Design Of Simulation Experiments Research Articles

Integrated Permeability Stress Sensitivity Studies of Multi-cycle Injection and Production for Underground Gas Storage

Abstract This paper details the design of multi-cycle injection and production stress simulation experiments, aimed at quantitatively characterizing the permeability stress sensitivity in underground gas storage(UGS) during multiple operation cycles. Derived from the reservoir sensitivity evaluation method and the stress sensitivity coefficient method, a suite of stress sensitivity evaluation index was introduced to discuss the effect of stress variation paths on the stress sensitivity characteristics. It is found out that the stress sensitivity is common in sampled gas storage cores. Meanwhile, there is a significant stress sensitivity hysteresis effect in multi-cycle injection and withdrawal experiments. After multiple cycles, the rock stress sensitivity is less dependent on the stress path. However, due to the superposition of multiple cycle stress sensitive hysteresis effect, the multi-cycle permeability damage ratio and irreversible damage ratio increased. The phenomenon of stress sensitivity hysteresis is especially pronounced in cores with low permeability. This study offers a theoretical foundation for predicting gas storage operation schemes and formulating technical policies.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Research and application of a coupled wheel-track off-road robot based on separate track structure

A novel coupled wheel/track mechanism is proposed to overcome the complexity and limited applicability of conventional coupled wheel/track robots. The mechanism is based on the design of a tracked off-road robot, where the track is separated from the driving wheel and the load-bearing wheel by a fork fixed on the frame. The tension-wheel compensates for the shortening of the track winding length by moving along the frame. The chain transmission between the first pair of load-bearing wheels and the driving wheel enables the wheeled operation. The coupling mechanism is characterized by only one set of walking mechanism, and the coupling is realized by separating part of the walking mechanism, avoiding the use of the clutch. The advantages of this mechanism are its simple and reliable structure, low cost, and easy installation of the suspension system. A simulation experiment is conducted to verify the performance of the mechanism, and a prototype is designed and manufactured for testing. The structure was used to design simulation experiments, and a test model of the vehicle was designed and fabricated. The design specifications were achieved by the results.

Simulation experiment design for calibration via active learning

Simulation models often have parameters as input and return outputs to understand the behavior of complex systems. Calibration is the process of estimating the values of the parameters in a simulation model in light of observed data from the system that is being simulated. When simulation models are expensive, emulators are built with simulation data as a computationally efficient approximation of an expensive model. An emulator then can be used to predict model outputs, instead of repeatedly running an expensive simulation model during the calibration process. Sequential design with an intelligent selection criterion can guide the process of collecting simulation data to build an emulator, making the calibration process more efficient and effective. This article proposes two novel criteria for sequentially acquiring new simulation data in an active learning setting by considering uncertainties on the posterior density of parameters. Analysis of several simulation experiments and real-data simulation experiments from epidemiology demonstrates that proposed approaches result in improved posterior and field predictions.

A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental Design

A Uniform Error Bound for Stochastic Kriging: Properties and Implications on Simulation Experimental Design

Evaluating current and future pedestrian mid-block crossing safety treatments using virtual reality simulation

Virtual reality (VR) simulation offers a proactive, cost effective, immersive, and low risk platform for studying pedestrian safety. Within immersive virtual environments (IVEs), existing and alternative design conditions and intelligent transportation systems (ITS) technologies can be directly compared, prior to real-world implementation, to assess the impacts alternatives may have on pedestrian safety, perception, and behavior. Environmental factors can be controlled within IVEs so that test trials are replicable and directly comparable. Coupled with stated preference feedback, participants’ observed preferences and behavior provide a comprehensive understanding of the impacts of proposed design alternatives. This research presents a case study of pedestrian behavior with three different mid-block crossing safety treatments modeled within a one-to-one scale IVE replication of a real-world location in Charlottesville, Virginia. The three safety treatments consider both passive and active collision avoidance designs and technologies, including (1) the existing painted crosswalk, (2) the addition of rectangular rapid flashing beacons (RRFBs), and (3) a pedestrian to everything (P2X) ITS phone application. Additionally, this paper demonstrates a VR simulation experimental design and framework for testing pedestrian safety treatments within naturalistic and replicable IVEs to assess both stated and observed preferences and behaviors of pedestrians. Repeated measures ANOVA indicated changes in both accepted gap size (p = 0.001) and crossing speed (p < 0.001) with alternative safety treatments. Generalized mixed models showed that pedestrians waited for statistically larger gap sizes (p = 0.02) without the assistance of alternative safety technologies (RRFBs and P2X application) and pedestrians crossed the street significantly faster (p = 0.001) without the alternative safety technologies, leading to unsafe dashing behavior. Through post-experiment surveys, it was found that participants perceived the As Built environment to be the least safe of the three treatments and that their sense of risk within the IVE was realistic. Considering both the observed crossing behavior and stated feedback, pedestrians exhibited intentionally unsafe darting behavior without assistive safety technology. This study demonstrates how VR simulation may be leveraged to study both stated preferences and observed behavior for understanding the safety implications of alternative roadway designs, providing a proactive approach for assessing and designing for pedestrian safety.

Teaching Practice of 'Integration of Theory and Practice' in the Motor Control Course under the Guidance of Moral Education"

The article carries out teaching reform in the aspects of teaching content, teaching methods and means, teaching mode, and moral education of the motor course, re-integrates the educational resources of the electrical control technology course, reconstructs the content of subject knowledge, integrates the craftsman spirit of "dedication, lean, focus, innovation" and the industry values of "safety, standardization, pragmatism, efficiency", and carries out a "theory-practice integration" teaching practice that combines online and offline teaching with ideological and political education as the leading factor, simulation experiment design as the main line, classroom theoretical explanation as the auxiliary line, and practical operation as the strengthening factor. It ensures the integration of value shaping, knowledge impartment, and ability cultivation, and implements the goal of cultivating morality and fostering talents.

Parameters Optimization for Electrophoretic Deposition of Mn1.5Co1.5O4 on Ferritic Stainless Steel Based on Multi-Physical Simulation

Solid oxide fuel cells (SOFCs) are an effective and sustainable energy conversion technology. As operating temperatures decrease, metal interconnects and supports are widely employed in SOFCs. It is critical to apply a protective coat on ferritic stainless steel (FSS) to suppress Cr evaporation and element interdiffusion under high temperatures. Electrophoretic deposition (EPD) is a promising approach for depositing metal oxides on FSS substrate. Here, a method based on 3D multi-physical simulation and orthogonal experimental design was proposed to optimize deposition parameters, including applied voltage, deposition time, and electrode distance. The EPD process to deposit Mn1.5Co1.5O4 particles in a suspension of ethanol and isopropanol was simulated and the effects of these three factors on the film thickness and uniformity were analyzed. The results indicate that applied voltage has the greatest impact on deposition thickness, followed by deposition time and electrode distance. Meanwhile, deposition time exhibits a more significant effect on film unevenness than applied voltage. Additionally, the particle-fluid coupling phenomenon was analyzed during the EPD process. In practice, these deposition parameters must be selected appropriately and the deposition time must be controlled to obtain a uniform coating. The proposed method can reduce cost and shorten the design period.

Simulation Experiment Design and Control Strategy Analysis in Teaching of Hydrogen-Electric Coupling System

Hydrogen energy, as a clean and green energy medium, is characterized by large capacity, extended lifespan, convenient storage, and seamless transmission. On the one hand, in the power system, hydrogen can be prepared by the electrolysis of water using the surplus power from intermittent new energy generation, such as photovoltaic and wind power, to increase the space for new energy consumption. On the other hand, it can be used to generate electricity from the chemical reaction between hydrogen and oxygen through the fuel cell and be used as a backup power source when there is a shortage of power supply. In this paper, based on the teaching practice, the conversion mechanism and coupling relationship between various forms of energy, such as photovoltaic energy, hydrogen energy, and electric energy, were deeply analyzed. Further, a hydrogen-electricity coupling digital simulation experimental system, including photovoltaic power generation, fuel cell, and electrolysis hydrogen system, was formed. Simultaneously, considering the synergy between hydrogen production and electricity generation businesses, as well as the demand for the efficient utilization and flexible regulation of multiple energy sources, eight sets of simulation experimental scenarios were designed. A cooperative control strategy for the hydrogen-electric coupling system was proposed and validated through simulation on the MATLAB/SIMULINK-R2023a platform. This study shows that the simulation system has rich experimental scenarios and control strategies, and can comprehensively and accurately demonstrate the multi-energy complementary and cooperative control characteristics of the hydrogen-electric coupling system.

The Simulated Experimental Design and Study of the Synergistic Treatment of Chicken Manure and Traditional Chinese Medicine Residues on Earthworm Growth and Soil Quality

Annelids conspicuously exert influence upon soil physicochemical attributes through their alimen-tary, burrowing, and excretion endeavors, thereby imparting ramifications upon soil erosion phenomena. Nev-ertheless, comprehension of the particular repercussions stemming from annelid activities vis-à-is soil erosion remains circumscribed. The primary objective of this investigation was to scrutinize the synergistic ramifica-tions of gallinaceous fecal matter and remnants of traditional Chinese medicinal substances on annelid prolif-eration and soil characteristics within a simulated experiment. In order to gauge the impact of annelid activities upon soil hydric distribution, runoff velocity, and soil erosion, a laboratory-simulated precipitation experiment was executed across three incline gradients (5 degrees, 10 degrees, and 15 degrees), featuring a uniform pre-cipitation intensity of 80 mm/h and a 60-minute precipitation duration post-runoff initiation. Findings evinced that annelids significantly heightened soil hydric infiltration and retention. In tanks inhabited by annelids, the increments in soil hydric retention were 93%, 51%, and 70% more elevated than those in control plots at incline gradients of 5 degrees, 10 degrees, and 15 degrees, respectively. Comparatively, earthworm activities led to a 70% reduction in runoff rate at a 5-degree slope, a 13% reduction at 10 degrees, and a 39% reduction at 15 degrees. However, soil erosion rates increased by 42% and 46% at slope gradients of 10 degrees and 15 degrees, respectively. Earthworms, through their feeding and burrowing activities, not only enhanced soil water infil-tration but also mitigated surface runoff while contributing to increased soil erosion. This research proffers invaluable perspicacity regarding the influence of subterranean fauna on the vicissitudes of soil erosion pro-cesses, furnishing empirical evidence amenable for assimilation into extant soil erosion simulation paradigms or as a substratum for the construction of nascent models.

基于器官系统的虚拟仿真实验项目设计——以呼吸系统为例

基于器官系统的虚拟仿真实验项目设计——以呼吸系统为例

THE FRACTIONAL DISEN–FISHER PLANE: AN EFFECTIVE APPROACH TO DISTINGUISH COMPLEX TIME SERIES

With the explosive growth of data quantity and rapid development of nonlinear dynamics as well as the growing demand for complex data classification in the field of artificial intelligence and machine learning, the research of complex time series, generated by complex systems, has attracted enormous interests. However, how to simultaneously distinguish different types of time series data and extract more accurate and detailed information from them in the light of localized and global scale perspectives remains significant and needs to be tackled. Thus, in this paper, we propose the fractional DisEn–Fisher plane, which is innovatively constructed by the fractional form of dispersion entropy and Fisher information measure. These are both effective tools to diagnose the essential properties of complex time series, to analyze the complexity of systems and to depict the contained statistical information with higher accuracy and effectiveness. Several classical entropy plane methods are selected as a comparison to design simulation experiments by simulated data and three real-world datasets. Comparative experimental results show that this method is a feasible and reliable improvement method, which will help to provide more additional information in time series recognition and dynamic characterization. It may not only provide new insights for further improvement of complex time series analysis, but also have important implications for developing complex data clustering methods.

Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event.

Attribution of compound events informs preparedness for emerging hazards with disproportionate impacts. However, the task remains challenging because space-time interactions among extremes and uncertain dynamic changes are not satisfactorily addressed in the well-established attribution framework. For attributing the 2020 record-breaking spatially compounding flood-heat event in China, we conduct a storyline attribution analysis by designing simulation experiments via a weather forecast model, quantifying component-based attributable changes, and comparing with historical flow analogs. We quantify that given the large-scale circulation, anthropogenic influence to date has exacerbated the extreme Mei-yu rainfall in the mid-lower reaches of the Yangtze River during June-July 2020 by ~6.5% and warmed the co-occurring seasonal extreme heat in South China by ~1°C. Our projections show a further intensification of the compound event by the end of this century, with moderate emissions making the rainfall totals ~14% larger and the season ~2.1°C warmer in South China than the 2020 status.

N-IoU: better IoU-based bounding box regression loss for object detection

Object detection is one of the core tasks of computer vision, and bounding box (bbox) regression is one of the basic tasks of object detection. In recent years of related research, bbox regression is often used in the Intersection over Union (IoU) loss and its improved version. In this paper, for the first time, we introduce the Dice coefficient into the regression loss calculation and propose a new measure which is superior to and can replace the IoU. We define three properties of the new measure and prove the theory by mathematical reasoning and analysis of the existing work. This paper also proposes the N-IoU regression loss family. And the superiority of the N-IoU regression loss family is proved by designing simulation experiments and comparative experiments. The main results of this paper are: (1) The proposed new measure is better than IoU which can be used to evaluate bounding box regression, and the three properties of the new measure can be used as a broad criterion for the design of regression loss functions; and (2) we propose N-IoU loss. The parameter n of N-IOU can be debugged, which can be widely adapted to different application scenarios with higher flexibility, and the regression performance is better.

Design and implementation of fine-grained realistic 3D virtual simulation experiment

Abstract Based on the virtual simulation platform, this paper firstly builds a five-in-one comprehensive, realistic 3D virtual platform with teaching method orientation, experiment method frontier, course thinking daily, team building refinement, and assessment and evaluation comprehensiveness. Then the establishment of the UAV nonlinear mathematical model, the bit attitude calibration of the UAV model, the virtual simulation of the motion of the UAV model, and the construction of the UAV flight sky realistic model based on the UAV corner control of virtual reality technology, and also the analysis of the UAV formation keeping control research based on 3D virtual simulation. The results show that the UAV takes off from a random initial position, so the initial error is large, and then the formation holding error is less than 0.1m when flying in a straight line, and the formation error is less than 1.79 when coordinating a turn. When flying in formation for the 40s, the ground sends a formation spreading command to perform a reconnaissance mission, and this study has a catalytic effect on the development of UAV aerial photography virtual simulation.

A new control strategy of CAVs platoon for mitigating traffic oscillation in a two-lane highway

With the development of autonomous driving technology, designing connected automated vehicles (CAVs) control strategies to mitigate traffic oscillations has become a hot topic. Most current control strategies are mainly oriented to single-lane highway scenarios and do not consider vehicles' lane-changing behaviors. To address the gap, this paper proposes the Follower-Stopper-Platoon (FSP) strategy, which attempts to mitigate traffic oscillation by controlling a platoon of CAVs in a two-lane scenario. Firstly, based on the Follower-Stopper (FS) control and considering CAVs' platoon behaviors, this paper proposes the FSP strategy and compares it with two comparative strategies, the Baseline and FS strategies. Then, a two-lane mixed traffic flow cellular automata model is developed and used to verify the effectiveness of the FSP strategy. Finally, this study demonstrates the efficacy of the FSP strategy by designing simulation experiments and analyzes the effect of CAVs' platoon size on the control effect. The result shows that (1) the FSP strategy can overcome the shortcomings of the FS control and will increase traffic flow speed while mitigating traffic oscillations. (2) In a two-lane scenario, as penetration rates of CAVs and traffic densities increase, the FSP strategy's advantages in mitigating traffic oscillations, reducing energy consumption and pollutant emissions, and improving speed and passenger comfort are gradually apparent. (3) Under the FSP strategy, the larger the maximum size of the CAVs platoon, the better the performance of the mixed traffic flow in terms of traffic efficiency, stability, fuel consumption, and pollutant emission.

Computationally Efficient Adaptive Design of Experiments for Global Metamodeling through Integrated Error Approximation and Multicriteria Search Strategies

Gaussian processes (GPs) are a popular technique for global metamodeling applications. Their objective in such settings is to establish an efficient and globally accurate approximation of the response surface of computationally expensive simulation models. When developing such GPs, the design of (simulation) experiments (DoE) plays an important role in reducing the required number of model runs for obtaining accurate approximations. Sequential (adaptive) selection of experiments can provide significant advantages, especially when the response surface is characterized by localized nonlinearities. Such adaptive DoE strategies for global metamodeling applications typically focus on minimizing the predictive GP variance, representing an exploration strategy, while recent developments have additionally considered the reduction of the GP bias obtained through cross validation, representing an exploitation strategy. While significant focus has been placed on the definition of appropriate adaptive DoE criteria, computational challenges still exist that limit the widespread adoption of adaptive DoE techniques—for example, related to the additional computational demand for identifying the optimal new experiment(s) or the necessity to establish proper schemes to combine exploration and exploitation strategies. To address these specific challenges, this research investigates two new adaptive DoE formulations. The first one focuses on the approximation of the popular integrated mean square error (IMSE) DoE criterion. The computationally demanding GP predictive variance update (after addition of each candidate experiment), required in the original IMSE formulation, is replaced by an approximation based on the current predictive variance and the domain of influence that surrounds each new experiment. The approximation is established through a parametric formulation that leverages the GP kernel to describe the aforementioned domain, with characteristics that are progressively calibrated across the GP training stages, to minimize the discrepancy between the actual and the approximated IMSE. The second formulation establishes a multicriteria search for simultaneously identifying multiple Pareto optimal experiments that balance exploration and exploitation objectives, replacing conventional strategies that establish a weighted combination of these objectives to promote a single DoE selection criterion.

Aerodynamics Optimization of Multi-Blade Centrifugal Fan Based on Extreme Learning Machine Surrogate Model and Particle Swarm Optimization Algorithm

The centrifugal fan is widely used in converting mechanical energy to aerodynamic energy. To improve the pressure of the multi-blade centrifugal fan used in an air purifier, an optimization process was proposed based on extreme learning machine (ELM) combined with particle swarm optimization (PSO). The blade definition position parameter and blade definition radian parameter were designed using the full-factor simulation experimental method. The steady numerical simulation of each experimental point was carried out using ANSYS CFX software. The total pressure of the multi-blade centrifugal fan was selected as the optimization response. The optimized ELM combined with the PSO algorithm considering the total pressure response value and the two multi-blade centrifugal fan parameters were built. The PSO algorithm was used to optimize the approximation blade profile to obtain the optimum parameters of the multi-blade centrifugal fan. The total pressure was improved from 140.6 Pa to 151 Pa through simulation experiment design and improved surrogate optimization. The method used in the article is meant for improving multi-blade centrifugal total pressure. The coupling optimization of impellers, volutes, and air intakes should be comprehensively considered to further improve the performance of centrifugal fans.

Track Planning for Damage Detection on the Building Enclosure by UAV Considering Image Detection

Considering the rapid acquisition and detection task of building enclosure damage information and the natural environment characteristics of building enclosure, a new unmanned aerial vehicle (UAV) track planning framework considering image detection and UAV dynamic characteristics is proposed. Firstly, considering the position of UAV in the task space for track planning, combined with the characteristics of the sensor detection equipment carried by UAV, and the task requirements of extracting and identifying the damage edge features and texture features on the shell of the building; the detection efficiency and detection safety of UAV are improved to the maximum extent. Secondly, the Improved Circle Chaos Initialization Strategy, Adaptive Weight Strategy and Elite Perturbation Mechanism are introduced to solve the problem that the traditional honey badger algorithm (HBA) is easy to fall into local optimal solution and to ensure that the designed algorithm is more in line with the inspection planning requirements of the outer surface of the building. Furthermore, improved honey badger algorithm (IHBA) is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA) and traditional badger algorithm in multi-dimensional horizontal through design simulation experiments, which shows that the design algorithm in this paper is efficient in solving the damage information collection of building enclosure.

Design of simulation experiments using Central Composite Design

In the context of research and development, it is key to achieve accurate and reliable results. However, often to obtain these results, a large number of experiments must be performed, which can significantly extend the research time and increase computational requirements. The solution to these problems may be efficient experimental planning, which allows for a reduction in the number of trials and optimization of the process. This article provides an insight into Central Composite Design (CCD) and its use in simulation experiments. We introduce various types of CCD designs, such as CCC (Central Composite Circumscribed), CCF (Central Composite Face centered), and CCI (Central Composite Inscribed), and analyze their use in creating second-order regression models. We also discuss the specific advantages and disadvantages of these approaches, as well as their possible alternatives, such as the Draper-Lin CCD design.