Impact of Suction and Injection Gas Superheat Degrees on The Performance of a Residential Heat Pump With Vapor Injection and Variable Speed Scroll Compressor

The market share of heat pump systems has grown significantly in Europe in the past decades and, in residential applications, air-source heat pumps (ASHP) are usually considered due to their relatively low cost. In the literature, it has been widely demonstrated that injection cycle can improve the system performance and operating range. This paper presents experimental results of an air-to-water residential heat pump using a variable speed scroll compressor with vapor injection. The first part of the paper focuses on the experimental results collected from a vapor injection and variable speed scroll compressor air-to-water residential heat pump. The unit is a 10 kW residential system working with R410a as working fluid and capable of providing floor heating and domestic hot water. It was tested in a controlled environment in order to achieve a wide range of outdoor and indoor conditions. The impact on the system performance of the vapor superheat degrees at both injection and suction ports is discussed. It is shown that a better control of these variables could improve the system COP and heating capacity by respectively 10 and 15%. It is also shown that the control of the superheat degrees is a coupled problem and the use of standard gain-scheduled SISO PIDs is not optimal. The second part of this paper presents a model of the system. Finite-volume models are used for the heat exchangers and the split lines. A thermodynamic model of the vapor injection scroll compressor is developed using empirical correlations for the volumetric efficiency, isentropic efficiency and the ratio between the injection and suction mass flow rates. A simple model is proposed for the four-way valve. Finally, a static decoupler-based controller is presented in order to take into account the coupling between both superheat degrees and shows increased performances compared to the SISO PID controller

Continuous-discrete time observer design for Lipschitz systems with sampled measurements

International audienceThis technical note concerns observer design for Lipschitz nonlinear systems with sampled output. Using reachability analysis, an upper approximation of the attainable set is given. When this approximation is formulated in terms of a convex combination of linear mappings, a sufficient condition is given in terms of linear matrix inequalities (LMIs) which can be solved employing an LMIs solver. This novel approach seems to be an efficient tool to solve the problem of observer synthesis for a class of Lipschitz systems of small dimensions

Waste heat recovery rankine cycle based system modeling for heavy duty trucks fuel saving assessment

peer reviewedThis paper presents a method to model heat exchangers used in waste heat recovery Rankine based systems in heavy duty trucks. The method is developed to predict both transfer and working fluid physical properties such as temperature and density after the heat exchange process. Due to the flexibility of such a model, it can be used for a numerous quantity of fluids including water-alcohol mixture. A validation of the developed model is shown and compared to steady state and dynamic test results. The model shows good performance as well in terms of accuracy, which is in the range of 5K error as in computational time which is faster than real time and makes it suitable for concept optimization, control and fuel economy evaluation

Transient performance evaluation of waste heat recovery Rankine cycle based system for heavy duty trucks

The study presented in this paper aims to evaluate the transient performance of a waste heat recovery Rankine cycle based system for a heavy duty truck and compare it to steady state evaluation. Assuming some conditions to hold, simple thermodynamic simulations are carried out for the comparison of several fluids. Then a detailed rst principle based model is also presented. Last part is focused on the Rankine cycle arrangement choice by means of model based evaluation of fuel economy for each concept where the fuels savings are computed using two methodologies. Fluid choice and concept optimization are conducted taking into account integration constraints (heat rejection, packaging . . . ). This paper shows the importance of the modeling phase when designing WHRS and yields a better understanding when it comes to a vehicle integration of a Rankine cycle in a truck

Optimal input design for parameter estimation of nonlinear systems: case study of an unstable delta wing

International audienceA closed-loop optimal experimental design for online parameter identification approach is developed for nonlinear dynamic systems. The goal of the observer and the nonlinear model predictive control theories is here to perform online computation of the optimal time-varying input and to estimate the unknown model parameters online. The main contribution consists in combining Lyapunov stability theory with an existing closed-loop identification approach, in order to maximise the information content in the experiment and meanwhile to asymptotically stabilise the closed-loop system. To illustrate the proposed approach, the case of an open-loop unstable aerodynamic mechanical system is discussed. The simulation results show that the proposed algorithm allows to estimate all unknown parameters, which was not possible according to previous work, while keeping the closed-loop system stable

Observer Design for Nonlinear Systems with Implicit Output

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Self-triggered control via dynamic high-gain scaling (long version)

This paper focuses on the construction of self-triggered state feedback laws. The approach followed is a high-gain approach. The event which triggers an update of the control law is based on an dynamical system which state is the high-gain parameter. This approach allows to design control laws ensuring convergence to the origin for nonlinear systems with triangular structure an a specific upper bound on the nonlinearities. This is a long version of a paper which has been published in NOLCOS 2016

Deep KKL: Data-driven Output Prediction for Non-Linear Systems

International audienceWe address the problem of output prediction, ie. designing a model for autonomous nonlinear systems capable of forecasting their future observations. We first define a general framework bringing together the necessary properties for the development of such an output predictor. In particular, we look at this problem from two different viewpoints, control theory and data-driven techniques (machine learning), and try to formulate it in a consistent way, reducing the gap between the two fields. Building on this formulation and problem definition, we propose a predictor structure based on the Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well into our general framework. Finally, we propose a constructive solution for this predictor that solely relies on a small set of trajectories measured from the system. Our simulations show that our solution allows to obtain an efficient predictor over a subset of the observation space