. Overall, LIS demonstrated superior engine performance compared to CIS. This finding is significant for evaluating the advantages of LIS in motorcycles, especially in the Honda Future FI 125cc engine. [ABSTRACT FROM AUTHOR]

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML). [ABSTRACT FROM AUTHOR]

Accurately characterizing the substrate used in anaerobic digestion is crucial for predicting the biogas plant's performance. This issue makes particularly challenging the application of modeling in codigestion plants. In this work, a novel methodology called substrate prediction module (SPM) has been developed and tested, using virtual codigestion data. The SPM aims to estimate the inlet properties of the substrate based on the reverse application of the anaerobic digestion model n1 (ADM1). The results show that, while the SPM can estimate some properties of the substrate based on certain output parameters, there are limitations in accurately determining all required variables. HIGHLIGHTS Extensive substrate characterization is challenging, especially for codigestion.; A reverse modeling approach is proposed to estimate unknown substrate properties.; The substrate prediction module processes measured AD data based on the ADM1.; Two data processing strategies are assessed: 7-day moving block and daily data.; The SPM could estimate some substrate properties but there are limitations.;

Abstract This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC.

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.