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Dynamic and steady-state averaged models of an aircraft's power systems are developed to better understand various aspects of operation such as coupling between energy domains, energy requirements, transient and fault conditions/recovery, power quality/reliability, system and subsystem-level losses, operational efficiency, and to explore numerous system architectures. These models focus on the electrical, thermal, hydraulic, mechanical, and pneumatic power systems traditionally found in commercial aircraft. Subsystems of components are capable of interfacing with each other and with an engine model that produces auxiliary power. One of the major challenges involved in modeling extensive systems of this nature is to arrive at an optimum trade-off between simulation execution time and the desired level of fidelity in the result.

This paper presents a nonlinear control approach to achieve good performances in vehicle path following and collision avoidance when the vehicle is driving under cruise highway conditions. Nonlinear model predictive control (NLMPC) is adopted to achieve online trajectory control based on a simplified vehicle model. GMRES/Continuation algorithm is used to solve the online optimization problem. Simulations show that the proposed controller is capable of tracking the desired path as well as avoiding the obstacles.

As the cost and complexity of modern aircraft systems advance, emphasis has been placed on model-based design as a means for cost effective subsystem optimization. The success of the model-based design process is contingent on accurate prediction of the system response prior to hardware fabrication, but the level of fidelity necessary to achieve this objective is often called into question. Identifying the key benefits and limitations of model fidelity along with the key parameters that drive model accuracy will help improve the model-based design process enabling low cost, optimized solutions for current and future programs. In this effort, the accuracy and capability of a vapor cycle system (VCS) model were considered from a model fidelity and parameter accuracy standpoint. A range of model fidelity was evaluated in terms of accuracy, capability, simulation speed, and development time.

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 AFRL (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.

Automotive air conditioning systems are subject to constantly changing operation conditions and steady state simulations are not sufficient to describe the actual performance. The refrigerant mass migration during transient events such as clutch-cycling or start-up has a direct impact on the transient performance. It is therefore necessary to develop simulation tools which can accurately predict the migration of the refrigerant mass. To this end a dynamic model of an automotive air conditioning system is presented in this paper using a switched modeling framework. Model validation against experimental results demonstrates that the developed modeling approach is able to describe the transient behaviors of the system, and also predict the refrigerant mass migration among system components during compressor shut-down and start-up (stop-start) cycling operations.

Selective catalytic reduction (SCR) of NOx is coming into worldwide use for automotive diesel emissions control. To meet the most stringent standards, NOx conversion efficiency must exceed 80% while NH3 emissions or slip must be kept below 10-30 ppm. At such high levels of performance, non-uniformities in ammonia-to-NOx ratio (ANR) at the converter inlet can limit the achievable NOx reduction. Despite its significance, this effect is frequently ignored in 1D catalyst models. The corresponding model error is important to system integration engineers because it affects system sizing, and to control engineers because it affects both steady-state and dynamic SCR converter performance. A probability distribution function (PDF) based method is introduced to include mixture non-uniformity in a 1D, real-time catalyst model.

Despite the fact that urea-based selective catalytic reduction (SCR) of NOx is a key technology for achieving on- and off-highway diesel emission standards, significant control challenges remain. Transient operation, combined with dramatic changes in catalyst dynamics over the operating range, cause highly nonlinear system behavior. Moreover, these effects depend on catalyst formulation and new catalysts continue to be developed. With many controllers, any difference in catalyst formulation, converter size, and engine emissions calibration require control system re-tuning. To minimize control development effort, this paper presents a novel “generic” controller for SCR systems. Control action is grounded in a physics-based, nonlinear, embedded model. Through the model, controller parameters are adjusted a priori for catalyst formulation and converter size. The few remaining tuning levers are quite intuitive, and require no special knowledge of controls theory.

Selective catalytic reduction (SCR) is allowing diesel engines to reach NOx emission levels which are unachievable in-cylinder. This technology is still evolving, and new catalyst formulations which provide higher performance and greater durability continue to be developed. Usually, their performance is measured on a flow reactor using ammonia as the reductant. However, in mobile applications a urea-water solution is used instead, and urea decomposition by thermolysis and hydrolysis provides the required ammonia to the catalyst. It is well known that urea decomposition is incomplete by the inlet face of the converter, and this is at least one reason why on-engine performance is generally lower than would be expected from reactor tests. Previous modeling of urea-water droplets has focused on developing detailed sub-models that can be implemented into computational fluid dynamics (CFD) codes.

Selective catalytic reduction (SCR) of NOx is coming into widespread use for diesel exhaust emissions control in passenger cars, light trucks, and commercial vehicles. Because of the transient nature of these applications, modeling is a critical element of the controls development process. For software-in-the-loop simulation, the model must run in real-time while still retaining first order accuracy. Furthermore, if used as an embedded system or nonlinear observer, the allowable time step must not be shorter than the control module clock rate. Unfortunately, the time scales for ammonia storage decrease exponentially with temperature. The end result is a trade-off between spatial resolution, real-time performance, and temperature range. If the SCR catalyst is placed downstream of a particulate filter, this issue is even more acute due to the high temperatures that occur during regeneration. A switched catalyst model is proposed that breaks this trade-off.

This paper describes a ‘toolbox’ for modeling liquid cooling system networks within vehicle thermal management systems. Components which can be represented include pumps, coolant lines, control valves, heat sources and heat sinks, liquid-to-air and liquid-to-refrigerant heat exchangers, and expansion tanks. Network definition is accomplished through a graphical user interface, allowing system architecture to be easily modified. The elements of the toolbox are physically based, so that the models can be applied before hardware is procured. The component library was coded directly into MATLAB / SIMULINK and is intended for control system development, hardware-in-the-loop (HIL) simulation, and as a system emulator for on-board diagnostics and controls purposes. For HIL simulation and on-board diagnostics and controls, it is imperative that the model run in real-time.

This paper presents experimental validation of a dynamic vapor compression cycle model specifically suited for multivariable control design. A moving-boundary lumped parameter modeling approach captures the essential two-phase fluid dynamics while remaining sufficiently tractable to be a useful tool for designing low-order controllers. The key contribution of the research is the application of the moving-boundary models to automotive vapor compression cycles. Recent additions to the available moving-boundary models allow for the simulation of automotive systems. This work demonstrates that the moving-boundary models are sufficiently accurate to serve as analysis and control design tools for systems which experience extreme transients, such as automotive air-conditioning systems.

This paper presents an experimental analysis of the performance of various control strategies applied to automotive air conditioning systems. A comparison of the performance of a thermal expansion valve (TEV) and an electronic expansion valve (EEV) over a vehicle drive cycle is presented. Improved superheat regulation and minor efficiency improvements are shown for the EEV control strategies. The efficiency benefits of continuous versus cycled compressor operation are presented, and a discussion of significant improvements in energy efficiency using compressor control is provided. Dual PID loops are shown to control evaporator outlet pressure while regulating superheat. The introduction of a static decoupler is shown to improve the performance of the dual PID loop controller. These control strategies allow for system capacity control, enabling continuous operation and achieving significant energy efficiency improvements.

Compressor rapid cycling has been shown to be capable of delivering the advantages of variable capacity control without the use of variable speed compressors. For automotive air conditioning systems, rapid cycling can be achieved by engaging and disengaging the clutch drive. However, rapid cycling results in oscillations in evaporator superheat which degrade system performance and may damage the compressor. This paper discusses the dynamics associated with compressor rapid cycling and possible system configurations and control strategies for modulating the expansion valve to regulate superheat during rapid cycling operation. These strategies include feedback control strategies such as thermostatic expansion valve (TXV), and PI control, as well as feedforward control strategies. The feedback control strategies regulate the average superheat temperature, but fail to eliminate the oscillations caused by rapid cycling.

This paper presents a dynamic model of a transcritical air-conditioning system, specifically suited for multivariable controller design. The physically-based model retains sufficient detail to accurately predict system dynamic response while also being simple enough to be of value in determining appropriate control strategies. The control focus would be quasi-steady transitions between operating states by modulating flow rates of both air and refrigerant to meet changing constraints on capacity, efficiency, noise, etc. The model structure is highly modular, accommodating various system configurations and component types. The modeling results are programmed as a library of components for use in Simulink, a graphical programming package.