Air Conditioning Cabin Simulation with Local Comfort Rating of Passengers

Transcription

1 P+Z Engineering GmbH Air Conditioning Cabin Simulation with Local Comfort Rating of Passengers P+Z Engineering GmbH, 2007 Dr. Stefan Paulke Michael Ellinger Stefan Wagner 2 nd European Workshop on MOBILE AIR CONDITIONING AND AUXILIARY SYSTEMS ATA / CRF, November 2007, Torino Italy

2 Motivation AIR CONDITIONING Main Objective THERMAL COMFORT Temperature Level Air Quality Draft Risk Noise Level Efficiency Additional Optimisation Tasks ECONOMY Fuel Reduction Operability Maintenance Reliability Durability ECOLOGY Emissions Recycling Contamination Filtering ERGONOMICS Usability Automatic Look and Feel Interface Control SAFETY Flash Fogging De-icing Eyestrain Tiredness Reactivity Boundary Conditions Weather + Localisation Customisation + Acculturation Driving Cycle + Driver s Profile Costs + Production Regulations + Laws Air conditioning is a quite complex and an interdisciplinary challenge! 1

3 Physically Speaking Solar Radiation Sky Temperature Wind Speed Conduction Transmission Reflection Ambient Temperature Convection Humidity Ventilation Convection Absorption Body Radiation Cabin = Cavity Thermodynamic system boundary for the energy conservation 2

4 Thermal Comfort People feel and remember temperature! Design Aesthetics according to Maslow, 1978, [4] ERGONOMICS NOISE CLIMATE LIGHT VIBRATIONS ODOUR COMFORT-NEED-PYRAMID according to T.H. Benzinger, 1979, [5] THERMAL COMFORT Variable Dependency Target Temperature f ( x, y, z, t) Comfort Range (22-25 C) Ambient surface temp. f ( x, y, z, t) (T surf T air ) 4K Humidity f ( x, y, z, t) Comfort Range (30 70 %) r r Air speed r f ( x, y, z, t) 0.15 m/s Air turbulence intensity f ( v t) < 20%, fluctuation > 2Hz Activity rate f ( Q A, t) Work load (~ met) Clothing f R, R, f ) Load case dependent ( cl ecl cl 3

5 Thermoregulation of Humans Bioheat Equation: PASSIVE SYSTEM ACTIVE SYSTEM Respiration Conduction Clothing Radiation Convection Evaporation from [3] 4

6 Thermal Sensation Models of Humans GLOBAL MODEL LOCAL MODEL Two Node Model Core (90% of total weight) Skin (10% of total weight) TILL FIALA-FE 5

7 Classification of Cabin Simulation SIMULATION METHOD EMPIRIC ONE AIRZONE MULTI ZONE COUPLED 3D Input Transfer Function Black Box Output Analytic equations Neural network Hardware-in-the-loop Statistics Fuzzy logic 1D/(3D) tools Semi-empiric Energy balancing Average cabin values Simple heat transfer 1D/2D/3D tools Air exchange rates Energy balancing Spatial cabin values Spatial heat transfer Linked 3D tools CFD Co-Simulation Local cabin values Local heat transfer T = f ( x i ) Q = h T Q = h T(x,y,z) r dm r = v A dt r q = kij T r v r r ρ + ρ( v ) v = t r r p + η v + L ( v) + r f 6

8 Example Model BMW X3 Modelling effort about one week! * Intel Xeon 2.8 GHz on Windows XP 32bit SHELL MESH ~ grids ~ car elements ~ 8350 manikin shells ~ degree of freedom (DOF) CAVITY PATCHES 1 cavity with about 9750 radiation patches VF-calculation time 15 min* PID/VOLUME SPLITTING 47 car groups + 48 manikin groups 1 airzone + 7 volumes MODEL VARIANTS with / without sun roof with / without manikins 7

9 Passive Heating T ~ 4 C T ~ 15 C Sun Roof Steel Roof Opaque Glazing (τ = 0) T ~ 30 C CONDITIONS Climate Chamber 1000 W/m 2 40 C 40 %RF Test Duration: 90 min Analysis time: 10 min* * Intel Xeon 2.8 GHz on Windows XP 32bit 8

10 Artificial Neural Network Input Variables Outside temperature [-30 45] C Outside velocity [0 35] m/s Inside velocity [0 0.25] m/s Radiation temperature [-20 30] C Initial inside temperature [ ] C Mass flowrate [0 0.2] kg/s Ventilaton temperature [-30 50] C Direct sun intensity [0 1200] W/m² Altitude angle [0 90] Azimuth angle [0 360] ANN Output Variables Sampling of the design space Calculation of the response Creation of the ANN 9

11 Cool Down with One Airzone CONDITIONS Climate Chamber Time: 0 min Time: 5 min Time: 10 min Time: 20 min Time: 30 min Time: 40 min 1000 W/m2 40 C 40 %RF Car in IDLE mode Air flow rate: 0,1 kg/s * Intel Xeon 2.8 GHz on Windows XP 32bit AC recirculation mode Test Duration: 40 min Analysis time: 7 min* 2nd Workshop on MOBILE AIR CONDITIONING AND AUXILIARIES ATA/CRF, Torino, Nov

12 Cool Down with Improved Heat Transfer Local heat transfer coefficient (HTC) values derived from steady-state CFD simulation HTC [ W/(m²K) ] Velocity Magnitude [ m/s ] 11

13 Cool Down with High-End Simulation LOCAL TEMPERATURE AND VELOCITY ACTUAL TIME STEP: 300 s Stream Lines: Air speed [0 1 m/s] Flow back to the outlet < 30 C Cut Section: Temperature Distribution > 45 C 12

14 Comfort Rating LOCAL EQUIVALENT TEMPERATURE LOCAL COMFORT INDEX 40 5 hot DIN EN ISO warm 3 neutral 20 2 cool 1 cold 10 ET [ C ] Q Q bc, real bc, real = Q = A rad, eq sf h rad + Q conv, eq ( ET T ) + A h ( ET T ) sf sf conv sf 13

15 Co-Simulation Technology BENEFITS Only one interface to the middleware required Platform independent Each client tool calculates in its own solution space with individual time steps and isolated solving methods Quick exchange of client tools by standardised data transfer Parallel processing instead of sequential operation One sender many synchronous receiver and vice versa 14

16 Simulation vs. Test Temperature [ C] 80 74, , BMW X3 - Cool Down IDLE One Airzone Simulation - Average Cabin Temperature One Airzone Simulation with local HTCs - Average Cabin Temperature Coupled 3D Simulation - Average Head Level Temperature Test - Head Level Temperature (from [2]) Target Time [s] 30,0 28,1 25,6 25,3 15

17 Pros and Cons + PROS -CONS EMPIRIC ONE AIRZONE MULTI ZONE COUPLED 3D Extreme fast response times Good for rough estimations Main use for climate control Simple and transparent principles Easy transferable into other tools Fast response and modelling times Main use for parametric studies Good for concept designs Integration into full system simulation Partially very detailed algorithm Deep insight to all interactions Quick response times Show principle local effects Closer to reality More accurate heat transfer Extreme local discretisation Near to reality Highest accuracy All phenomena covered Less input required Nice looking result pictures Accurate for one purpose Each model have to be adjusted again Only result values nothing to show Not all results available Neglecting local effects Only average cabin temperature Accuracy loss for cabin s convection Neglecting local effects in cabin 1D tools neglect shading and radiance Sometimes adjustments necessary Need a lot of input data High modelling effort Only sparse spatial discretisation Need air exchange rates for input Sometimes still adjustments required Long calculation times High modelling effort Big amount of invest (soft-/hardware) Only usable by experts More difficult to overview Error-prone 16

18 Extended Air Conditioning Strategies Glazing materials (electro-chromic, near infrared, suspend particle devices) SUMMER Jalousie Solar panelled ventilation Electrical driven compressor Special car paint Vented seats, special purpose textiles Cold reservoir Ventilation concepts, diffuse air flow, humidity control, fogging risk minimization R744 AC-system Multi-zone systems with enthalpy control Heat accumulator WINTER CO 2 heat pump Auxiliary heating (PTC, electrical, fired) Seat heating Residual heat Highly isolating glazing and car body, heated windscreen PARKING START OF CAR RIDE DRIVING START/STOP according to [4] 17

19 Summary Nowadays air conditioning of cabins can be evaluated fully virtual by a plenty of methods and tools, which may even include local thermal comfort rating with active human models. In order to provide effective solutions in time and quality required from the commercial point of view, it is no longer sufficient to determine the actual state of affairs merely by experiments in the prototype phase of the development process. Energy optimised air conditioning systems must be assessed with regard to the thermodynamic behaviour of the whole system including the effects of the cabin. The further need of fuel, CO 2 and emissions reduction will bring up more preventive and well-balanced air conditioning strategies and measures in the future. 18

20 References [1] THESEUS-FE, Manuals v2.1. P+Z Engineering GmbH, Munich [2] S. Jagsch, A.-M. Blondet, C. Kußmann, T. Mooshammer. Virtueller Versuch am Beispiel Klimatisierung. VDI-Berichte Nr. 1967, S , 2006 [3] D. Fiala, K.J. Lomas, M. Stohrer. Computer prediction of human thermoregulatory and temperature responses to a wide range of environmental conditions. Int J Biometeorol 45, S , 2001 [4] F. Frühauf. Thermische Behaglichkeit im Fahrzeug von morgen. IIR-Fachkonferenz Thermomanagement im Kfz, Stuttgart, 2002 [5] E. Mayer, R. Hellwig, A. Holm. Thermische Behaglichkeit im Fahrzeug. Haus der Technik- Fachbuch, PKW-Klimatisierung V, S. 1 11, expert verlag, Renningen, 2007, ISBN: [6] T. Schneider, M. Ellinger, S. Paulke, St. Wagner, H. Pastohr. Modernes Thermomanagement am Beispiel der Innenraumklimatisierung. Automobiltechnische Zeitschrift, Jahrgang 109, Vol. 2, S , Vieweg Verlag, Wiesbaden,