Overview
Interactive Fluid Simulation Twin to Elevate Engineering Precision
Build digital twins that replicate complex fluid behavior in real time. Nexa's simulation blueprint enables teams to model, visualize, and optimize fluid systems faster with immersive interactivity and real-world accuracy
Scalable, Real-Time Simulation Engine
Secure Cloud or On-Prem Deployment
Adaptive Control for Dynamic Environments
What help you get to reinvent
Use smart sensors and cameras to continuously capture high-fidelity visual data from real-world fluid environments — such as pipelines, cooling systems, or chemical tanks. Visual AI agents interpret this data to replicate fluid behaviors with near-zero latency
Deploy edge-based AI models that simulate fluid dynamics instantly without relying on cloud latency. Whether it’s a remote facility or an on-device scenario, edge AI ensures performance, privacy, and seamless feedback loops
Connect your digital twin with SCADA systems, CAD tools, or industrial IoT platforms to centralize fluid simulation data. This enables automated alerts, performance predictions, and rapid prototyping within your existing tech ecosystem
Train visual AI agents to detect flow irregularities, pressure drops, or unexpected viscosity shifts autonomously. These agents can suggest optimizations or simulate alternate scenarios to improve system efficiency and safety
Architecture Overview
User Interaction Layer
This layer serves as the visual gateway for engineers, operators, and analysts to interact with the digital twin. Built with modern frontend frameworks like React or Angular, it provides access to dashboards, simulation controls, and visualization panels
Application Logic Layer
At the core of the simulation experience, this layer manages rules, scenarios, and interaction logic. It interprets user inputs, controls simulation flows, and coordinates various system responses
Agent Orchestration Layer
This layer handles the coordination of autonomous visual agents responsible for monitoring and adapting the simulation. Agents manage tasks such as anomaly detection, flow deviation alerts, and performance tuning
AI/ML Models Layer
Here, deep learning and physics-informed machine learning models simulate complex fluid behaviors. These models are trained on real-world and synthetic data, enabling the twin to predict fluid movement, turbulence patterns, and system anomalies
Data & Knowledge Layer
This foundational layer manages all structured and unstructured data including sensor streams, historical fluid dynamics data, material properties, and domain-specific rules. It powers both the AI models and agent decisions through real-time data ingestion, processing, and contextualization
Core Components
Orchestrator
Simulation Orchestration Engine
Acts as the central coordinator for all simulation workflows. It intelligently manages fluid state updates, visual input interpretation, and decision-making logic by routing data through the appropriate agents and models in real time—ensuring synchronized and adaptive digital twin performance
Data Router
Real-Time Sensor Integration and Routing
Continuously collects and organizes fluid system data from cameras, IoT sensors, and edge devices. This layer ensures accurate structuring of flow, pressure, and temperature data for immediate ingestion into AI models—enabling live simulations and continuous updates
Monitoring
Visual Agent Monitoring and Event Triggering
Empowers vision-based agents to monitor system dynamics such as flow irregularities, turbulence spikes, or viscosity changes. Agents trigger automated alerts or initiate corrective simulation actions—creating a responsive and self-regulating twin environment
Knowledge
Physics-Aware Knowledge Graphs
Integrates simulation rules, historical scenarios, and fluid dynamics equations into a structured knowledge graph. Enables agents and engineers to reference validated physical models, material behavior under stress, and past simulation data—enhancing learning, reasoning, and decision-making accuracy
API Layer
Secure Simulation API Gateway
Provides authenticated, rate-controlled access to simulation endpoints and agent functions. External systems like SCADA, CAD, or PLM tools can securely interact with the digital twin for data sync, simulation control, and real-time feedback without compromising security or system performance
Featured Blogs
Compliance and Privacy – Digital Twin for Interactive Fluid Simulation
Encrypted Sensor and Visual Data Pipelines
All incoming data from edge devices, cameras, and sensors is encrypted in transit using advanced TLS protocols. This ensures that real-time fluid flow data and system telemetry remain tamper-proof and private during transmission
Role-Based Simulation Access
Strict role-based access controls (RBAC) define who can view, modify, or execute simulations. Engineers, operators, and analysts only access relevant features—minimizing internal risk and enforcing operational boundaries
Private Cloud and On-Prem Deployment
Deploy the digital twin platform within isolated cloud environments or on your own servers. This ensures compliance with industry-specific regulations (e.g., energy, aerospace) and full control over simulation environments and data
End-to-End Data Encryption at Rest
All stored datasets, fluid simulation states, model parameters, and historical logs are encrypted at rest using enterprise-grade AES-256 encryption with key management integration
Full Audit Logging and Activity Monitoring
Track all user activity, agent actions, and simulation adjustments with comprehensive audit logs. These logs are immutable, timestamped, and accessible for compliance reviews, incident response, or certification processes
AI Agent Privacy by Design
Autonomous agents are designed to operate under strict privacy constraints—accessing only the minimal data required for fluid analysis and prediction. No personal or unrelated data is ever collected or processed, ensuring ethical AI behavior