A Novel Aircraft Air Conditioning System with a Sterilization Unit by Ultra-High-Temperature Air Stream

An aircraft cabin is a narrow,closed-space environment.To keep the air quality in cabin healthy for passengers,especially during an epidemic such as SARS-CoV-2(or 2019-nCoV)in 2020,a novel aircraft air conditioning system,called the ultra-high-temperature instantaneous sterilization air conditioning system(UHTACS),is proposed.Based on the proposed system,a simulation of the UHT-ACS is analysed in various flight states.In the UHT-ACS,the mixing air temperature of return and bleed air can reach temperature up to 148.8°C,which is high enough to kill bacilli and viruses in 2一8 s.The supply air temperature of the UHT-ACS in a mixing cavity is about 12 C in cooling mode both on the ground and in the air.The supply air temperature is about 42 C in heating mode.Compared with the air conditioning systems(ACS)of traditional aircraft the supply air temperatures of the UHT-ACS in the mixing cavity are in good agreement with those of a traditional ACS with 60%fresh air and 40%return air.Furthermore the air temperature at the turbine outlet of the UHT-ACS is higher than that of a traditional ACS which will help to reduce the risk of icing at the outlet.Therefore the UHT-ACS can operate normally in various flight states.

Analyses of an air conditioning system with entropy generation minimization and entransy theory

In this paper,based on the generalized heat transfer law,an air conditioning system is analyzed with the entropy generation minimization and the entransy theory.Taking the coefficient of performance（denoted as COP） and heat flow rate Qout which is released into the room as the optimization objectives,we discuss the applicabilities of the entropy generation minimization and entransy theory to the optimizations.Five numerical cases are presented.Combining the numerical results and theoretical analyses,we can conclude that the optimization applicabilities of the two theories are conditional.If Qout is the optimization objective,larger entransy increase rate always leads to larger Qout,while smaller entropy generation rate does not.If we take COP as the optimization objective,neither the entropy generation minimization nor the concept of entransy increase is always applicable.Furthermore,we find that the concept of entransy dissipation is not applicable for the discussed cases.

Effect of Two Kinds of Similarity Factors on Principal Component Analysis Fault Detection in Air Conditioning Systems

Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.

Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

Principal component analysis(PCA)has been already employed for fault detection of air conditioning systems.The sliding window,which is composed of some parameters satisfying with thermal load balance,can select the target historical fault-free reference data as the template which is similar to the current snapshot data.The size of sliding window is usually given according to empirical values,while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied.The air conditioning system is a dynamic response process,and the operating parameters change with the change of the load,while the response of the controller is delayed.In a variable air volume(VAV)air conditioning system controlled by the total air volume method,in order to ensure sufficient response time,30 data points are selected first,and then their multiples are selected.Three different sizes of sliding windows with 30,60 and 90 data points are applied to compare the fault detection effect in this paper.The results show that if the size of the sliding window is 60 data points,the average fault-free detection ratio is 80.17%in fault-free testing days,and the average fault detection ratio is 88.47%in faulty testing days.

Energy Efficient Air Conditioning System Using Geothermal Cooling-Solar Heating in Gujarat, India

It is well known that one unit of electrical energy saved is equal to more than two units produced. One way of economizing the power is utilization of energy efficient systems at all locations. In the present study, the air conditioning system is analysed and an innovative way is suggested. We use natural low temperature of shallow sub surface (1 - 3 m) of the earth—geothermal cooling system. It is known that majority of the households and the apartment complexes in India have two tanks for water storage. One is the underground water sump and the other is the overhead water tank. In our study, we use these two water storage systems for space cooling during summer and also for heating during winter. The main aim of our paper is air-conditioning of the space in an economic way to save electricity. It is based on a simple idea of transferring the low temperature from underground water sump to the room in the house using water as a mode of transport. Since India is a tropical country located at low latitude, most of the year, the air temperature is high and demands space cooling. However, for a couple of months during severe winter months (Dec.-Jan.) at Ahmedabad, heating of the space is required. For heating the space, we suggest to use the well-known solar water heater. Effective use of heat exchanger is shown through computation, modelling schemes and lab experiment. We recommend geothermal cooling for 10 months in a year and solar hot water system during 2 months of winter. It is observed that the ambient air temperature of 35°C - 40°C in the room can be brought down to 26°C without much consumption of electricity. In a similar manner, the room temperature at night (13°C) during winter in Ahmedabad can be increased to 27°C through circulation of water from solar water heater in the heat exchanger.

Metal hydride work pair development and its application on automobile air conditioning systems

Aiming at developing exhaust gas driving automobile air conditioning systems, a hydride pair LaNi4.61Mn0.26A10.13/ La0.6Y0.4Ni4.8Mn0.2 was developed working at 393-473 K/293-323 K/263-273 K. Property tests showed that both alloys have flat plateau slopes and small hystereses; system theoretical coefficient of performance （COP） is 0.711. Based on this work pair, a function proving automobile metal hydride refrigeration system was constructed. The equivalent thermal conductivities of the activated reaction beds were merely 1.1-1.6 W/（m-K）, which had not met practical requirement. Intermittent refrigeration cycles were achieved and the average cooling power was 84.6 W at 423 K/303 K/273 K with COP being 0.26. By altering cycling parameters, experiment data showed that cooling power and system COP increase with the growth of heat source temperature as well as pre-heating and regeneration time while decrease with heat sink temperature increment. This study confirms the feasibility of automobile metal hydride refrigeration systems, while heat transfer properties of reaction beds still need to be improved for better performance.

Modelling, Transient Simulation and Economic Analysis of Solar Thermal Based Air Conditioning System in Gujarat

Commercial building sector accounts for 8% of the total electricity consumption in India. Cooling activities (HVAC) in commercial buildings consume 55% of the total energy utilized. Consequently, CO2 emissions from conventional buildings in India were estimated to be 98 metric tonnes of CO2 per million ft2 in 2014. Solar thermal air conditioning can be the solution to these demands and can contribute to about 15% to 20% of India’s total oil consumption thereby reducing the dependence on fossil fuels. Hence, the main objective of the work is to model and simulate a solar absorption cooling system for GERMI office building located in Gandhinagar, Gujarat, India, using the transient simulation software ‘TRNSYS’. Cooling load estimation and comfort conditions required for the building were determined based on ASHRAE standards. Evacuated tube collectors were selected because of its market availability, ease of manufacturing and proven technology. Single effect absorption chiller was used because of its commercial availability. The effects of storage tank volume, collector area and collector slope were also investigated for parametric optimization. The results of the simulation and parametric analysis are analyzed and presented in the paper.

Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.

Optimization and comparative performance analysis of conventional and desiccant air conditioning systems regenerated by two different modes for hot and humid climates:Experimental investigation

A desiccant air conditioning system is considered a capable alternative to a conventional air conditioning system because of its independent control of temperature,humidity and being eco-friendly.Also,to resolve the problem of more energy consumption for the restoration of a desiccant,structures comprising of desiccant can utilize thermal energy or complete waste heat to revive desiccant material.Therefore,this research work executes an experimental,optimization and comparative examination for conventional and desiccant air conditioning sys-tems regenerated by two different modes,i.e.firstly using(Mode-I)complete waste heat from condenser and secondly using(Mode-II)rod(electric heater)heat for regeneration at different process air inlet temperatures,i.e.(28,29.5,31,32.5,34,35.5 and 37°C),at different process air inlet velocities,i.e.(1.5,2.5,3.5 and 4.5 m/s)and a fix(2.5 m/s)regeneration air inlet velocity.Thus,optimization of performance parameters,i.e.VCOP,ECOP,dehumidification effectiveness,moisture removal capacity(kg/hr),DCOP,regeneration effectiveness and regeneration rate(kg/hr),is identified for achieving maximum efficiency of conventional and desiccant air con-ditioning systems under the above operating conditions.

Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality.Through varying compressor and supply fan speeds of a direct expansion(DX)air conditioning(A/C)unit,the air temperature and humidity in the conditioned space can be regulated simultaneously.However,most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible.The energy consumption,which is directly influenced by the compressor and supply fan speeds,is not considered in the relevant controller formulations,and thus the system may not operate with the highest possible energy efficiency.To effectively control temperature and humidity while minimizing the system energy consumption,a model predictive control(MPC)strategy was developed for a DX A/C system,and the development results are presented in this paper.A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments.To facilitate the design of MPC,the physical model was further linearized.The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption.Based on the results of command following and disturbance rejection tests,the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity.In comparison to linear-quadratic-Gaussian(LQG)controller,better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.

Operating Parameters of the District Heating Substation Cooperating With the Installation of Technological Air Conditioning With High Efficiency of Heat Recovery

The article analyses the problem of determining the operating parameters of the district heating substation cooperating with the air heating system in technological air conditioning systems equipped with heat exchangers with high efficiency of heat recovery.Attention was paid to the correct selection of heat exchangers for the heat output balance depending on the heat recovery protection algorithms against a drop in the temperature of the heat transfer surface below 0℃.Critical parameters were determined in Polish climatic conditions,at which the operation of the heat recovery exchanger in the air conditioning system is switched off or limited.It has been proven that the proper functioning of the district heating substation cooperating with the installation of air conditioning with high heat recovery efficiency requires the use of two heat exchangers with different characteristics,equipped with properly selected temperature control systems.The optimal model of cooperation between the technological air conditioning system and the heating substation was also indicated.

Analysis and Modeling of the Central Air-Conditioning System in Intelligent Buildings

The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.

Aircraft air conditioning system health state estimation and prediction for predictive maintenance

The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System(ACMS) data.However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System(ACS).This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS.First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method.Then a dynamic linear model is proposed to describe the degradation process for failure prognostics.Bayesian inference formulas are carried out for degradation estimation and prediction.The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year.The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%.This would allow operators to proactively plan future maintenance.

Experimental Research and Optimization on the Environmental Friendly R1234yf Refrigerant in Automobile Air Conditioning System

Currently, the refrigerant used in the automotive air conditioning system is R134 a which has a high global warming potential(GWP) value, so people are eager to find a good replacement for it. According to recent studies, R1234 yf is considered as one of the top candidates to replace R134 a. In this article, the performances of R1234 yf "drop-in" system and the optimization to the R1234 yf system are experimentally studied and investigated.The expansion valve used for the R1234 yf system is optimized by changing the charged fluid and adjusting the spring force. The results indicate that it can improve the performance of R1234 yf system significantly through adjusting the thermostatic expansion valve. For the thermostatic expansion valves(TEVs) charged with R134 a in thermal bulb, the system performance is optimal. Compared to the original expansion valve, for the R1234 yf system applying the adjusted expansion valves, under different working conditions the cooling capacity increases by 11.3% averagely and the coefficient of performance(COP) increases by 8% averagely.

A Lightweight Compact Lithium-Ion Battery Thermal Management System Integratable Directly with EV Air Conditioning Systems

This paper discusses an improved thermal management system to ameliorate the performance of lithium-ion battery storage systems for electric vehicles(EVs) applications. A compact and lightweight cold plate is designed and fabricated to fit 18650-type lithium-ion batteries, using aluminum-finned copper tubes. A dynamic temperature PID(proportional, integral, differential) control algorithm for electronic expansion valves is developed to study using EV air conditioning refrigerant, R134a, to control battery modules’ temperature with this compact and lightweight thermal management system. The experimental results show that the proposed battery thermal management system can effectively control the battery module’s temperature. In addition, during 1C discharge, when the PID temperature algorithm control scheme is used, the maximum temperature difference across the battery module peaks at less than 4℃, and the maximum temperature within the battery module is less than 36℃.

Optimum Energy Management of the Central Air-Conditioning System in Intelligent Buildings

An optimum energy saving scheduling strategy of the central air conditioning system in an intelligent building (IB) was proposed. Based on the system analysis a set of models of the central air conditioning system was established. The periodically autoregressive models (PARM) based on genetic algorithms (GA) were used to predict the next day’s cold load. The improved genetic algorithms (IGA) with stochastic real number coding were used to finish the optimum energy saving scheduling of the system. The simulation results for the building of the Liangmahe Plaza show that the proposed strategy can save energy up to about 24 5%.

Evaluation on Cycle Performance of R161 as a Drop-in Replacement for R407C in Small-Scale Air Conditioning Systems

In this work, with excellent environmental characteristics and thermodynamic properties, R161 is considered as a drop-in replacement of R407C used in small-scale air conditioning systems. In order to study the potential of R161 in the application, the theoretical and experimental cycle performances of R161 and R407C were compared under different working conditions. The experimental results agreed well with theoretical results. In the experiment, COPs of R161 were about 15% higher than those of R407C. The compressor power consumptions of R161 were about 18% lower than those of R407C. The discharge temperatures of R161 were about 0-4℃ lower than those of R407C. Besides, the annual electric consumption of a small-scale split air conditioning in Beijing, Shanghai and Hongkong could be reduced by about 19.5%, when the working fluid was changed from R407C to R161. Overall, the results indicated that R161 has a better cycle performance and good compatibility with the existing systems designed for R407C.

Load Management Scheme Using Air Conditioning Electric Power Consumption and Photovoltaic Power System Generation

In the last few decades, in the world and also in the European Union, considerable resources had been invested in the rapid development of renewable energy sources and distributed generation in general. At the same time, power consumption is continuously increasing, and consumers are becoming more complex, which ultimately requires new investments in the distribution network. Concept of smart grids is generally accepted as a possible solution. Smart grid is a concept with many elements, where monitoring and control of every element in the chain of production, transmission, distribution and final consumption enable much more efficient delivery and use of electricity. One of the elements of smart grid efficiency is the ability of real-time demand-supply balancing. This balancing is carried out by monitoring of consumption and redistribution of electricity among individual end users, according to their needs. The aim of this paper is creating algorithm for real-time load management using power measurements. Algorithm for real-time load management at the ETFOS （Faculty of Electrical Engineering in Osijek）, Croatia is created based on measurements of photovoltaic power plant production, the power consumption of air conditioning system and the faculty building total electricity consumption. Expected result of real-time re-dispatching of air conditioners consumption, depending on the level of electricity production in photovoltaic power plant is decreasing peak demand of the faculty.

System design and analysis of the trans-critical carbon-dioxide automotive air-conditioning system

As an environmentally harmless and feasible alternate refrigerant, CO 2 has attracted worldwide attention, especially in the area of automobile air conditioning (AAC). The thermal property of CO 2 and its trans critical refrigeration cycle is very different from that of the traditional CFC or HCFC system. The detailed process of CO 2 system thermal cycle design and optimization is described in this paper. System prototype and performance test bench were developed to analyze the performance of the CO 2 AAC system.

Localized Theoretical Analysis of Thermal Performance of Individually Finned Heat Pipe Heat Exchanger for Air Conditioning with Freon R404A as Working Fluid

This work contributes to the improvement of energy-saving in air conditioning systems. The objective is to apply the thermal efficiency of heat exchangers for localized determination of the thermal performance of heat exchangers with individually finned heat pipes. The fundamental parameters used for performance analysis were the number of fins per heat pipe, the number of heat pipes, the inlet temperatures, and the flow rates of hot and cold fluids. The heat exchanger under analysis uses Freon 404A as a working fluid in an air conditioning system for cooling in the Evaporator and energy recovery in the Condenser. The theoretical model is localized and applied individually to the Evaporator, Condenser, and heat exchanger regions. The results obtained through the simulation are compared with experimental results that use a global approach for the heat exchanger. The thermal quantities obtained through the theoretical model in the mentioned regions are air velocity, Nusselt number, thermal effectiveness, heat transfer rate, and outlet temperature. The comparisons made with global experimental results are in excellent agreement, demonstrating that the localized theoretical approach developed is consistent and can be used as a comprehensive analysis tool for heat exchangers using heat pipes.