Simulation Model Research Articles

Discrete element model construction and dehulling simulation verification based on harvested quinoa grains.

The dry dehulling process of quinoa grains faces challenges such as poor visibility and unclear dynamic response characteristics, leading to irrational settings of the shelling process and high losses of the final processed product. Simulation methods provide an effective solution to this issue, with the accuracy of the simulation model being crucial. In this study, using the Experts in Discrete Element Modeling (EDEM) simulation software, based on the biological characteristics and mechanical properties of the grains, a method was proposed to construct a discrete-element simulation model for the double-layer bonding of the quinoa grain using the three-axis spatial coordinates method. The Plackett-Burman method, the steepest rise method and the Box-Behnken method were used to simulate the compression test. The bonding parameters of the quinoa grain were calibrated. The parameters were further validated by constructing a mechanical shear test. Finally, the process of the dehulling quinoa grain was simulated in a dry dehulling equipment. The results showed that the relative error between the simulated and actual maximum compression force of the quinoa grains was 0.32%. The breaking shear force in the simulation had a relative error of no more than 5% compared to the actual measurements. In the simulation, the dehulling rate and broken rice rate were 78.54% and 1.38%, respectively, which exceeded the bench test values (75.32% for dehulling rate and 1.24% for broken rice rate) by 3.22% and 0.14%, respectively. The error was within the acceptable range. The calibration of the discrete element model parameters for quinoa grains was accurate, reflecting the mechanical properties differences between the rice and the pericarp effectively, providing a reliable basis for optimizing the design of dry dehulling mechanisms.

Availability of five hydraulic damper models for dynamics simulation and application of two types of high-speed electrical multiple units

ABSTRACT To attenuate oscillations of railway passenger vehicles, hydraulic dampers are widely employed in suspension structures, for which high-speed electrical multiple units (EMUs) with excellent dynamics performance are critical. This study employs a vehicle-track coupling model to predict damper loads with an operation speed of 400 km/h. Five models characterizing hydraulic damper dynamics, namely the simplification model, Maxwell model, the new damper model presented by Ren, and two new damper models presented in this paper, are employed to study the availability of the damper models in simulating vehicle dynamics. Field-tested loads of the damping forces for the dampers of two types of EMUs are used to fulfill the investigation. It indicates that the damping velocity and unloading velocity of the damper is critical to the damping load and vehicle dynamics. The simplification model and Maxwell model have some deficiencies in the vehicle dynamics calculation, especially as the unloading velocity of the damper is much low.

Eulerian Parameter Inference: a probabilistic change of variables for model-based inference with high-variability data sets

Advances in measurement technology have led to the generation of increasingly large data sets across various scientific fields. Data that capture the variability of the underlying system or process, such as single-cell or imaging data, are particularly interesting. However, calibrating computational models to explain this type of data remains challenging.We interpret the model calibration as a Stochastic Inverse Problem (SIP), where the measurements are interpreted as probabilistic samples. Our new SIP solution approach, Eulerian Parameter Inference (EPI), only requires data with large empirical variance and a deterministic simulation model that maps model parameters to simulation results. EPI solves the SIP through a change of variables. This computationally efficient approach allows for direct and point-wise evaluation of parameter densities without ever inverting the simulation model. The resulting parameter distribution captures data variability completely, enabling full data reconstruction. Further, estimating multivariate model parameters can be reduced to a series of one-dimensional problems under the assumption of stochastically independent parameters. This property is vital for complex simulation models and involved problems.With EPI, we explain 1) annual average temperature data of 3168 weather stations around the world with an arithmetic model, 2) German district-specific COVID-19 infection data through a differential equation model, and 3) artificial data generated via a partial differential equation model with independent parameters. With a robust mathematical foundation and an easily accessible Python package, we provide a versatile solution framework for integrating data with a high variance into simulation models that promotes application to various research questions.

Comparing Recovery Volumes of Steady and Unsteady Injections into an Aquifer Storage and Recovery Well

Aquifer Storage and Recovery (ASR) can involve injecting available surface water into an unconfined aquifer and then extracting it to provide secondary water for irrigation. This study demonstrates a method for evaluating the appropriateness of steady injection versus unsteady injection for an assumed situation. In design, it can be important to affect the transient: the proportion of the injected water that would be subsequently extracted (versus that remaining in the aquifer) and the proportion within the extracted water that would be an injectate (versus ambient groundwater). These proportions can be predicted from the predicted value of an ASR well’s Recovery Effectiveness (REN)—the time-varying proportion of injectate that is extracted subsequently from the same fully penetrating well. Applying the demonstrated procedure with appropriately detailed data and simulation models can predict the REN values resulting from steady versus unsteady injection, followed by steady extraction. For convenience in displaying and computing REN, the injectate was assumed to have a 100 ppm conservative solute concentration. For this demonstration, a homogenous isotropic unconfined one-layer aquifer was assumed. The scenarios involved steady or unsteady injection for 61 days via a fully penetrating ASR well. Then, 91 days of steady pumping led to the extraction of a total volume equal to that injected. For the assumed hydrogeologic data—31 years of Salt Lake City, Utah, rainfall data and estimated captured runoff—the results show that steady injection is more likely to cause a predictable REN but might not cause a higher REN than daily varying injection of the same total volume. Assuming different runoff or hydrogeologic flows would lead to different REN values. Steady injection causes a predictable groundwater mound and can assure a sufficient vadose zone thickness for overlying plants. Augmentation and storage of captured rainwater can help to provide a steady injection rate. For a situation that requires REN management, appropriate simulations can help water managers design ASR systems that will achieve REN goals and increase sustainable groundwater availability.

Mechanical Design and Testing of Mobile Monitoring Units for Security Systems

Mobile monitoring systems are currently used in many applications related to environmental applications or the monitoring of health status. However, security monitoring systems are usually chosen for a specific object, area or perimeter. The main goal of our article is to present the mechanical design of mobile monitoring units. These units create the basis of a developed mobile monitoring security system, which can be applied to monitor any area of interest, even in demanding weather conditions, involving, for example, windiness or wide operational temperature ranges. Therefore, this article is focused on the mechanical design of mobile monitoring units, which are constructed not only so that they can withstand challenging environmental conditions, but also with regard to their simple transportation, manufacturing process and, if necessary, possible repairs. During the design, emphasis was also placed on the vibrations of the mobile monitoring units and their temperature dependence, because vibrations can significantly affect the correct functioning of the mobile monitoring security system and cause false alarm situations. To confirm the correctness of the simulation models, experiments were performed on the mobile monitoring unit prototypes.

"Room of Horrors": A Proof-of-Concept Simulation Model for Error Reduction Training in the Emergency Department.

This pilot study aimed to assess the effectiveness of a "room of horrors" (RoH) simulation in identifying patient safety threats in a pediatric emergency department (PED) and to evaluate health care workers' (HCWs') perceptions of the experience. We developed an RoH simulation featuring 25 potential safety hazards derived from actual PED incidents and "never events." The teams of physicians and nurses who participated in the simulation identified as many hazards as they could within a 10-minute window followed by a debriefing session during which errors were corrected and missed hazards were pointed out. The participants' perceptions were measured on a 5-point Likert scale of a questionnaire. Twenty sessions that included a total of 45 participants were conducted. The teams identified an average of 23 of 25 safety threats, yielding a mean detection rate of 86% for electronic medical record errors, 95% for physical space errors, and 97% for communication errors. The simulation received high ratings for overall positive experience (4.88/5) and impact on daily activities (4.68/5). The RoH simulation experience was effective in evaluating the level of retention of earlier training and the ability of multidisciplinary teams to correctly identify major safety threats in a PED setting. The participants found the experience valuable and engaging. Future research should focus on other means by which the retention of learned skills can be reinforced and new safety threats can be identified with a high level of alertness.

A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model

AbstractCurrent simulation models considerably underestimate local-scale, short-duration extreme precipitation induced by tropical cyclones (TCs). This problem needs to be addressed to establish active response policies for TC-induced disasters. Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. The TCPM-ML was applied for multiple temporal scale hazard assessment. The results show that: (1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations, but also preserved the physical meaning of the results, contrary to ML methods; (2) Machine learning algorithms enhanced the TCPM ability to reproduce observations, although the hourly extreme precipitations remained slightly underestimated; (3) Best performance was obtained with the XGBoost or EL algorithms. Combining the strengths of both XGBoost and RF, the EL algorithm yielded the best overall performance. This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.

A Study on the Influence of Natural Fractures in Tight Sandstone Reservoirs on Hydraulic Fracture Propagation Behavior and Post-Fracture Productivity

Tight sandstone gas reservoirs are rich in reserves and are an important part of unconventional oil and gas resources. However, natural fractures’ impact on hydraulic fracture propagation behavior and network formation mechanisms remain unclear. Exploring how to optimize fracturing parameters to maximize post-fracturing productivity requires further investigation. Therefore, this study focused on the characteristics of tight sandstone gas reservoirs and established a three-dimensional numerical simulation model for hydraulic fracture propagation and post-fracturing productivity using production history matching to validate the reliability of the model. Based on this model, this study investigated the influence mechanisms of natural fracture angles, density, and lengths on hydraulic fracture propagation behavior and network formation. The spatial distribution of hydraulic fracture widths in three dimensions is also explored. When natural fracture angles are lower, a greater number of natural fractures are activated, leading to more developed secondary hydraulic fractures and the formation of complex fracture networks. Hydraulic fractures tend to penetrate directly through high-angle natural fractures. Single-well cumulative gas production increases initially with increasing natural fracture angles, then decreases, but increases with higher natural fracture density and length. Optimal fracturing in areas with longer natural fractures, lower angles, and higher density distribution enhances single-well productivity effectively.

Contributon-Informed Approach to RPV Irradiation Study Using Hybrid Shielding Methodology

An important aspect of pressurized water reactor (PWR) lifetime monitoring is supporting radiation shielding analyses which can quantify various in-core and out-core effects induced in reactor materials by varying neutron–gamma fields. A good understanding of such a radiation environment during normal and accidental operating conditions is required by plant regulators to ensure proper shielding of equipment and working personnel. The complex design of a typical PWR is posing a deep penetration shielding problem for which an elaborate simulation model is needed, not only in geometrical aspects but also in efficient computational algorithms for solving particle transport. This paper presents such a hybrid shielding approach of FW-CADIS for characterization of the reactor pressure vessel (RPV) irradiation using SCALE6.2.4 code package. A fairly detailed Monte Carlo model (MC) of typical reactor internals was developed to capture all important streaming paths of fast neutrons which will backscatter the biological shield and thus enhance RPV irradiation through the cavity region. Several spatial differencing and angular segmentation options of the discrete ordinates SN flux solution were compared in connection to a SN mesh size and were inspected by VisIt code. To optimize MC neutron transport toward the upper RPV head, which is a particularly problematic region for particle transport, a deterministic solution of discrete ordinates in forward/adjoint mode was convoluted in a so-called contributon flux, which proved to be useful for subsequent SN mesh refinement and variance reduction (VR) parameters preparation. The pseudo-particle flux of contributons comes from spatial channel theory which can locate spatial regions important for contributing to a shielding response.

Designing unit Ising models for logic gate simulation through integer linear programming

Ising models minimizing a quadratic objective function with spin variables of either − 1 or + 1 are instrumental in tackling combinatorial optimization problems by programmable Quantum Annealers. This paper introduces unit Ising models, where non-zero coefficients are restricted to − 1 or + 1 . Due to the limited resolution of quantum annealers, unit Ising models are more suitable for quantum annealers. A fixed unit Ising model for logic circuits could lead to Application-Specific Unit Quantum Annealers (ASUQAs) for inverse function computation, similar to ASICs. Our findings suggest a powerful new method for compromising the RSA cryptosystem by leveraging ASUQAs for factorization.

Learning soft tissue deformation from incremental simulations.

Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM). Prior works divide FEM simulations into increments to enhance convergence and accuracy. However, this practice elongates simulation time, thereby impeding clinical integration. To accelerate simulations, deep learning (DL) models have been explored. Yet, previous efforts either perform simulations in a single step or neglect the temporal aspects in incrementalsimulations. This study investigates the use of spatiotemporal incremental modeling for biomechanics simulations of facial soft tissue. We implement the method using a graph neural network. Our method synergizes spatial features with temporal aggregation using DL networks trained on incremental FEM simulations from 17 subjects that underwent orthognathicsurgery. Our proposed spatiotemporal incremental method achieved a mean accuracy of 0.37 mm with a mean computation time of 1.52 s. In comparison, a spatial-only incremental method yielded a mean accuracy of 0.44 mm and a mean computation time of 1.60 s, while a spatial-only single-step method yielded a mean accuracy of 0.41 mm and a mean computation time of 0.05 s. Statistical analysis demonstrated that the spatiotemporal incremental method reduced mean errors compared to the spatial-only incremental method, emphasizing the importance of incorporating temporal information in incremental simulations. Overall, we successfully implemented spatiotemporal incremental learning tailored to simulate soft tissue deformation while substantially reducing simulation time compared toFEM.

System to Assist the Driver During a Single Lane Change Maneuver, in the Conditions of Danger Arising from a Change in the Condition of the Road Surface

The article describes the application of a vehicle simulation model to assess the driving hazards that arise due to changes in the tyre–road adhesion during a lane change. An experimentally verified vehicle dynamics model was used. The typical single lane change maneuver, performed on dry concrete, wet concrete, and ice-covered roads, was simulated for three vehicle speeds. The disturbance was introduced as a changed tyre–road adhesion on the target lane. Typical sinusoidal (one-period) input was applied to the steering wheel. Adhesion change may lead to an accident when considering the driver’s reaction time. An original control algorithm has been built. It is the driver assistance system with a double PID controller of the steering wheel angle. The system parameters were determined based on the principles recommended in the automatic control engineering monographs. The controller fulfils its tasks for motion at very high speeds on a homogeneous road surface and for the case where the tyre–road adhesion is changed during a maneuver. Thanks to this, the threat can be avoided.

Calculation Method for Equivalent Ejection Mass of Variable Thrust in the Internal Ballistic Equation of Pyrotechnic Folding Rudder

In order to solve the calculation of actuator ejection mass in pyrotechnic folding rudder, this paper investigates the calculation method for equivalent ejection mass of variable thrust in the Internal ballistic equations of pyrotechnic folding rudder, and obtains the fitting curves of equivalent ejection mass and gunpowder thrust in the case of variable forces and calculates the gunpowder thrust curves during the unfolding process by establishing the dynamic simulation model of the pyrotechnic folding rudder. After test verification, the test results are consistent withe the simulation results, indicating that the method is accurate and effective, and provides a theoretical basis for the design of the pyrotechnic folding rudder.

Mechanism Analysis of Bubble Discharge Within Silicone Gels Under Pulsed Electric Field

Silicone gel, used in the packaging of high-voltage, high-power semiconductor devices, generates bubbles during the packaging process, which accelerates the degradation of its insulation properties. This paper establishes a testing platform for electrical treeing in silicone gel under pulsed electric fields, investigating the effect of pulse voltage amplitude on bubble development and studying the initiation and growth of electrical treeing in a silicone gel with different pulse edge times. The relationship between bubbles and electrical treeing in silicone gel materials is discussed. A two-dimensional plasma simulation model for bubble discharge in silicone gel under pulsed electric fields is developed, analyzing the internal electric field distortion caused by the response times of different ions and electrons. Additionally, the discharge current and its effects on silicone gel under pulsed electric fields are examined. By studying the influence of different pulse edge times, repetition frequencies, and temperatures on discharge current magnitude and ozone generation rates, the impact of electrical breakdown and chemical corrosion on the degradation of organic silicone gel under various operating conditions is analyzed. This study explores the macroscopic and microscopic mechanisms of dielectric performance degradation in organic silicone gel under pulsed electric fields, providing a basis for research on high-performance packaging materials and the development of high-voltage, high-power semiconductor devices.

Kinematic Analysis and Trajectory Planning Based on a Six-Axis Robotic Arm

Kinematic analysis and time-optimal trajectory planning are applied to the robot in order to enhance its operational efficiency and preserve its continuous, smooth running trajectory. In this paper, the robot kinematics model is established based on the D-H parametric method with the Daqi Elfin series E05 six-axis robot as the research object. After that, forward and inverse kinematic analysis is carried out. And the segmented polynomial interpolation trajectory planning model of robotic arm is optimized in time based on the improved particle swarm algorithm. And MATLAB robot toolbox is used to build the robotic arm simulation model. From there, the trajectory planning simulation experiments are performed. As well as the experimental results show that the improved particle swarm algorithm effectively reduces the trajectory planning time. In the simulation experiment the time is reduced by 40.78% compared to the initial setting. The effectiveness of the robotic arm is greatly increased by this research.

MULTI-AEROELASTIC PHENOMENA MODELLING PART I: THE DEVELOPMENT OF WHOLE LPC MODEL FOR CIVIL TURBOFAN ENGINE

Abstract Aeroelastic phenomena in the low pressure compression (LPC) system of civil turbofan engines can cause a significant high cycle fatigue (HCF) risk to LPC components. Currently, the mechanical integrity risk to the LPC components is assessed by performing multiple, discrete aeromechanical simulations, which require large resources and incur high time cost. Therefore, the development of a comprehensive modelling simulation approach is necessary to assess multiple phenomena and components simultaneously that can provide accurate results within design timescales. This paper presents the methodology for generating a common, system level model for investigating multiple aeroelastic phenomena in fan and outlet guide vanes (OGVs). The development of a high fidelity, full annulus CFD model of the whole low pressure compression system is described. Time accurate unsteady CFD simulations are then conducted at multiple flight conditions. These information-rich simulation models are interrogated to extract various parameters of interest to assess multiple aeroelastic phenomena such as, 1EO fan forced response, fan alternating passage divergence (APD) forced response, OGV buffet, and OGV resonant forced response etc. In the second part of this paper, the application of this modelling methodology is demonstrated to evaluate and to better understand the efficacy of OGV asymmetric cyclic patterns.

Performance study of oil-injection hermetic CO2 scroll compressor for automotive air conditioning system.

An oil-injection CO2 scroll compressor prototype and relative simulation model are developed for automotive Air Conditioning. The effects of oil to refrigerant mass ratio on the compressor performance are studied theoretically and experimentally. The results show that oil-injection is an effective way to improve the volumetric and indicated efficiencies, and reduce discharge temperature. The optimal oil-injection quantity varies at different conditions, and it decreases from 7.9% to 6.3% at fixed discharge pressure 10MPa when the suction pressure increases from 4.5MPa to 5MPa. Similar linear relationships are found and the different slopes of the line are exhibited at fixed suction and discharge pressure separately. Numerical results show that oil-injection has little influence on pressure in working chamber during suction and discharge process, but it can slow up the rising of pressure in compression process and reduce the salient of pressure at the end of compression process.

Modeling incident management processes in information security at an enterprise

Objectives. The primary aim of the study is to develop a model for managing information security incidents within an enterprise that minimizes damage and costs associated with incident resolution under limited resources and time constraints.Methods. The paper analyzes existing approaches to managing information security incidents, including mathematical and simulation models, stochastic differential equations, Markov chains, and other methods. The study is based on a systems approach, incorporating analysis of incident parameters, actions for their resolution, response times, damages due to incident occurrence, and the probability of incident elimination. To validate the developed model, synthetic data reflecting various types of incidents and possible actions were used.Results. The proposed model optimizes incident management by minimizing damage and costs. It considers parameters such as incident criticality, available resources, response time, and the likelihood of successful incident resolution. Testing of the model on synthetic data showed that the proposed approach significantly improves the selection of optimal actions for responding to incidents in situations constrained by budget and time limitations, thereby enhancing the overall effectiveness of incident management.Conclusions. Implementing the proposed model in enterprises will improve the overall level of information security, enhance incident response efficiency, and strengthen information protection processes. This will ensure the minimization of risks associated with data leaks and other incidents, thus helping enterprises to make informed and timely decisions under conditions of limited resources and time.

IPMC-Based Purcell’s Three-Link Swimmer: Simulations and Experiments at Low-Reynolds Conditions

The Purcell’s swimmer, consisting of three links with two one-degree-of-freedom joints as defined by Edward M. Purcell, has been studied by several authors since its introduction in 1977. Researchers have delved into its mathematical foundations, analysing and optimising its motion for efficient propulsion. However, despite these theoretical advances, the practical realisation and experimental characterisation of Purcell’s swimmers remains relatively unexplored. Critical aspects such as material selection, manufacturing techniques, and experimental validation under real conditions represent important knowledge gaps. This paper contributes to bridging this gap by presenting a prototype of such a swimmer using ionic polymer-metal composites (IPMC) as link actuators. A simulation model is developed based on physical modelling tools in MATLAB®/Simulink®. Both simulation and experimental results at low-Reynolds-number (Re) conditions are presented to demonstrate the performance of the swimmer.

Efficient Positioning of QTL and Secondary Limit Thresholds in a Clinical Trial Risk-Based Monitoring.

In the high-stakes world of clinical trials, where a company's multimillion-dollar drug development investment is at risk, the increasing complexity of these trials only compounds the challenges. Therefore, the development of a robust risk mitigation strategy, as a crucial component of comprehensive risk planning, is not just important but essential for effective drug development, particularly in the RBQM (Risk-Based Quality Management) ecosystem and its component-RBM (Risk-Based Monitoring). This emphasis on the urgency and significance of risk mitigation strategy can help the audience understand the gravity of the topic. The paper introduces a novel modeling framework for deriving an efficient risk mitigation strategy at the planning stage of a clinical trial and establishing operational rules (thresholds) under the assumption that contingency resources are limited. The problem is solved in two steps: (1) Deriving a contingency budget and its efficient allocation across risks to be mitigated and (2) Deriving operational rules to be aligned with risk assessment and contingency resources. This approach is based on combining optimization and simulation models. The optimization model aims to derive an efficient contingency budget and allocate limited mitigation resources across mitigated risks. The simulation model aims to efficiently position each risk's QTL/KRI (Quality Tolerance Limits/Key Risk Indicators at a clinical trial level) and Secondary Limit thresholds. A case study illustrates the proposed technique's practical application and effectiveness. This example demonstrates the framework's potential and instills confidence in its successful implementation, reassuring the audience of its practicality and usefulness. The paper is structured as follows. (1) Introduction; (2) Methodology; (3) Models-Risk Optimizer and Risk Simulator; (4) Case study; (5) Discussion and (6) Conclusion.