Improving the Accuracy and Scope of Control-Oriented Vapor Compression Cycle System Models

The benefits of applying advanced control techniques to vapor compression cycle systems are well know. The main advantages are improved performance and efficiency, the achievement of which brings both economic and environmental gains. One of the most significant hurdles to the practical application of advanced control techniques is the development of a dynamic system level model that is both accurate and mathematically tractable. Previous efforts in control-oriented modeling have produced a class of heat exchanger models known as moving-boundary models. When combined with mass flow device models, these moving-boundary models provide an excellent framework for both dynamic analysis and control design. This thesis contains the results of research carried out to increase both the accuracy and scope of these system level models. The improvements to the existing vapor compression cycle models are carried out through the application of various modeling techniques, some static and some dynamic, some data-based and some physics-based. Semiempirical static modeling techniques are used to increase the accuracy of both heat exchangers and mass flow devices over a wide range of operating conditions. Dynamic modeling techniques are used both to derive new component models that are essential to the simulation of very common vapor compression cycle systems and to improve the accuracy of the existing compressor model. A new heat exchanger model that accounts for the effects of moisture in the air is presented. All of these model improvements and additions are unified to create a simple but accurate system level model with a wide range of application. Extensive model validation results are presented, providing both qualitative and quantitative evaluation of the new models and model improvements.Air Conditioning and Refrigeration Project 17

Design of a Linear Time-Varying Cross-Coupled Iterative Learning Controller

In many manufacturing applications contour tracking is more important than individual axis tracking. Many control techniques, including iterative learning control (ILC), target individual axis error. Because individual axis error only indirectly relates to contour error, these approaches may not be very effective for contouring applications. Cross-coupled ILC (CCILC) is a variation on traditional ILC that targets the contour tracking directly. In contour trajectories with rapid changes, high frequency control is necessary in order to meet tracking requirements. This paper presents an improved CCILC that uses a linear time-varying (LTV) filter to provide high frequency control for short durations. The improved CCILC is designed for raster-scan tracking on a Cartesian robotic test platform. Analysis and experimental results are presented

A Learning Based Precool Algorithm for Utilization of Foodstuff as Thermal Energy Storage

Abstract — Maintaining foodstuff within predefined temper-ature thresholds is important due to legislative requirements and to sustain high foodstuff quality. This is achieved using a refrigeration system. However, these systems might not be dimensioned for hot summer days or possible component per-formance degradation. A learning based algorithm is proposed in this paper, which precools the foodstuff in an anticipatory manner based on the saturation level in the system on recent days. The method is evaluated using a simulation model of a supermarket refrigeration system and simulations show that thermal energy can be stored in foodstuff to cope with saturation in refrigeration equipment. Additional hardware or a system model is not required, making it easy to implement the method in existing systems. I

High Bandwidth Control of Precision Motion Instrumentation

This article presents a high-bandwidth control design suitable for precision motion instrumentation. Iterative learning control (ILC), a feedforward technique that uses previous iterations of the desired trajectory, is used to leverage the repetition that occurs in many tasks, such as raster scanning in microscopy. Two ILC designs are presented. The first design uses the motion system dynamic model to maximize bandwidth. The second design uses a time-varying bandwidth that is particularly useful for nonsmooth trajectories such as raster scanning. Both designs are applied to a multiaxis piezoelectric-actuated flexure system and evaluated on a nonsmooth trajectory. The ILC designs demonstrate significant bandwidth and precision improvements over the feedback controller, and the ability to achieve precision motion control at frequencies higher than multiple system resonances

Graphene-Based Electromechanical Thermal Switches

Thermal management is an important challenge in modern electronics, avionics, automotive, and energy storage systems. While passive thermal solutions (like heat sinks or heat spreaders) are often used, actively modulating heat flow (e.g. via thermal switches or diodes) would offer additional degrees of control over the management of thermal transients and system reliability. Here we report the first thermal switch based on a flexible, collapsible graphene membrane, with low operating voltage, < 2 V. We also employ active-mode scanning thermal microscopy (SThM) to measure the device behavior and switching in real time. A compact analytical thermal model is developed for the general case of a thermal switch based on a double-clamped suspended membrane, highlighting the thermal and electrical design challenges. System-level modeling demonstrates the thermal trade-offs between modulating temperature swing and average temperature as a function of switching ratio. These graphene-based thermal switches present new opportunities for active control of fast (even nanosecond) thermal transients in densely integrated systems

Dynamic Modeling, Control, and Fault Detection in Vapor Compression Systems

According to the US Department of Energy (www.energy.gov), air conditioning and refrigeration (AC&R) applications account for approximately one third of the total electrical use in US homes and commercial buildings. The massive energy consumption of AC&R systems provides both economic and environmental motivation to develop highly efficient systems that maintain operating efficiency through the use of online diagnostic modules. Recent developments in actuator technologies (i.e. variable speed and variable displacement compressors, electronic expansion valves) managed by proper control architectures can capitalize on the efficiency gains obtained through continuous system operation, enabling significant efficiency gains to be realized in vapor compression systems. The addition of fault detection and diagnosis (FDD) algorithms to the overall control architecture provides the capability to maintain a high level of system performance over its lifetime of operation. This thesis makes contributions to both the effective transient control and diagnostic capability of vapor compression systems. Accurate control-oriented models that balance simplicity and accuracy are leveraged to improve the understanding of system level fault impact. The general control architecture for vapor compression systems is analyzed from a design perspective, and effective methods to achieve improved system performance and capacity control are demonstrated. Additionally, a discussion of the presence of dynamic fault signatures that may enable improved detection and identification is presented for a subset of system faults.Air Conditioning and Refrigeration Project 17

Dynamic Modeling and Advanced Control of Air Conditioning and Refrigeration Systems

Over 15 billion dollars is spent on energy for residential air-conditioning alone each year, and air conditioning remains the largest source of peak electrical demand. Improving the efficiency of these systems has the potential for significant economic and environmental impact, but requires not only refining individual component designs, but increasing overall system efficiency using advanced control strategies. Transient control of vapor compression cycles faces two significant challenges: 1) creating control-oriented models that balance simplicity with accuracy, and capture the complex heat and mass flow dynamics, and 2) developing control strategies that can achieve high performance over a wide range of operating conditions. This dissertation makes contributions on both fronts and can be divided into two distinct parts. The first portion of the dissertation presents the development, simulation, and experimental validation of a first principles modeling framework that captures the dynamics of a variety of vapor compression cycles in a form amenable to controller design. These models are highly nonlinear, and require a nonlinear control strategy to attain high performance over the entire operating envelope. To this end, a gain-scheduled control approach based on local models and local controllers is presented that uses endogenous scheduling variables. This comprises the second portion of the dissertation, where a theoretical framework for designing gain scheduled controllers, tools for analyzing the stability of the nonlinear closed loop system, and experimental evaluation of advanced control strategies for vapor compression systems is presented. These results demonstrate that while linear control techniques offer significant advantages versus traditional a/c control systems over small ranges, the gain-scheduled approach extends these advantages over the entire operating regime.Air Conditioning and Refrigeration Project 16