Simulation Module Research Articles

Experimental and simulation study on a zeotropic ORC system using R1234ze(E)/R245fa as working fluid

Organic Rankine Cycle (ORC) is a highly promising technology for converting heat to power, which has a widespread application in renewable energy utilization and waste heat recovery. Zeotropic working fluids are widely investigated to enhance the performance of ORC. In the present study, an experimental study on zeotropic mixture R1234ze(E)/R245fa is conducted under specified operating conditions and the operating behaviors of the ORC system are evaluated. An ORC simulation model (BP-ORC) based on a back propagation neural network and heat exchanger simulation modules is developed. The contribution factors that trigger the differences in heat transfer and system performance among the various working fluids are investigated. The operating behaviors of the heat exchangers and system are simulated for the working conditions beyond the experimental study. R1234ze(E)/R245fa (0.25:0.75 and 0.50:0.50) features a wider operating range of mass flowrate than R245fa due to their relatively lower density. The net power output of R1234ze(E)/R245fa (0.50:0.50) is 4.63 % higher than that of R245fa at a mass flow rate of 0.164 kg/s due to its relatively higher heat transfer performance. The variation of density and operating pressure plays a significant role in influencing the heat transfer behaviors and system performances.

The Mechanism of The Arithmetic Logic Unit

The Arithmetic Logic Unit is widely used in electrical components nowadays such as the CPU in computers. The traditional research is based on the theory of a combination of multiple logic units, the results are not performed intuitional. This paper provides an introduction to the Arithmetic Logic Unit and its mechanisms, which consist of multiple logic gates to perform binary operations and send commands to the computer. The article discusses binary operations using logic gates, starting from the simplest one-bit half-adder to the more complex four-bit ALU, encompassing functions like addition, subtraction, division, and multiplication. The simulation module is used to justify the mechanism of the ALU. This research provides a new method in the process of developing the ALU, may largely increases the productivity. The approach may be used in the research and development of computer performance, providing visual, instant feedback and reducing the cost.

Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions

Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce overly optimistic results, rendering current solution evaluations inadequate for real-world environments. This paper proposes a framework based on the simulation of real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The simulation module uses techniques and queueing theory to replicate a real-world message stream. The processing module refines the input messages for optimal classification. The classification module categorises the generated message stream using existing solutions. The evaluation module evaluates the performance of the classification solutions by measuring accuracy, precision and recall. The framework can model different behaviours from different sources, such as different spammers with different attack strategies, press media or social network sources. Each profile generates a message stream that is combined into the main stream for greater realism. A spam detection case study is developed that demonstrates the implementation of the proposed framework and identifies latency and message body obfuscation as critical classification quality parameters.

Study on Urea Crystallization Risk Assessment and Influencing Factors in After-Treatment System of Diesel Engines

In order to meet the increasing pollutants discharge standard, the selective catalytic reduction (SCR) module in the diesel engine after-treatment system is an important means to reduce nitrogen oxide (NOx) emissions. SCR systems are prone to urea crystallization at lower temperatures, especially during the cold-start conditions of diesel engines. In this study, we use the diesel engine after-treatment system test bench to obtain the boundary parameter of the simulation modules, and the urea crystallization risk assessment model of the diesel SCR system is established. Comparing the computational fluid dynamics (CFD) results with the test bench results, it is shown that the predicted urea film distribution of the assessment model is in good agreement with the experimental results. In order to clarify the various factors that affect the urea crystallization risk, this paper conducts a simulation analysis on a nozzle and mixer structure and operating parameters. The CFD results indicate that the increase in urea spray time will increase the maximum urea film thickness on the SCR system mixer surface. Exhaust temperature is the most important influencing factor. When the diesel engine exhaust temperature increases from 190 °C to 300 °C, the maximum urea film thickness decreases by 32 and the urea film mass accumulation decreases by 5%. Exhaust flow has a small impact on urea crystallization risk. When the exhaust flow increases from 300 kg/h to 600 kg/h, the maximum urea film thickness decreases by 39% and the urea film mass accumulation decreases by about 1%. In addition, urea spray rate, nozzle numbers, spray angle, and spray cone angle are also factors that affect urea crystallization risk.

Evaluating factors that affect full- and half-sibling inferences via genetic pedigree reconstruction without parental genotypes

Close-kin mark-recapture (CKMR) is a challenging but exciting new approach to study dispersal because sampled and genotyped individuals can be used to “tag” full- and half-sibling family members without using parental genotypes via sibship reconstruction. CKMR increases sampling efficiency and, as a result, has enabled abundance estimation of migratory species and improved understanding of dispersal for species that are difficult to sample using traditional methods. Accordingly, there is interest in using CKMR to study dispersal ecology for the invasive sea lamprey in the Great Lakes, yet much is unknown about this new approach. We investigated the performance of a Rapture panel for sea lamprey reconstruction pedigrees without parental genotypes using two programs, Colony and Sequoia. Initially, datasets were created in the simulation module of Colony, where the number of parents (250, 500, 1000), number of offspring (250 or 500), and the number of loci (400, 600, 800) varied totaling 50 datasets for each of the 18 parameter combinations. Colony and Sequoia were then used to reconstruct the pedigrees without parental genotypes. Performance metrics were compared across parameter space and between sibship reconstruction programs. Colony and Sequoia inferred full-sibling and unrelated individuals with low assignment error. Sequoia was much faster at reconstructing pedigrees (<5 min compared to ∼ 1 week); however, the program nearly always (>0.99) misassigned known half-siblings as unrelated, which is an issue because sea lamprey likely produce many half-siblings. Accordingly, Colony should be used to reconstruct sea lamprey pedigrees when only offspring genotypes are provided.

Study on the hydrodynamics of the helix with non-uniform helical radius in viscoelastic fluid at low Reynolds number

Bacteria exhibit swimming behavior through the rotation of helical flagellar filaments, a common mode of propulsion for artificial micro-swimmers as well. However, most studies have focused on helical filaments with a uniform radius, leaving limited research on filaments with nonuniform radii. This paper presents a simulation of the hydrodynamics of a rotating helical filament with a nonuniform radius. The degree of nonuniformity is characterized by a radius ratio,β. Using the OpenFOAM platform, a simulation module for non-Newtonian Stokes flow was developed and integrated. The kinematics of the filament were predefined by fixing either the pitch or the helical angle, in addition to the helical radius. Thrust acting on the filament were analyzed with regards to the helical radius ratio, rotation speed, and fluid viscoelasticity. Certain modifications were made to apply the resistive force theory (RFT) to filaments with nonuniform helical radii. Results revealed that when the pitch was fixed, the thrust of the helix varied non-monotonically with the radius ratio, with excessive ratios leading to increased resistance. In contrast, helices with fixed helical angles exhibited an increasing thrust with a higher radius ratio. The thrust was greater in Newtonian fluids compared to non-Newtonian fluids, and the propulsion of the helix was influenced by the Deborah number. As the Deborah number approached zero, the thrust of viscoelastic swimming speed approached that of viscous swimming speed. For small, β(,β=1 and 4), the ratio of the thrust in viscoelastic fluid to the thrust in viscous fluid was always less than unity. At, β = 9, elasticity hindered propulsion at low De (De<1.5), while the opposite was observed at high De (De>1.5). A new approach was proposed to evaluate the impact of viscoelasticity on the helix, it was expected that the new calculation method can reduce the computational effort effectively.

Benefit of enhanced electrostatic and optical accelerometry for future gravimetry missions

Twenty years of gravity observations from various satellite missions have provided unique data about mass redistribution processes in the Earth system, such as melting of Greenland’s ice shields, sea level changes, ground and underground water depletion, droughts, floods, etc. The ongoing climate change underlines the urgent need to continue this kind of observations with future gravimetry missions using enhanced concepts and sensors. This paper studies the benefit of enhanced electrostatic and novel optical accelerometers and gradiometers for future gravimetry missions.One of the limiting factors in the current space gravimetry missions is the drift of the Electrostatic Accelerometers (EA) which dominates the error contribution at low frequencies (<1mHz). This study focuses on the modeling of enhanced EAs with laser-interferometric readout, so-called optical accelerometers, and on evaluating their performance for gravity field recovery in future satellite missions.In this paper, we simulate gravimetry missions in multiple scopes, applying various software modules for satellite dynamics integration, accelerometer (ACC) and gradiometer simulation and gravity field recovery. The total noise budgets of the modeled enhanced electrostatic and optical ACCs show a similar sensitivity as the ACC concepts from other research groups. Parametrization w.r.t. the weight of the test mass (TM) of ACCs and the gap between the TM and the surrounding electrode housing confirmed the fact known from previous results that an ACC with a heavier TM and a larger gap will perform better. Our results suggest that the anticipated gain of novel ACCs might at some point be potentially limited by noise from the inter-satellite laser ranging interferometry.In order to present the advantage of the novel sensors, time-variable background models and associated aliasing errors were not considered in our simulations. The utilization of enhanced EAs and optical ACCs shows a significant improvement of accuracy compared to the currently used GRACE-like EA. In addition, their benefit in double satellite pairs in a so-called Bender constellation as well as in the combination of low-low satellite-to-satellite tracking with cross-track gradiometry has been investigated.

Challenges and advantages of cut solar cells for shingling and half-cell modules

Cutting silicon solar cells from their host wafer into smaller cells reduces the output current per cut cell and therefore allows for reduced ohmic losses in series interconnection at module level. This comes with a trade-off of unpassivated cutting edges, which result in power losses. This performance drop can be seen in fill factor FF and open-circuit voltage VOC losses on cut cell level. Based on experimental realization of different solar cell layouts on the same industrial blue wafers (solar cell precursors), a combined simulation method to predict the performance on module level is demonstrated. This method uses Gridmaster+ for cell simulation and SmartCalc. Module for module simulation. The accuracy of the simulations is demonstrated by comparing with experimental results both from host and cut cell level. In addition, significant influence of the current–voltage (I–V) measurement configuration is demonstrated, mainly affecting FF. Using flexible methods like GridTOUCH® for I–V testing gives fast results but can also lead to overestimation of the host cell performance, resulting in overestimated cell-to-module losses or unreasonable comparisons between hosts and cut cells. It is also demonstrated that application of the passivated edge technology (PET) yields I–V characteristics close to those of cells with ideal edges, i.e., without edge recombination. The implications on the module efficiency are also compared between modules built using cells with and without edge passivation, giving the highest efficiency for a shingled module with PET.

A large-scale hybrid micro- and mesoscopic simulation approach for railway operation

In this study, the existing agent-based simulation framework MATSim is extended towards a module for the simulation of trains and their interactions with the infrastructure. The proposed railway simulation module railsim offers several advantages over existing railway planning tools: railsim is open-source, easily adaptable, allows for a microscopic, mesoscopic or hybrid micro-mesoscopic simulation setup and is fully integrated in an existing simulation framework. Trains have a spatial dimension along several infrastructure segments. Trains accelerate and decelerate based on vehicle- and infrastructure-specific attributes as well as the available capacity ahead of the vehicle. The interaction of vehicles as well as the modeling of the infrastructure is adapted to the railway system. Trains are enabled to reserve infrastructure elements (fixed block segments, moving blocks), and reservation requests interact with each other. The illustrative Switzerland case study reveals that an overall unique capacity of 3 trains per section and 10 trains per station ensures most trains reach their destinations successfully, while lower capacities lead to insufficient infrastructure and train disruptions.

MARLens: Understanding Multi-Agent Reinforcement Learning for Traffic Signal Control Via Visual Analytics.

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation. Moreover, effective TSC necessitates coordinated actions across multiple intersections. Existing visual analysis solutions fall short when applied in multi-agent settings. In this study, we delve into the challenge of interpretability in multi-agent reinforcement learning (MARL), particularly within the context of TSC. We propose MARLens, a visual analytics system tailored to understand MARL-based TSC. Our system serves as a versatile platform for both RL and TSC researchers. It empowers them to explore the model's features from various perspectives, revealing its decision-making processes and shedding light on interactions among different agents. To facilitate quick identification of critical states, we have devised multiple visualization views, complemented by a traffic simulation module that allows users to replay specific training scenarios. To validate the utility of our proposed system, we present three comprehensive case studies, incorporate insights from domain experts through interviews, and conduct a user study. These collective efforts underscore the feasibility and effectiveness of MARLens in enhancing our understanding of MARL-based TSC systems and pave the way for more informed and efficient traffic management strategies.

Numerical analysis of single SC-50-800 and SC-50-500x2-x4 group centrifugal cyclones: efficiency comparison

Centrifugal cyclones are important devices used to separate solids or liquids from gas streams using centrifugal forces. They are widely used in various industrial processes and air and gas purification systems. In this work, numerical modeling and study of the efficiency of cyclones of various configurations are carried out: single cyclone SC-50-800, group cyclones SC-50-500x2 and SC-50-400x4. To conduct the study, the engineering software package Comsol Multiphysics 6.1 was used with the addition of the Flow Simulation module, specialized for modeling hydrodynamic processes. To simulate turbulent flows, the v2-f model was chosen, which is often used in the case of swirling flows. The computational experiment was carried out at a constant air flow rate of 4500 m3/h. Based on the obtained numerical data, an analysis of the effectiveness of various cyclone configurations was carried out. The results of the study show that the operating efficiency of group cyclones significantly exceeds the efficiency of single cyclones. This is confirmed by analysis of separation parameters and comparison of performance indicators of various cyclone configurations. The data obtained can be useful for the design and optimization of gas stream purification systems in various industrial and environmental applications.

Sequential adaptive e-learning and hands-on simulator modules for the unilateral biportal endoscopic (UBE) technique - results from an EANS Young Neurosurgeons training course

Sequential adaptive e-learning and hands-on simulator modules for the unilateral biportal endoscopic (UBE) technique - results from an EANS Young Neurosurgeons training course

A product performance rapid simulation approach driven by digital twin data: Part 1. For variable product structures

In the product design stage, the effective performance analysis method is the premise to guarantee the application performance of products. However, digital simulation, as a widely used performance analysis method, has a long simulation cycle and high requirements for computer hardware performance, which leads to long product design and iteration time. Facing this problem, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures is proposed to replace the digital simulation method. Firstly, the framework for variable product structures is designed, which includes a digital simulation module, a data-driven module, and a surrogate-based simulation module. Then, module implementation of the proposed framework is introduced in detail, in which the twin data-driven product performance rapid simulation model (RSM) for variable product structures is studied based on the makeTwin reference architecture. Finally, a case study is conducted on the iterative design of a connecting rod, focusing on equivalent stress evaluation. The result shows that the proposed method is feasible, as well as costs a shorter simulation time than traditional digital simulation.

Development of Integrated Simulation Module for Schizophrenia Patients with Hypertensive Crisis: Pilot Test

Development of Integrated Simulation Module for Schizophrenia Patients with Hypertensive Crisis: Pilot Test

Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.

Integrated Agent-Based Simulation and Game Theory Decision Support Framework for Cash Flow and Payment Management in Construction Projects

Effective cash flow management has become crucial for projects and stakeholders given the wide payment-related problems and financial risks encountered in the construction industry worldwide. Previous studies mostly addressed cash flow and payments from the perspective of a specific stakeholder, resulting in an imbalanced cash flow management culture that is further intensified by the power asymmetry of the top-down payment decision-making process. This research proposes an adaptive decision support framework for evaluating and negotiating payment options in construction projects while incorporating the individual and collective financial roles of stakeholders. The framework is comprised of three modules for data acquisition, payment simulation, analysis, and negotiation, as well as decision support. It integrates agent-based simulation, data envelopment analysis, and game theory for a multi-level study of project performance while capturing the driving forces of stakeholders in payment negotiations. A case study project is used to demonstrate the framework implementation under varying payment conditions and interest rates. The results provide quantitative profiles of stakeholders to identify incurred charges, balanced payment conditions, and suitable compensation. Finally, the framework can be utilized by stakeholders and jurisdictions to move towards enhanced contractual arrangements that alleviate economic and financial risks with the informed collaboration of its entities.

A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers.

Geant4 implementation of axial channeling radiation in classical description

The progress in developing a C++ module for simulation of channeling radiation in Geant4 using classical description is being discussed. The created module is built into Geant4 as a discrete set of physical processes, which allows combining it with other physical processes of Geant4. The direct simulation of CR in Geant4 allows describing devices such as, e. g. positron sources, without using external programs for CR calculation. In previous version, the module was capable to simulate only planar channeling radiation. In this paper, the implementation of axial channeling radiation in Geant4 is shown. The module now simulates both planar and axial channeling radiation from ultra-relativistic electrons channeled in Si, C, Ge and W single crystals. The simulation results are compared with available experimental data.

Creation of A Mixed-Reality Simulator Professional Development Module to Attend to the Unique Needs of International Teaching Assistants in Active Learning Classrooms

International teaching assistants (ITAs) bring cultural perspectives and language diversity to university programs. However, ITAs can be underserved through traditional professional development initiatives. A mixed-reality teaching simulator module was created to attend to the unique needs of chemistry ITAs and prepare them for active learning instruction. To create the module, seven ITAs were interviewed about their professional development needs. ITAs most commonly wanted to practice asking and answering student questions, engaging disengaged or disrespectful students, and navigating language difficulties with students. The mixed-reality simulated teaching module provided opportunities for ITAs to rehearse one or more of the three focus areas. Three ITAs tested the simulator and reported that using the simulator for professional development with cultural and linguistic layers would be beneficial both to existing and new ITAs. These research findings set the foundation for generating a transformative set of training materials that institutions can use to reduce teaching anxiety and increase the effectiveness of ITAs in the classroom, thus enhancing student learning through more effective active learning instruction.

Designing a Multidisciplinary Cardio-obstetrics Curriculum for General Cardiology and Obstetrics Residents: A National Survey of Educational Needs

BackgroundThe increasing and potentially preventable cardiac events in pregnant patients have led to calls to enhance multidisciplinary cardio-obstetrics education. To design a multidisciplinary cardio-obstetrics curriculum for general cardiology and obstetrics and gynecology (OBGYN) residents, we need to define educational needs from the perspectives of both cardiology and OBGYN residents. Our study characterizes the educational needs of Canadian cardiology and OBGYN residents. MethodsCanadian cardiology and OBGYN residents were surveyed on clinical exposures, perceived needs for topics, unperceived needs for topics (multiple-choice questions) and preferences for educational formats. High priorities were defined as ≥ 50% of responses indicating a perceived need or ≥ 50% indicating an unperceived need. ResultsA total of 154 residents participated (cardiology n = 44, OBGYN n = 110). Residents reported insufficient clinical exposure to nearly all cardiac disorders, with 33% of exposures occurring in multidisciplinary contexts. All topics aside from gestational hypertension were rated as high priority on perceived needs by both specialties. High-priority unperceived needs were congenital heart disease (both specialties), pre-existing acquired heart disease (both specialties), medication safety (OBGYN), peripartum management (OBGYN), and pregnancy-related heart disease (OBGYN). Cardiology and OBGYN residents shared preferences for in-person simulation, virtual simulation, and online modules. ConclusionsResidents in both specialties reported low clinical exposure to most cardiac disorders during pregnancy, identified high-priority perceived needs in multiple topics, and shared 2 high-priority unperceived needs. OBGYN residents identified 3 additional high-priority unperceived needs. These data can inform design of multidisciplinary cardio-obstetrics curricula for general cardiology and OBGYN residents.