HVAC & Thermal Energy Systems
Dynamic behavior, design, and operation of heat pumps, vapor-compression systems, water heating, thermal storage, hydronic systems, and integrated thermal-energy technologies.
Research
Systems, intelligence, and validation.
ESIL combines thermal-fluid science, control and optimization, cyber-physical energy systems, sensing, and data-driven methods to improve the performance, flexibility, and reliability of building and thermal energy systems.
Research thrusts
Each thrust is designed to connect physical understanding with algorithms and measurable performance.
Advanced Control & Optimization
Model predictive control, supervisory control, control-oriented system identification, constrained optimization, and intelligent or autonomous operation.
Grid-Interactive Buildings & Demand Flexibility
Flexible building loads, load shifting, demand response, building-grid coordination, and control of HVAC and distributed energy resources to support grid needs.
Fault Detection, Diagnostics & Automated Correction
Detection and isolation of performance degradation, virtual sensing, model-based and data-driven diagnostics, and control actions that restore efficient operation.
Physics-Based, Data-Driven & AI-Enabled Modeling
Reduced-order thermal models, digital twins, machine learning, hybrid physics/data models, state and parameter estimation, and predictive analytics for control and decision-making.
Experimental Validation & Field Demonstration
Controlled-environment HVAC testing, sensor and data-acquisition development, laboratory controller validation, and performance evaluation in real buildings and energy systems.
Representative prior work
Research evidence across models, controls, diagnostics, and hardware.
These schematic summaries are adapted from Junke Wang's published research and research presentations. They illustrate recurring technical themes rather than a complete project portfolio.
Building modeling & MPC
From learned thermal dynamics to predictive control
Reduced-order thermal models and parameter estimation support optimization of HVAC operation using weather, price, comfort, and equipment constraints.
Representative of work published in Energy and Buildings, Applied Energy, and Building and Environment.
Fault detection & diagnostics
Low-cost sensing plus model-based performance monitoring
IoT-enabled measurements are compared with predicted evaporator and equipment performance to identify installation and degradation faults.
Representative of residential heat-pump FDD work published in Science and Technology for the Built Environment and ASHRAE proceedings.
Heat pumps & digital twins
Multi-functional heat-pump modeling, control, and validation
System-level models, controller logic, data acquisition, and laboratory measurements are combined to study coupled space-conditioning, water-heating, and heat-recovery operation.
Representative of multi-functional heat-pump work reported in ASHRAE Transactions/conference publications and subsequent control-sequence development.
Grid-interactive commercial HVAC
Controls embedded in real AHU/BAS environments
Virtual airflow metering, occupancy-responsive ventilation, and energy-feedback control illustrate how system models and control logic can be implemented in commercial HVAC systems.
Representative of published work on advanced HVAC demand control, virtual outdoor-air flow metering, and grid-interactive AHU control.
Research architecture
Two layers, one objective.
Our application layer focuses on physical thermal systems and flexible buildings. Our intelligence layer provides the models, optimization, controls, diagnostics, and experiments needed to improve them.
Application layer
Thermal systems & flexible buildings
Heat pumps, HVAC equipment, water heating, thermal storage, building thermal dynamics, and grid-interactive operation.
Intelligence layer
Models, controls, diagnostics & experiments
Dynamic modeling, AI and data analytics, optimization, MPC, fault diagnosis, sensing, hardware implementation, and validation.
Workflow
From first principles to measured performance.
Physics & data
→
Models & estimation
→
Optimization & control
→
Lab & field validation
Current directions
Near-term ESIL work emphasizes heat-pump controls and performance, demand flexibility, advanced diagnostics, and experimentally validated modeling and control methods.
Heat pumps & electrification
Control and optimization under variable loads, weather, equipment constraints, and multi-service operation.
Controls that transfer to hardware
Methods that can be tested with real sensors, actuators, controllers, and thermal equipment rather than remaining simulation-only.
Flexible & resilient operation
Use building thermal dynamics and controllable loads to provide flexibility while maintaining comfort, efficiency, reliability, and equipment-aware operation.