3d Immersive Simulation
3d Immersive Simulation - Related Videos
11:29
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
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Cited by 14 •
2014
A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
05:11
Multimodal 3D Printing of Phantoms to Simulate Biological Tissue
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Cited by 1 •
2020
Spin coating, polyjet printing, and fused deposition modeling are integrated to produce multilayered heterogeneous phantoms that simulate structural and functional properties of biological tissue.
06:04
Simulation of the Planetary Interior Differentiation Processes in the Laboratory
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Cited by 1 •
2013
The high-pressure and high-temperature experiments described here mimic planet interior differentiation processes. The processes are visualized and better understood by high-resolution 3D imaging and quantitative chemical analysis.
11:30
Buoyancy and Drag on Immersed Bodies
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2023
Source: Alexander S Rattner and Sanjay Adhikari; Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA Objects, vehicles, and organisms immersed in fluid mediums experience forces from the surrounding fluid in the form of buoyancy- a vertical upward force due to fluid weight, drag- a resistive force opposite the direction of motion, and lift- a force perpendicular to the direction of motion. Prediction and characterization of these forces is...
FAQs
The system operates as a closed interactive loop. Real-time rendering updates the visual environment, spatial audio supplies contextual cues, motion tracking records movement, and interactive scenarios respond to participant choices. These linked components make actions consequential within the simulation, allowing researchers to connect specific environmental events with observable behavior rather than relying only on responses to static material.
Controlled simulated environments allow researchers to present repeatable situations while retaining context that can influence attention, decisions, emotions, or social responses. This balance matters because highly controlled conditions support comparison across participants, whereas realistic scenarios make behavior more relevant to situations such as learning, clinical encounters, social interaction, or risk. The design must serve both goals.
Simulation permits researchers to model situations under controlled and repeatable conditions, while real-world settings may introduce logistical difficulties and less control over what participants encounter. It can also reduce some ethical challenges associated with exposing people to risky or difficult circumstances. The resulting behavioral data therefore reflect responses to standardized scenarios rather than uncontrolled everyday events.
A study begins by selecting the behavior and situation to examine, such as attention during a learning task or decisions in a risky environment. Researchers then present an interactive scenario, use motion tracking and participant choices as behavioral measures, and examine how responses change within the controlled setting. Repeating the same scenario supports comparisons across observations or participants.
The method can generate data about movements and choices while participants engage with simulated situations. Those measures support investigation of attention, decision-making, emotion, and social responses. For example, a study may examine how a participant responds during a social interaction, learning situation, clinical encounter, or risky environment, using the scenario to connect behavior with a defined context.
It is particularly useful when researchers need realistic behavioral contexts but face practical or ethical limits in real-world experiments. Simulated social interactions, learning situations, clinical encounters, and risky environments can be studied without depending entirely on direct exposure to those settings. The approach also supports repeatable observation, making it valuable for comparing responses across controlled behavioral conditions.