HIL Water Tank Testbed

• HIL water tank testbeds are cyber-physical platforms integrating virtual and physical water tanks, fluid dynamics, and real-time controllers to support reproducible research.
• They utilize multi-layered architectures with industrial controllers (OpenPLC, LabVIEW) and IoT protocols (Modbus, ZigBee, LoRa) to enable process automation and cyber-physical security.
• Mathematical modeling and high-frequency data acquisition facilitate real-time feedback, robust control validation, and AI-enhanced anomaly detection in diverse operational regimes.

A hardware-in-the-loop (HIL) water tank testbed constitutes a cyber-physical experimental platform where virtual or physical water tanks, fluid dynamics, and control architectures are integrated in real time with hardware controllers, sensor networks, actuators, and simulation environments. Such testbeds support reproducible research for system identification, control validation, security evaluation, and AI-in-the-loop experimentation in domains spanning process automation, cyber-physical security, and water resource management. Representative exemplars include platforms for PUF-based authentication and attack-detection, active slosh damping, digital-twin validation, and intelligent distributed water networks (Oun et al., 25 Jan 2026, Jetzschmann et al., 2018, Batarseh et al., 2023).

1. Architectures and Key Components

Architectural diversity in HIL water tank testbeds reflects differing research aims, ranging from industrial control emulation to AI-powered cyber-biosecurity.

Process Control HIL (PUF, Security Testing):

A multi-layered architecture comprises a Factory IO 3D-simulated water tank (virtual process with sensor-actuator interface), an OpenPLC running on a Raspberry Pi 4 (serving as the programmable logic controller), a Python-based HMI (human–machine interface) script, and a Simulink-based PUF (Physically Unclonable Function) emulator (Oun et al., 25 Jan 2026).

Slosh Damping and Fluid-Structure HIL:

Experiments employ a large cylindrical tank (600–1100 L) mounted on a six-degree-of-freedom Hexapod, equipped with force sensors at each leg and actuated by pneumatically driven actuators. A real-time controller (LabVIEW platform) manages Hexapod motion and closed-loop damping via force feedback, optionally integrating with computational fluid dynamics (CFD) simulation (FLOW-3D) for hybrid co-simulation (Jetzschmann et al., 2018).

AI-Driven Water Networks:

The ACWA testbed supports multiple topologies (line, bus, star, soil-integrated) with modular physical tanks (ranging from 3–53 L), DC diaphragm pumps, manual valves, diverse sensors (pH, EC, flow, pressure, turbidity), and multi-protocol gateways (ZigBee, Modbus, LoRa). A digital-twin simulator, integrated via Node-RED and MongoDB, synchronizes simulated states, live sensor streams, and AI-based control logic (Batarseh et al., 2023).

Component	Process/PUF HIL	Slosh Damping	ACWA AI Testbed
Tank	Sim (300 mm)	600–1100 L, Cylindrical	3–53 L, Rectangular/Cubic
Controller	OpenPLC (Raspberry Pi 4)	LabVIEW PC (200 Hz)	Node-RED + Python AI
Sensors	Level (virtual), Voltage	6x force, internal accel	EC, DO, pH, pressure, ultrasonic
Simulation	Factory IO, Simulink PUF	FLOW-3D, SimMechanics	ACWA digital twin (Python)
Comm. Protocols	Modbus-TCP	Ethernet	Modbus, ZigBee, LoRa, MQTT

2. Physical and Simulation Parameters

Testbed geometry, instrumentation rates, and actuator characteristics are tailored to scenario specifics.

• Process HIL (PUF):
  • Circular water tank: diameter = 0.283 m, height = 0–0.300 m, cross-sectional area A = 0.0628 m²
  • Drain orifice area a = 2.5 × 10⁻⁴ m², inflow (on/off) = 3.5 × 10⁻⁵ m³/s
  • PLC scan rate: 65 ms (~15.4 Hz), PUF poll+response <11 ms total, HMI update: 0.5–2 s (Oun et al., 25 Jan 2026)
• Slosh Damping:
  • Tank filled to 600 or 1100 L, mounted on Hexapod (max ±0.04 m stroke, ±20° tilt)
  • Sensors: 6x HBM U10M-5 kN force, data acquisition 200 Hz, feedback loop ~15 ms delay (Jetzschmann et al., 2018)
• ACWA:
  • Topologies: line (3×37.8 L), bus (4×20.8 L), star (1×53 L+4×11.4 L), soil (drip pots + drains)
  • Sampling: EC/pH/DO 1–30 s, pressure/flow/turbidity/nitrate ~1 Hz, level 1–60 s

3. Mathematical Modeling and Control

Each testbed encodes core hydrodynamic equations and real-time feedback strategies specific to its research thrust.

Process/Authentication HIL:

• Nonlinear ODE:

$$\frac{d\,h(t)}{d\,t} = \frac{K_p}{A}\left(u(t) - a\sqrt{2g\,h(t)}\right)$$

• Linearized transfer function around $h_0$: $$G(s) = \frac{K_p}{A\,(s + \alpha)}, \qquad \alpha = \frac{a}{2\sqrt{2g\,h_0}}$$ This models first-order inertial tank response with nonlinear outflow (Oun et al., 25 Jan 2026).

Slosh Damping HIL:

• Mass–spring–damper model:

$$\begin{aligned} m_s \ddot{x}_s &= -k(x_s - x_h) - c(\dot{x}_s - \dot{x}_h) + F_d \ m_r \ddot{x}_h &= +k(x_s - x_h) + c(\dot{x}_s - \dot{x}_h) + F_h \end{aligned}$$

• State-space: $\dot{x} = A\,x + B\,u$, $y = C\,x + D\,u$
• Closed-loop transfer function for reaction force: $$G(s) = \frac{F_{sp}(s)}{a(s)} = \frac{k + cs}{m_s m_r s^4 + \ldots + k^2}$$ Robust control applied via $\mu$-synthesis and parametric $H_\infty$ optimization (Jetzschmann et al., 2018).

AI and Digital-Twin HIL (ACWA):

• Hydraulic balance: $dH/dt = (Q_{in} - Q_{out}) / A$
• Bernoulli, Darcy-Weisbach, and advection-reaction ODEs for transport and quality:

$$\frac{\partial C}{\partial t} + v \frac{\partial C}{\partial x} = -k_{react} C$$

Dynamic model parameters (pipe roughness, reaction rates) are calibrated via HIL coupling of simulated and measured data streams (Batarseh et al., 2023).

4. Data Flows, Communications, and Integration

Real-time, multi-protocol communication is critical for effective HIL operation. Key integration patterns include:

• Modbus-TCP for industrial automation controller and emulator communication (e.g., OpenPLC, Simulink PUF, HMI Python) (Oun et al., 25 Jan 2026).
• High-throughput data paths: Up to 200 Hz data acquisition (LabVIEW–Hexapod), with co-simulation at 50 Hz (FLOW-3D) (Jetzschmann et al., 2018).
• Multi-protocol IoT: ACWA leverages ZigBee, Modbus, LoRa for sensors and actuators, with Node-RED as a message broker and MongoDB for time-series storage. Tight synchronization (NTP, ±100 ms) mitigates controller drift (Batarseh et al., 2023).
• Digital-twin feedback: Simulators predict water levels and species concentrations; discrepancies are used in real time to refine control commands or initiate fallback actuations in the event of communication disruptions.

5. Validation and Performance Metrics

Comprehensive HIL-based testing spans normal operation and adversarial or faulted regimes.

• Authentication/PUF HIL:
  • 5.18 h operation ($N=286{,}281$ samples): 99.97% PUF accuracy, false-positive rate 0.025%, voltage-PUF failures (72 samples), temporal-PUF failures (0)
  • Sliding window: ≥99.94% accuracy in every 30-min interval
  • Faults: Spike (4.6–4.9 s) and hard-over (60–88 s) detected in <1 PLC cycle; hardware-trojan bias flagged during/preceding tank overflow
  • End-to-end PUF latency: $\sim$11 ms, full PLC+PUF handshake <100 ms (Oun et al., 25 Jan 2026)
• Active Damping HIL:
  • Open-loop Q $\approx 10$ (damping $\approx$ 5 cycles to 90% decay), closed-loop damps within 2 cycles ($\sim$3 s) for both 600 L and 1100 L, peak force attenuation from $\pm$200 N to $\pm$20 N
  • Actuator accelerations $\leq$0.4 g, outer velocity loop prevents platform drift
  • Frequency domain: 20 dB attenuation at slosh mode, nominal closed-loop bandwidth 5 rad/s (Jetzschmann et al., 2018)
• ACWA Digital-Twin & AI:
  • Digital-twin validation in line topology (3.5 gpm inflow): $<$0.02 m RMS error tank-level (1 s updates)
  • Cyberbiosecurity: AI-based anomaly detection flagged $\geq$95% of injected nitrate sensor faults within 5 s; resilience to DoS via digital-twin fallback
  • RL-based pump scheduling realized $\sim$12% run-time reduction; AI-based PID achieved tighter level control than PI alone (Batarseh et al., 2023)

6. Applications and Lessons Learned

HIL water tank testbeds provide benchmark infrastructures for research in several domains:

• Security and Fault Diagnosis: PUF-based HIL architectures enable real-time authentication, sensor degradation tracking, and rapid anomaly detection, with minimal legacy plant disruption (Oun et al., 25 Jan 2026).
• Robust Fluid-Structure Control: Force-feedback and $\mu$-synthesis are effective for active fluid slosh damping, relevant for aerospace and transportation; collocated force sensing and structured uncertainty models are crucial (Jetzschmann et al., 2018).
• AI-Augmented Water Networks: Digital twin integration with IoT sensors enables real-time calibration, predictive analytics, cyber-physical anomaly detection, AI control, and experiment repeatability (Batarseh et al., 2023).

Key design takeaways include the importance of collocated force sensing, high-frequency data acquisition, integration of digital-twin feedback, and modular topologies for reconfigurability. Low-latency communication, sensor redundancy, and systematic model calibration are essential for robust real-world emulation and controller validation.

7. Future Directions

Research trajectories for HIL water tank testbeds now emphasize:

• Integration of motorized valves, FPGA-based controllers enabling sub-100 ms feedback loops, and standardized digital interfaces (OPC UA, SAMPLETs) to support broad interoperability and timing guarantees (Batarseh et al., 2023).
• Incorporation of advanced physically unclonable function (PUF) circuits and extended fault-injection scenarios for supply chain assurance (Oun et al., 25 Jan 2026).
• Adaptation to micro-gravity slosh control (e.g., ISS) via fill-level tuning, further digital-twin/AI fusion, and enhanced structured robustness frameworks (Jetzschmann et al., 2018).
• A plausible implication is that HIL methodologies will become central for cyber-physical risk assessment, digital-twin validation, and learning-based adaptive control in utility and industrial automation contexts.

The reproducibility and extensibility of these testbeds, as detailed in the respective references, provide essential blueprints for both foundational research and practical system deployment.