Justin P. Koeln

A Dynamic Modeling Toolbox for Air Vehicle Vapor Cycle Systems

Abstract : Modern air vehicles face increasing internal heat loads that must be appropriately understood in design and managed in operation. This paper examines one solution to creating more efficient and effective thermal management systems (TMSs): vapor cycle systems (VCSs). VCSs are increasingly being investigated by aerospace government and industry as a means to provide much greater efficiency in moving thermal energy from one physical location to another. In this work, we develop the Air Force Research Laboratory Transient Thermal Modeling and Optimization (ATTMO) toolbox: a modeling and simulation tool based in Matlab/Simulink that is suitable for understanding, predicting, and designing a VCS. The ATTMO toolbox also provides capability for understanding the VCS as part of a larger air vehicle system. The toolbox is presented in a modular fashion whereby the individual components are presented along with the framework for interconnecting them. The modularity allows for easy user re-configurability as well as the ability to scale from simple to full vehicle systems. A computational environment is discussed that allows for simulations running many times faster than real time. Simulation results are presented for a laboratory scale test stand system consisting of both single and multiple evaporators. The simulations are verified against experimental results demonstrating the potential of the tool.

Decentralized controller analysis and design for multi-evaporator vapor compression systems

The high degree of coupling in multi-evaporator vapor compression systems (ME-VCSs) makes independent control of the cooling capacity for each evaporator difficult. This paper examines the sources of coupling in these systems and the importance of this coupling when making control decisions. Structured singular value (SSV) analysis is used to quantify the degree of coupling in relation to the diagonal dominance of these systems. A 5-evaporator system model is used to demonstrate this analysis and shows that these systems are not diagonally dominant, suggesting poor decentralized control performance when individually controlling both cooling capacity and superheat. However, SSV analysis is used to show that decentralized control is capable of controlling cooling capacity if superheat is not included in the feedback. Both centralized and decentralized model predictive control (MPC) architectures were developed to verify the findings of the SSV analysis.

Scalable model predictive control for multi-evaporator vapor compression systems

Multi-evaporator vapor compression systems (ME-VCSs) are becoming widely used to meet the cooling needs for multiple thermal loads via a single system. As the number of evaporators increases, the size of these systems can make a centralized control approach impractical and computationally expensive; thus, motivating a decentralized control design. Linear gray-box modeling techniques show that ME-VCSs have a distinct underlying structure between the actuators and dynamic states known as a block arrow structure (BAS). This structure captures the high degree of coupling found in ME-VCSs, which can lead to poor decentralized control performance. This paper presents a partially decentralized model predictive control strategy which directly considers the coupling in the system when making control decisions by exploiting the BAS. The gray-box modeling approach and the decentralized nature of this BAS control strategy prove scalable to n-evaporator systems. Through simulated case studies, it is shown that this BAS control strategy can approximate the performance of a centralized control approach for ME-VCSs while significantly reducing computational costs.

Hierarchical Control of Multi-Domain Power Flow in Mobile Systems: Part I — Framework Development and Demonstration

This two-part paper presents the development of a hierarchical control framework for the control of power flow throughout mobile systems. These vehicles are comprised of multiple interconnected systems each with multiple subsystems which exhibit dynamics over a wide range of timescales. These interconnections and the timescale separation pose a significant challenge when developing an effective control strategy. Part I presents the proposed graph-based modeling approach and the three-level hierarchical control framework developed to directly address these interconnections and timescale separation. The mobile system is represented as a directed graph with vertices corresponding to the states of the vehicle and edges capturing the power flow throughout the vehicle. The mobile system and the corresponding graph are partitioned spatially into systems and subsystems and temporally into vertices of slow, medium, and fast dynamics. The partitioning facilitates the development of models used by model predictive controllers at each level of the hierarchy. A simple example system is used to demonstrate the approach. Part II utilizes this framework to control the power flow in the electrical and thermal systems of an aircraft. Simulation results show the benefits of hierarchical control compared to centralized and decentralized control methods.Copyright

Experimental validation of graph-based modeling for thermal fluid power flow systems

Model-based control design has the ability to meet the strict closed-loop control requirements imposed by the rising performance and efficiency demands on modern engineering systems. While many modeling frameworks develop controloriented models based on the underlying physics of the system, most are energy domain specific and do not facilitate the integration of models across energy domains or dynamic timescales. This paper presents a graph-based modeling framework, derived from the conservation of mass and energy, which captures the structure and interconnections in the system. Subsequently, these models can be used in model-based control frameworks for thermal management. This framework is energy-domain independent and inherently captures the exchange of power among different energy domains. A thermal fluid experimental system demonstrates the formulation of the graph-based models and the ability to capture the hydrodynamic and thermodynamic behaviors of a physical system.

Hierarchical control of multi-domain power flow in mobile systems - Part II: Aircraft application

This two-part paper presents the development of a hierarchical control framework for the control of power flow throughout large-scale systems. Part II presents the application of the graph-based modeling framework and three-level hierarchical control framework to the power systems of an aircraft. The simplified aircraft system includes an engine, electrical, and thermal systems. A graph based approach is used to model the system dynamics, where vertices represent capacitive elements such as fuel tanks, heat exchangers, and batteries with states corresponding to the temperature and state of charge. Edges represent power flows in the form of electricity and heat, which can be actuated using control inputs. The aircraft graph is then partitioned spatially into systems and subsystems, and temporally into fast, medium, and slow dynamics. These partitioned graphs are used to develop models for each of the three levels of the hierarchy. Simulation results show the benefits of hierarchical control compared to a centralized control method.Copyright

HVAC System Modeling and Control: Vapor Compression System Modeling and Control

In this chapter, we delve deeper into understanding modeling and control approaches for one of the important subsystems in an intelligent building, the HVAC system. Specifically, Vapor Compression Systems (VCS) are the primary energy systems in building air conditioning, heat pump, and refrigeration systems. We will discuss standard methods for constructing dynamic models of vapor compression systems, and their relative advantages for analysis, design, control design, and fault detection. The principal interests are moving boundary and finite-volume approaches to capture the salient dynamics of two-phase flow heat exchangers. We will present modeling approaches for auxiliary equipment, such as, valves, compressors, fans, dampers, and heating/cooling coils, allowing the reader to understand the construction of typical HVAC system models. We will then highlight limitations of such models and address advanced modeling approaches for challenging transient scenarios. Finally, we give a summary of single-input, single-output control strategies for HVAC system, with simulation and experimental examples to illustrate their effectiveness.

Graph-based hierarchical control of thermal-fluid power flow systems

To meet the rising performance and efficiency demands on high performance thermal management systems, this paper proposes a hierarchical model-based control framework for thermal-fluid power flow systems. This hierarchy uses scalable graph-based dynamic models of the hydrodynamics and thermodynamics of these systems, derived from conservation of mass and conservation of thermal energy, respectively. Leveraging the inherent timescale separation between thermal and hydraulic dynamics, a three-layer control hierarchy is constructed. The use of Model Predictive Control (MPC) at each layer allows actuator and state constraints to be explicitly considered and allows preview of upcoming thermal disturbances to be used for optimization. In addition, the hierarchy has functionality to account for actuator dynamics, including rate limits and time delays. The proposed control approach is demonstrated in simulation on a system configuration that is notionally representative of a simplified aircraft fuel thermal management system.

Optimal Subcooling in Vapor Compression Systems via Extremum Seeking Control

Building systems constitute a significant portion of the overall energy consumed each year in the U.S., and a large portion of this energy is used by air-conditioning systems. Therefore, the efficiency of these systems is important. This paper presents a method to increase system efficiency using an alternative system architecture for vapor compression systems. This architecture creates an additional degree of freedom which allows for independent control of condenser subcooling. It is found that there exists a non-zero subcooling that maximizes system efficiency; however, this optimal subcooling can change with different operating conditions. Thus, extremum seeking control is applied to find and track the optimal subcooling using only limited information of the system. In a simulation case study, a 10% reduction in energy consumption is reported when using the alternative system architecture and extremum seeking control when compared to a conventional system configuration.© 2013 ASME

Multi-zone Temperature Modeling and Control

In this chapter, we now address modeling and control for multi-zone and building-level temperature regulation. Many commercial and residential buildings have multiple independent zones requiring space conditioning. These zones may be uniform in size or may have a significant size distribution. Moreover, the space conditioning requirements may be quite disparate depending on the zone usage. This chapter provides an overview of multi-zone temperature control within buildings. We review modeling approaches for building zones and their interconnections. The primary modeling framework is a resistor–capacitor framework where thermal energy is stored in a zone in a capacitive sense and transferred between zones of different temperature through a thermally resistive path. The zones are fed by a cooling or heating system that could be liquid or air. Subsequently, we focus attention on control of multi-zone systems starting with an appropriate architecture for their representation. We present particular structures for energy flow that naturally decompose the dynamic properties of the building into hierarchical structures. These structures can then be used to create controllers or either a centralized or distributed variety. Simulations serve to illustrate the concepts and represent the results for both the modeling and dynamic control.