Neuro-QFLC: A Hybrid Neural-Fuzzy Controller for Safe, Real-Time EHD Thermal Regulation
| International Journal of Mechanical Engineering |
| © 2026 by SSRG - IJME Journal |
| Volume 13 Issue 2 |
| Year of Publication : 2026 |
| Authors : M. Lavanya, S. Mathankumar |
10.14445/23488360/IJME-V13I2P104
How to Cite?
M. Lavanya, S. Mathankumar, "Neuro-QFLC: A Hybrid Neural-Fuzzy Controller for Safe, Real-Time EHD Thermal Regulation," SSRG International Journal of Mechanical Engineering, vol. 13, no. 2, pp. 33-46, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I2P104
Abstract:
Electrohydrodynamic (EHD) cooling provides compact, fanless thermal management; however, its advantages are closely linked to Joule heating and current-density thresholds that may induce dangerous temperature spikes during workload surges. To introduce Neuro-QFLC, a hybrid controller that integrates a neural surrogate for thermo-electrohydrodynamic dynamics with a Quantum-inspired Fuzzy Logic Controller (QFLC) and a constraint-aware safety filter. The neural surrogate predicts the next peak temperature and the amount of uncertainty that can be calibrated. The QFLC encodes rules for interpreting error trends, and a quadratic-program safety layer uses a log barrier and CVaR-based tail cushions to enforce temperature and current limits. In a representative testbed, Neuro-QFLC gets a temperature RMSE of 0.42 K, a peak overshoot of 1.8%, and a violation rate of 0.12%, all while keeping 6.1 K steady-state headroom at a loop latency of about 9.8 ms (with GPU support). This is better than tuned PID, classical FLC, and a neural-only baseline. Calibration makes predictions more reliable (ECE 0.029, Coverage@95% 95.7%), which makes it possible to use principled risk buffers. Stress testing with sensor noise, dropouts, ageing drift, and external heat pulses shows graceful degradation (violations ≤ 0.41%, recovery ≤ 9.5 s). Ablations show that components are needed: taking away the safety QP raises violations by +1.97%, and taking away CVaR raises tail breaches by +0.54%. These results show that Neuro-QFLC is a practical, understandable, and provably safer way to achieve high-performance EHD thermal regulation that can be used in embedded edge deployment.
Keywords:
Electrohydrodynamic, Quantum-inspired Fuzzy Logic Controller, Constraint-aware safety filter, Joule-heating, Thermal management.
References:
[1] Afshin Shaygani, Kazimierz Adamiak, and Mehrdad R. Kermani, “Precision Airflow Control via EHD Actuator: A Co-Simulation and Control Design Case Study,” IEEE Transactions on Plasma Science, vol. 52, no. 8, pp. 3153-3165, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Boyu Li, Kang Luo, and Jian Wu, “Controlling the Heating Wall Temperature during Melting via Electric Field,” International Communications in Heat and Mass Transfer, vol. 159, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] R. Deepak Selvakumar, and Ahmed K. Alkaabi, “Design and Performance Evaluation of a Novel Electrohydrodynamically Enhanced PCM Heat Sink,” Journal of Energy Storage, vol. 94, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] An Li et al., “Heat Transfer Performance and Electrohydrodynamic Characteristics Optimization of a Dividing-wall-type Ionic Wind Heat Exchanger,” International Journal of Heat and Mass Transfer, vol. 228, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] V. Navaneethakrishnan, M. Muthtamilselvan, and Eunseop Yeom, “Impact of Electro-Hydrodynamics on Combined Convection in an Opposed Ventilation System,” Physics of Fluids, vol. 36, no. 9, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Masahito Nishikawara et al., “Demonstration of Heat Switch Function of Loop Heat Pipe Controlled by Electrohydrodynamic Conduction Pump,” Applied Thermal Engineering, vol. 249, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Motorin Oleg, Bologa Mircea, and Podlesny Igor, “Improving the Performance of Evaporation-Condensation Systems in an Electric Field,” Materials Science and Condensed Matter Physics, 2024.
[Google Scholar] [Publisher Link]
[8] Leonard L. Vasiliev et al., “Autonomous Devices with an Evaporation–Condensation Cycle for Thermal Control of Heat-Loaded Equipment,” Surface Engineering and Applied Electrochemistry, vol. 60, no. 4, pp. 650-657, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yuzhe Wang, and Xinquan Liang, “Soft Pumps: Catalysts for Advancing Fluid‐Driven Soft Robots,” Advanced Engineering Materials, vol. 26, no. 14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Galal M. Moatimid, Mohamed F. E. Amer, and Doaa A. Ibrahim, “EHD Instability of a Cylindrical Interface Separating Two Couple-Stress Fluids,” Scientific Reports, vol. 14, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Xue-Lin Gao et al., “The Electrohydrodynamic Enhancement of Heat Transfer on Interdigitated Electrodes by a Charge Injection Pump,” Physics of Fluids, vol. 36, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Nilesh Gedam et al., “Investigation of Heat Transfer Enhancement Techniques in Microchannel Heat Sinks,” Nanotechnology Perceptions, vol. 20, no. 6, pp. 2663-2671, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ahmet Polat, “Analysis of Drying Characteristics and Quality Attributes in Peach Slices Dried via Electrohydrodynamic, Hot Air and Electrohydrodynamic-hot Air Methods,” Journal of Thermal Analysis and Calorimetry, vol. 149, no. 14, pp. 7551-7563, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Xuehang Bai et al., “Novel Soft Robotic Finger Model Driven by Electrohydrodynamic (EHD) Pump,” Journal of Zhejiang University-SCIENCE A, vol. 25, pp. 596-604, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chakrit Suvanjumrat et al., “Assessment of the Pineapple Drying with a Forced Convection Solar-Electrohydrodynamic Dryer,” Case Studies in Thermal Engineering, vol. 59, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ji-chen Ma et al., “Experimental and Numerical Analysis of Thermal Performance in Electrohydrodynamic (EHD) Enhanced Heat Exchangers,” Journal of Enhanced Heat Transfer, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Xue-Lin Gao et al., “Flow Control and Heat Transfer Enhancement of Modular Electrohydrodynamic Pump,” Physics of Fluids, vol. 37, no. 9, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ju-Hun Ahn, Chae-Rim Yoon, and Chang-Yull Lee, “Heating Simulation of Film Heaters Fabricated by EHD Inkjet Printing for Satellite Applications,” Advanced Materials Interfaces, vol. 12, no. 14, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Jing Wang et al., “Flow Distribution Regulation in Microchannels for Emitter Side-mounted Ionic Wind Heat Sink and its Implication for High-efficiency Thermal Management,” International Journal of Heat and Mass Transfer, vol. 249, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Tahar Tayebi et al., “Artificial Neural Network Prediction of an Electrohydrodynamic Thermosolutal Buoyancy-driven Convection of NEPCMs-dielectric Suspension within an Oblique Enclosure with Active Blocks,” International Communications in Heat and Mass Transfer, vol. 169, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] S.A. Ohid et al., “Application of an Active Heat Transfer Amelioration Technique to a Dielectric Non-Newtonian Fluid: Machine Learning Approach,” International Communications in Heat and Mass Transfer, vol. 169, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[22] B. Rajalakshmi et al., Automated Spam Detection Using ECSA-Based Feature Selection with BGRN Classifier in Soft Computing Applications, Soft Computing in Industry 5.0 for Sustainability, pp. 225-244, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Shruti Patil et al., “Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification,” Information Systems Engineering, vol. 29, no. 5, pp. 2021-2030, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Arunadevi Thirumalraj, R. J. Anandhi, and V. Revathi, Supply Chain Management Using Fermatean Fuzzy-Based Decision Making with ISSOA, Convergence of Industry 4.0 and Supply Chain Sustainability, IGI Global Scientific Publishing, pp. 296-318, 2024.
[CrossRef] [Google Scholar] [Publisher Link]