Virtual Experiments Research Articles

A 3-D interactive microbiology laboratory via virtual reality for enhancing practical skills

Virtual Reality (VR) laboratories are a new pedagogical approach to support psychomotor skills development in undergraduate programmes to achieve practical competency. VR laboratories are successfully used to carry out virtual experiments in science courses and for clinical skills training in professional courses. This paper describes the development and evaluation of a VR-based microbiology laboratory on Head-Mounted Display (HMD) for undergraduate students. Student and faculty perceptions and expectations were collected to incorporate into the laboratory design. An interactive 3-dimensional VR laboratory with a 360° view was developed simulating our physical laboratory setup. The laboratory environment was created using Unity with the (created) necessary assets and 3D models. The virtual laboratory was designed to replicate the physical laboratory environment as suggested by the students and faculty. In this VR laboratory, six microbiology experiments on Gram staining, bacterial streaking, bacterial motility, catalase test, oxidase test and biochemical tests were placed on the virtual platform. First-year biomedical science students were recruited to evaluate the VR laboratory. Students’ perception of the virtual laboratory was positive and encouraging. About 70% of the students expressed they felt safe using the VR laboratory and that it was engaging. They felt that the VR laboratory provided an immersive learning experience. They appreciated that they could repeat each experiment multiple times without worrying about mistakes or mishaps. They could personalise their learning by concentrating on the specific experiments. Our in-house VR-based microbiology laboratory was later extended to other health professions programmes teaching microbiology.

Trajectory planning of a closed five-bow-shaped bar linkage based on finite Fourier series

With aim to reduce the energy consumption, a trajectory planning method is presented for a closed five-bow-shaped bar linkage, which can be propelled itself by morphing configuration. The objective herein is to optimize the driving joints trajectories within the global feasible region when the linkage rolls along the ground with a desired acceleration. The driving joint trajectories were represented by finite Fourier series, whose coefficients were solved by genetic algorithm to ensure a minimal energy consumption of the linkage. The impact of the number of terms of finite Fourier series on the energy consumption was also discussed through numerical examples. As a result, the energy consumption based on this strategy had been reduced by 19%, comparing with the constant potential energy strategy. A number of terms between six and eight using to denote the joint trajectories are appropriate, because that a small number of terms is incapable of expressing the joint trajectories accurately, whereas, a large number makes the joints to be subjected to vibration shock. At last, simulation on a virtual model and experiments on a prototype were carried out to verify the effectiveness of the proposed method.

Elevating haptic interfaces: Dual‐rate sampling and field programmable gate array implementation for multi‐degree‐of‐freedom performance enhancement

AbstractIn this work, our primary focus centered on exploring the adaptability of the dual‐rate sampling scheme proposed earlier to enhance the performance of multi‐degree‐of‐freedom (multi‐DOF) impedance‐based haptic interfaces. The scheme employed independent sampling rates in a haptics controller, effectively mitigating the issue of reduced Z‐width at higher sampling rates. A key aspect of our investigation was the intricate implementation of the dual‐rate sampling scheme on a field programmable gate array (FPGA). This implementation on a logic hardware FPGA was challenging and led to the effective comparison of the uniform‐rate and dual‐rate sampling schemes of the multi‐DOF haptic controller. We used an in‐house developed two‐DOF pantograph as the haptic interface and an FPGA for implementing the controller strategy. FPGA‐based implementation presented challenges that were vital in testing controller performances at higher sampling rates. Virtual wall experiments were conducted to determine the stable and unstable interactions with the virtual wall. To complement the experimental results, we simulated the haptics force law for multi‐DOF system on Simulink/MATLAB. Notably, the dual‐rate sampling approach maintained the Z‐width of the two‐DOF haptic interface, even at higher controller sampling rates, distinguishing it from the conventional two‐DOF uniform‐rate control scheme. For example, employing a dual‐rate sampling combination of 20–2 kHz consistently ensured the stable rendering of a maximum virtual stiffness of approximately 700 N/mm and maintained a reliable virtual damping range spanning from 0 to 5 Ns/mm. In contrast, the 20 kHz uniform‐rate sampling approach failed to ensure interface stability in the presence of virtual damping, ultimately resulting in the unsuccessful implementation of any virtual stiffness at higher sampling rates. This work, therefore, establishes the potential of dual‐rate sampling in the realm of haptic technology, with practical applications in multi‐DOF systems.

Assessing Students' Critical Thinking Skills in Terms of Cognitive Style: A Study of the Application of the Inquiry Collaborative Based V-Lab Model in Programming Courses

This study explored the assessment of students' critical thinking skills in programming courses by considering individual cognitive styles. We apply the inquiry collaborative-based V-Lab model as a learning approach, allowing students to participate in virtual experiments and interactive simulations. This approach is applied to a programming class, taking into account the student's cognitive style as the determining factor. We use measurement methods that include solution analysis, problem-solving, and critical thinking of students. The results showed that the V-Lab model based on collaborative inquiry was effective in improving students' critical thinking skills, with variations in its effect depending on the individual's cognitive style. The implications of these findings are presented for the development of more adaptive and effective learning approaches in programming courses, as well as for further research into the interaction between cognitive styles and technology-based learning.

Effects of gas-pore sizes and locations on fatigue failure: A peridynamic study

Due to complicated features of gas-pores and great dispersions of fatigue lifetimes in additively manufactured metals, it is cumbersome to reveal the exact effects of gas-pores on fatigue failure by purely analyzing experiment data. In this paper, two-dimensional peridynamic virtual fatigue experiments (PD-VFE) are performed to investigate the effects of a singular circular gas-pore on the fatigue failure in smooth specimens. PD-VFE well captures three different modes of fatigue crack initiation (FCI), which are revealed to dominate the gas-pore impacts on fatigue lifetimes. Effects of two typical features of gas-pores, i.e. locations and sizes, are also clarified; to be specific, gas-pore location is the major factor to decide FCI mode and subsurface gas-pore is the most dangerous, while the role of gas-pore size is to scale the impacts of gas-pore location on fatigue lifetimes. The above PD-VFE results accord with experiment data in variation tendency, and can also explain assumptions and simplifications in Murakami approach.

The impact of presentation modes on mental rotation processing: a comparative analysis of eye movements and performance

Mental rotation is the ability to rotate mental representations of objects in space. Shepard and Metzler’s shape-matching tasks, frequently used to test mental rotation, involve presenting pictorial representations of 3D objects. This stimulus material has raised questions regarding the ecological validity of the test for mental rotation with actual visual 3D objects. To systematically investigate differences in mental rotation with pictorial and visual stimuli, we compared data of N=54\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=54$$\\end{document} university students from a virtual reality experiment. Comparing both conditions within subjects, we found higher accuracy and faster reaction times for 3D visual figures. We expected eye tracking to reveal differences in participants’ stimulus processing and mental rotation strategies induced by the visual differences. We statistically compared fixations (locations), saccades (directions), pupil changes, and head movements. Supplementary Shapley values of a Gradient Boosting Decision Tree algorithm were analyzed, which correctly classified the two conditions using eye and head movements. The results indicated that with visual 3D figures, the encoding of spatial information was less demanding, and participants may have used egocentric transformations and perspective changes. Moreover, participants showed eye movements associated with more holistic processing for visual 3D figures and more piecemeal processing for pictorial 2D figures.

Collaborative optimization of the wet screening process: A CFD-DEM based modeling approach with bi-objective optimization

Wet Vibrating Screening Process (WVSP) is an intricate physical process with interactions between screen surface, fluid and particles. In this paper, the linear WVSP was modeled with the CFD-DEM and optimized based on virtual experiments. Firstly, a numerical model was built to explore the impact of water on the particle behavior. Secondly, virtual experiments were conducted to establish the response surface for the screen efficiency and the screen surface load. Finally, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) was employed to collaboratively optimize the screening parameters for both screen efficiency and screen surface load. The modeling results indicate that the water stream is not conducive to the screen efficiency and the optimization results show that with the screen efficiency equal to 93.7%, 96.2% and 98.5%, the optimizable range of the screen surface load is 61.0%, 48.9% and 40.1% respectively. While the numerical model provides a revealing insight into the WVSP, the collaborative optimization significantly enhances screening performance and extends the screen's service life.

Mechanical characterization and constitutive modeling of additively-manufactured polymeric materials and lattice structures

Additively manufactured polymeric lattice structures are being extensively studied, primarily because their mechanical properties can be tailored by controlling the unit cell geometry, giving them higher designability than stochastic materials. However, the inherent layer-wise additive manufacturing process affects the base material properties related to the printing direction, which in turn affects the macroscopic responses of the entire lattice materials. A robust understanding and modeling of lattice structures' elastic and plastic yield behavior in a homogenized approach are essential to enhance their design and analysis efficiency in engineering applications. In pursuit of this goal, a unified printing angle-dependent constitutive model of base materials is proposed in line with the tensile experimental data. The elastic material properties (elastic modulus, shear modulus, and Poisson's ratio), obtained through numerical simulations of one unit-cell with periodic boundary conditions, exhibit anisotropic properties, with the degree of anisotropy determined by the angle of the constituent members and base materials. Furthermore, both experimental and numerical results of lattices demonstrate anisotropic mechanical response under horizontal and vertical compression. Virtual multiaxial experiments are conducted through multi-cell numerical simulations, enabling the determination of initial yielding points of two different lattice structures (Kelvin and Simple cubic and body-centered cubic hybrid structures) under various loading conditions using a dissipation energy-based criterion. Overall, the multiaxial yield surface of the investigated lattices under various stress states, except for the isotropic principal stress plane, can be properly depicted by the Extended-Hill anisotropic yield criterion.

Research on Virtual Simulation Experiment Teaching of Scraper Conveyor Condition Monitoring and Fault Diagnosis

The scraper conveyor status monitoring and fault diagnosis virtual simulation experiment system is designed and developed based on the operating environment of the scraper conveyor in the fully mechanized mining face. Using augmented virtual reality teaching resources, through immersive display and interactive operation experiments, students are guided on basis of scraper conveyor mechanical cognition, master the virtual teaching experiment of state monitoring and fault diagnosis such as broken chain monitoring, motor fault classification and identification, reducer compound fault separation, etc. The teaching practice shows that the system can overcome the harsh production environment in coal mines and the risk factors caused by equipment failure, improve students' understanding of the mechanical fault diagnosis technology, and mobilize students' enthusiasm for experiments and initiative to enhance students' innovative ability.

Experimental Design for the Acute Toxicology Assessment of Nanoscale Ultrafine Materials Based on Virtual Simulation Experiments and A Diverse Interactive Blended Teaching Approach

The escalating production of nanoscale ultrafine materials has led to a greater release of nanoparticles into aquatic environments, raising concerns about their environmental impact and potential health risks. As a cross-disciplinary field, environmental functional material toxicology seeks to blend professional courses and foster innovative talents in environmental health. This study introduces a novel experimental teaching program in nanoscale ultrafine material toxicology, centered on microalgae cultivation. A comprehensive experiment assessing the biological toxicity of ultrafine materials was developed, focusing on their impact on microalgae growth inhibition. By incorporating varied teaching methods like “online virtual simulation experiments with traditional offline teaching, offline experiments, and post-learning reinforcement”, this study advocates for a diverse interactive teaching approach for environmental toxicology experiments. This student-centric teaching model prioritizes interaction through virtual simulations, sustains course relevance, enhances classroom instruction, nurtures student individuality, underscores disciplinary advancements, and cultivates students’ abilities to pose, analyze, and resolve issues within a broad academic framework, thereby fostering innovative thinking capabilities.

Head-locked, world-locked, or conformal diminished-reality? An examination of different AR solutions for pedestrian safety in occluded scenarios

Many collisions between pedestrians and cars are caused by poor visibility, such as occlusion by a parked vehicle. Augmented reality (AR) could help to prevent this problem, but it is unknown to what extent the augmented information needs to be embedded into the world. In this virtual reality experiment with a head-mounted display (HMD), 28 participants were exposed to AR designs, in a scenario where a vehicle approached from behind a parked vehicle. The experimental conditions included a head-locked live video feed of the occluded region, meaning it was fixed in a specific location within the view of the HMD (VideoHead), a world-locked video feed displayed across the street (VideoStreet), and two conformal diminished reality designs: a see-through display on the occluding vehicle (VideoSeeThrough) and a solution where the occluding vehicle has been made semi-transparent (TransparentVehicle). A Baseline condition without augmented information served as a reference. Additionally, the VideoHead and VideoStreet conditions were each tested with and without the addition of a guiding arrow indicating the location of the approaching vehicle. Participants performed 42 trials, 6 per condition, during which they had to hold a key when they felt safe to cross. The keypress percentages and responses from additional questionnaires showed that the diminished-reality TransparentVehicle and VideoSeeThrough designs came out most favourably, while the VideoHead solution caused some discomfort and dissatisfaction. An analysis of head yaw angle showed that VideoHead and VideoStreet caused divided attention between the screen and the approaching vehicle. The use of guiding arrows did not contribute demonstrable added value. AR designs with a high level of local embeddedness are beneficial for addressing occlusion problems when crossing. However, the head-locked solutions should not be immediately dismissed because, according to the literature, such solutions can serve tasks where a salient warning or instruction is beneficial.

Constrained trajectory optimization and force control for UAVs with universal jamming grippers

This study presents a novel framework that integrates the universal jamming gripper (UG) with unmanned aerial vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, increasing the level of automation and operational reliability in aerial grasping. This control structure, while simple, is a powerful tool for various applications, ranging from material handling to disaster response, and marks an advancement toward genuine autonomy in aerial manipulation tasks. The key contribution of this research is the combination of a UG with a suitable control strategy, that can be kept relatively straightforward thanks to the mechanical intelligence built into the UG. The algorithm is validated through numerical simulations and virtual experiments.

An investigation into the influence of gender on crowd exit selection in indoor evacuation

Identifying how pedestrians make exit choices in an emergency is a critical topic related to safe indoor evacuation. Personal characteristics can influence the exit choice behavior of pedestrians. In particular, the gender factor is often used as an essential variable in indoor safety evacuation studies. However, the role of gender has not been explored in depth. The paper assumes that the following behavior of pedestrians depends on gender. Therefore, three essential factors are identified: the gender factor, crowd flow patterns, and exit width size. For the paper, a virtual experiment was conducted with 196 participants, and they had to decide whether to follow the crowd or not in the presence of capturing these different factors. The findings show that pedestrians prefer the same gender following, i.e., men are more likely to follow men to make exit choices, and women are more likely to follow women to make exit choices. We refer to this change in exit choice behavior depending on the influence of gender as the ‘gender effect.’ Furthermore, it was found that pedestrians have an innate preference to avoid the majority exit. However, when the width of the exit increases, pedestrians tend to choose the more comprehensive exit. But when the flow of people at larger exits increases, pedestrians choose to avoid the broader exits and choose smaller exits with fewer people, and the effect of the gender effect between pedestrians is diminished. These findings provide greater insight into pedestrian exit choice behavior and may be helpful for pedestrian evacuation management. In addition, they highlight the importance of gender effects and provide a basis for future research.

Coupling intention and actions of vehicle–pedestrian interaction: A virtual reality experiment study

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian’s intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.

Design of Digital Twin Cutting Experiment System for Shearer.

This study presents an advanced simulated shearer machine cutting experiment system enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to accurately mirror the operational dynamics of actual mining cutters. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature, and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal rock identification research, offering significant insights into the nuances of shearer operation.

Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment

Abstract. Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.

Development and Calibration of a Phenomenological Material Model for Steel-Fiber-Reinforced High-Performance Concrete Based on Unit Cell Calculations.

A phenomenological material model has been developed to facilitate the efficient numerical analysis of fiber-reinforced high-performance concrete (HPC). The formulation integrates an elasto-plastic phase-field model for simulating fractures within the HPC matrix, along with a superimposed one-dimensional elasto-plasticity model that represents the behavior of the embedded fibers. The Drucker-Prager plasticity and one-dimensional von-Mises plasticity formulations are incorporated to describe the nonlinear material behavior of both the HPC matrix and the fibers, respectively. Specific steps are undertaken during the development and calibration of the phenomenological material model. In the initial step, an experimental and numerical analysis of the pullout test of steel fibers embedded in an HPC matrix is conducted. This process is used to calibrate the micro-mechanical model based on the elasto-plastic phase-field formulation for fracture. In the subsequent step, virtual experiments based on an ellipsoidal unit cell, also with the resolution of fibers (used as a representative volume element, RVE), are simulated to analyze the impact of fiber-matrix interactions and their physical properties on the effective material behavior of fiber-reinforced HPC. In the final step, macroscopic boundary value problems (BVPs) based on a cuboid are simulated on a single scale using the developed phenomenological material model. The resulting macroscopic stress-strain characteristics obtained from both types of simulations, namely simulations of virtual experiments and macroscopic BVPs, are compared. This comparison is utilized for the calibration of material parameters to obtain a regularized solution and to assess the effectiveness of the presented phenomenological material model.

A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

Virtual experiment on platonic solids for teaching space-time quantization

The work presents the virtual experiment on Platonic solids in a complementary way, where its importance is crucially used for the treatment of the teaching of the quantization of space-time via loop quantum gravity. The research was part of one of the studies carried out with 23 students in the 3rd year of high school in São Cristóvão, Rio de Janeiro, Brazil. The project is part of several introduced subjects on modern and contemporary physics, as well as frontier research topics. The student starts to have a student-researcher posture and based on Ausubel and Bruner's learning theories, they develop significant learning and receive new information and concepts at the elementary level, conditioning them to a critical and reflective posture. We use the experiment in which students interactively learn three-dimensional shapes, calculate surface area and volume and discover mathematical properties of shapes, necessary knowledge that covers the discussion of the theory. Subsequently, they carried out the skills test and we collected data from the students' operations to present the scenario of innovation and reflection on the possibility of introducing research topics in physics, but with appropriate language for the audience of interest according to Bruner's learning theory.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.