Modelling Approaches to Natural and Mixed Mode Ventilation

Transcription

1 Acknowledgements Modelling Approaches to Natural and Mixed Mode Ventilation Malcolm Cook Reader in Building Performance Modelling Dept. Loughborough University Institute of Energy and Sustainable Development, De Montfort University Dr Ibrahim Abdalla Dr Paul Cropper Mr Sherif Ezeldin Dr Yingchun Ji Dr Simon Rees Engineering and Physical Sciences Research Council Natural and Mixed Mode Ventilation Modelling Building Simulation Group UCL 24 May 21 What s special about natural ventilation? What sets it aside from mechanical ventilation? Smaller driving forces Driving forces are less clearly understood and are not constant and thus more challenging to predict NV often inextricably linked to architectural form and fabric thus attention to both architectural and services issues Coverage of Presentation Concept design methods Analytical methods Dynamic thermal simulation Why model? Commercial Client request Compliance Communication Competition bids Scientific Uncertainty re. driving forces New, innovative strategies Dynamic behaviour Non linear behaviour Computational Fluid Dynamics Application example Concept design Psychrometric chart with comfort envelope Concept design Psychrometric chart with operating modes London Hong Kong 1

2 Concept design: simplified equations Analytical methods Stack calc See paper by Lomas (27), Energy and Buildings Architectural design of an advanced naturally ventilated building form Relatively straightforward equations can be used: Heat gain Floor area Flow rate QA q m3 //s C p T free opening area, A m2 to give useful concept design advice: Lightwell x sect q v Assumed air velocity ALW Q Atotal v C p T Total floor x sect Dynamic Thermal and Computational Fluid Dynamics What vs how: DTS and CFD both predict thermal and ventilation performance DTS whole building for a whole year ~ coarse spatial resolution CFD key spaces for a point in time ~ fine spatial resolution DTS CFD WHAT HOW excl. (Weekends, August, 1.W.Jul, 1 W Jul Aug Sep O ct N ov 7 4 D ec T >2 >21 >22 >23 >24 >2 Slide 9 Dynamic thermal simulation model Dynamic thermal simulation results Thermal performance predictions (graphical) Ga ain (kw) CO2 concentrattion (ppm) Tempe erature ( C) Mon Tue Wed Thu Fri Sat Sun 3 Mon Date: Mon 9/Jul to Sun 1/Jul Air temperature: FF classroom 2.18 (year_allnightvent3.aps) Dry-bulb temperature: (LondonTRY.fwt) Room CO2 concentration: FF classroom 2.18 (year_allnightvent3.aps) Internal gain: FF classroom 2.18 (year_allnightvent3.aps) Solar gain: FF classroom 2.18 (year_allnightvent3.aps) 2

3 Dynamic thermal simulation results Thermal performance predictions (tabulated) Working Hours: excl. (Weekends, August, 1.W.Jul, 1 W TOTAL Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct Nov Dec T hr % > % > % > % > % > % >2 1 1% > % 1 47 > % 9 3 > % 2 1 > % 2 >3 2 % >31 % >32 % >33 % >34 % Exceedence temperature during working hours using TRY (excluding last 2 weeks July, August and first week of September) Dynamic thermal simulation results Thermal performance predictions (post processing) Room Number of Hours Over 28 C Target is 12 Average Int / Ext Temp Diff ( C) Target is C Maximum Internal Temperature ( C) Target is 32 C GF FF FF FF FF Coupled DTS and CFD modelling Computational Fluid Dynamics: ventilation performance Dynamic/historic behaviour can be represented in CFD simulation using DTS output Surface temperatures resulting from radiation (heat flux in heat flux out) = stored energy Prediction of solar gain Computational Fluid Dynamics: fresh air distribution Computational Fluid Dynamics: temperature distribution Temperature (C) Height (m ) 3

4 Computational Fluid Dynamics: the how? question Computational Fluid Dynamics: the how? question predicting the neutral pressure planes weak airflow Integrated thermal comfort and CFD modelling Integrated thermal comfort and CFD modelling D. Fiala, PhD thesis, De Montfort University, 1998 Concept design Mixed mode case study Harm A Webber library and faculty building at Judson College, Elgin, USA View from the east (source: B Starrenburg) Psychrometric chart with comfort envelope 11 Psychrometric Chart Percentage Saturation CHICAGO N 87.7 W S Moisture Content kg/kg (dry air) M Dry-Bulb Temperature C Specific Enthalpy kj/kg S 4

5 Judson College: Section Judson college: operating modes Source: Short and Lomas, Building Research and Information, 27 A A LW total Q Source: Short and Lomas, Building Research and v C T Information, 27 p Mid season Summer Judson college: Passive ventilation mode Predicted space temperatures and airflow rates Heat Load [kw] Temperature [ºC] DBT MRT DRT DBT(Lightwell) DBT(Ambient) Sep -Sep 6-Sep 7-Sep Heat gain 34W/m 2 during the day Int.Gains Solar Irrad. Htg./Cool. Load Airflow Temperature [ºF] Airflow Rate [ac/h] Judson college: Passive ventilation and heating mode Predicted space temperatures and airflow rates Heat Load [kw] Temperature [ºC] DBT MRT DRT DBT(Lightwell) DBT(Ambient) Sep 3-Sep 1-Oct 2-Oct Heat gain 34W/m 2 during the day Int.Gains Solar Irrad. Htg./Cool. Load Airflow Airflow Rate [ac/h] Temperature [ºF] -1 4-Sep -Sep 6-Sep 7-Sep Sep 3-Sep 1-Oct 2-Oct -1 Judson college: Mechanical ventilation and cooling mode Predicted space temperatures and airflow rates Judson college: Energy predictions Comparison of predicted monthly heating, cooling and fan energy loads Judson 3 Temperature [ºC] Temperature [ºF] DBT MRT DRT DBT(Lightwell) DBT(Ambient) Aug 4-Aug -Aug 6-Aug Heat gain 34W/m 2 during the day, ach -1 at 21 C air supply 4 1 Int.Gains Solar Irrad. Htg./Cool. Load Airflow 3 US Standard Judson Cooling Load [kw] Airflow Rate [ac/h] Aug 4-Aug -Aug 6-Aug -1 Source: Short and Lomas, Building Research and Information, 27

6 Judson college: hours of operation Comparison of predicted annual energy cost Percent occ cupied hours [%] 1% 8% 6% 4% 2% % MV+H MV+C MV PV+H PV Judson Standard US (24) Standard US (26) Mechanical ventilation and heating (MV+H); mechanical cooling (MV+C); mechanical ventilation only (MV); passive ventilation and heating (PV+H); and passive ventilation (PV) Source: Short and Lomas, Building Research and Information, 27 Source: Short and Lomas, Building Research and Information, 27 Predicted horizontal temperature distribution (passive mode) Predicted vertical temperature distribution (passive mode) Level two and level four temperatures at.91m (3ft) above floor level The future Large Eddy Simulation? More coupling of CFD and DTS programs??? Simulation of control to optimise mixed mode performance??? Time dependent modelling using CFD??? Solution multiplicity Development and stability of ventilation flows 6