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Simplified approach to weather analysis for detailed thermal simulation in tropical and subtropical areas.

INTRODUCTION

Simplified guidelines for building construction on tropical areas are normally encountered in most of the HVAC industry literature. Givoni provided guidelines for two main climates of our interest: hot-dry and hot-humid regions (he also provided recommendations for cold, and for hot-humid with cold winter regions). Lippsmeier, expanded the range of classification distinguishing equatorial rain forest, humid savanna, desert and uplands regions. Recognizing the drawback of such simplified classifications, De Wall tried to make a more extensive climate classification for tropical climates, identifying at least 10 climates that covered almost 91% of all tropical climates, but eliminating the upland (high altitudes lands, higher than 1,500 meters above sea level) which, unfortunately, Mexico has a high percentage.

Source of Climate data

Getting climate data is one of the difficulties the designers normally find in developing countries. The sources are scarce and not reliable. Two main sources normally used are: the US Department of Energy under the Energy Plus program which only includes data taken from the World Climate Design Data 2001 ASHRAE Handbook from three cities: Mexico City, Veracruz and Acapulco. Another source of information is the Mexican National Meteorologic Service (Servicio Metereologico Nacional).

Climate Group

Topography as climate modifier

Altitude is an important modifier, since temperature normally decreases with the increasing altitude at about 1.2[degrees]R (0.65[degrees]K) for every 328 ft (100m). in the atmosphere. On the mountains, the temperature drop can be even be higher, estimated at 1.8[degrees]R (1[degrees]K) for each 184 ft(56 m) during the summer and each 223 ft(68 m) during the winter. Wind speed usually increases with altitude, which results in that high-rise buildings generally have good access to breezes, except at very high building densities (Goetz).

During the day, as surfaces are heated by solar radiation, the air closer to the ground acquires the highest temperature. In calm conditions the air within 6.6 ft (2 m) of the ground remains stratified.

When heat builds up, and the lowest layer of air becomes larger enough, an upward flow of warm air takes place resulting in a mixing of cooler and warmer air. On clear nights, the ground loses much heat by radiation and its temperature falls bellow of the temperature of the air. The direction of heat flow is reversed and the lowest air layer becomes the coolest.

The heat loss radiated from the ground during the night causes a cold-air layer near the surface of the ground, creating a "cold island" at the lowest point. This is more profound in valleys where the flow of cool air slides down the valley slopes. (However, in the particular case of Mexico City this cold island effect does not happen as reported by Jauregui, who identified a positive heat island value probably caused maybe by the contamination effect on the radiant heat lost.) Late in the evening, the valley floor will be very cold and a plateau will be cold also. However, the higher slopes of the hill will be warm, as the low circulation mixes the ground layer with neighboring warm air. As a result, mountain slopes experience fewer temperature changes than the lowlands, while the plateau have higher temperature ranges than the mountains. Temperature changes over the water tend to be less than over land because of the water high heat capacity; meaning that it heats up and cools down slower than land. Thus, the further one gets from water surfaces, the more extreme is the temperature. Regarding radiation, slopes facing south receive more direct sunlight and have a warmer climate than those facing north.

The climatic parameter that is most influenced by topography is the wind pattern. Valleys tend to channel wind along their own axis. Raising contours opened to the wind produce up currents on the windward side, reverse eddy currents over the crest on the leeward side. The topography influences the wind pattern and speed. Wind speed is usually lower at ground because of friction, increasing with altitude.

Mountain, hills and tall buildings act as an obstacle to the movement of air. Wind flow is diverted by them in horizontal and vertical pattern, as shown in Figure 2. This result in higher wind velocities at the top or the windward side of the mountain; and lower and less turbulence in the leeward slope. The wind speed begins to decrease on the windward side of a wind break and regains its full magnitude at a distance, depending on the type of barrier.

However, mountains also create local winds that vary from day to night. During the day, the air next to the surface heats up faster than free air at the same height and thus warm air moves up along the slopes. During the night, as mountain surfaces cool down by radiation faster than the free air, cold breezes are formed and slide down the slopes of the mountains, as shown. This phenomenon can be intense, especially in narrow valleys, which can experience strong upward winds along the valley floor during the day and down the valley at night.

Due to the difference in heat capacity of water to ground (as discussed before), similar but reverse wind pattern between day and night occur near large water surfaces. During the day the air is warmer over the land (low pressure) than over the water surface (high pressure) and the resultant pressure difference imposes a breeze, called "sea breeze", from the sea towards the land. Seaside areas benefit from sea breezes, which can lower the temperature up to 15[degrees]R (8.3[degrees]K) on a hot day. At night, the air flow reverses, but the established breezes are weaker because the temperature difference between land and water is less than during the day. Late in the afternoon and early in the morning there is no breeze, as land and water are approximately the same temperature. The presence of water in a region can effectively dampen the temperature fluctuations. Due to the evaporation of water, the relative humidity increases, resulting in decreasing re-radiation of heat from surface. This results in the modification of night time cooler temperatures.

In case the wind flows down the mountain slopes carrying moisture, the rainfall on the hills could be very pronounced. When the ground level changes by 984 ft (300 m), the windward slope can be expected to receive more rainfall than the regional average and the leeward slope less. Other examples of urban planning and site selection can be found in: (Bitan, Golany and Rahamimoff and Bornstein)

THE PROPOSED METHODOLOGY

This is a simplified approach based on the topography as the main driving force for the wind direction. So we will expect the wind direction will be driven by the temperature's quartile. During the work we have done this theory has proven to be true in almost all locations, with the exception of a site located on a mountain valley, where the wind direction was not predictable at all. Even the local readings did not fit with the official meteorological station, just 10 miles away (this site was located in Santa Fe Mexico City).

The procedure is as follows: we validate the Meteorological readings taken on site against local readings using a calibrated meteorological station. These local readings are performed for as long as possible, usually an architect allow us three to six months to setup and make on site readings. If data fitting is required, a stochastic analysis is performed and data fitting is done to the closer significant Official Meteorological station available.

We then check for data integrity from the Meteorological station, if some filling is required this is done by interpolation with adjoining days (Long). If many days of readings are missing we discharge the data set for a period or for a complete year.

A MatLab[R] subroutine is run where we define the Temperature and Wind direction sectors. Temperature sectors are defined as low, mean and high temperature quartiles, based on the monthly mean temperature and half of the standard deviation of the month. In most cases, we use six wind direction sectors (although there is not specific reason to use six wind direction sectors, but it has work for us). Low velocities wind are not considered on the analysis (for indoor environmental design purposes, we eliminated readings lower than 0.31 miles/hr (0.5 km/hr).

The MatLab[R] subroutine arranges the monthly readings for the temperature and wind direction sectors and calculates the probability of the wind direction taken place in every temperature sector. As an output (besides the numerical values), we have a simplified graph were the monthly probabilities of wind direction due to temperature can be appreciated.

Some examples of the method are:

Veracruz (sea coast location Golf of Mexico, so sea wind during the day and land wind in the night are expected).

In this case we will expect "hot" wind from wind sectors 1 and 2. And cold winds from sectors 5 and 6. So if a fast detailed simulation is required CFD can be performed just for 3 or 4 cases.

Mexico City (Valley surrounded by mountains and opening to the NE), Year 2000

Mexico City (Valley surrounded by mountains and opening to the NE), Year 2002

Piedras Negras (desert land border with Texas US), Hot and dry summer, cold winter.

Since this is an extreme weather location, with very hot dry summer and cold winter. Wind sector 2 is critical during the summer and sector 5 for the winter. So our natural ventilation strategies and people shelter need to focus on these factors.

Other conditions not drove by temperature changes due to topography; we found other cases were the wind driving force was not the topography. Some examples are: Salamanca, Gto (Higher plateau, flat land field),

In this, case it seems that the wind direction is driven more from the season of the year (months 7 to 12 wind sector 1, months 1 to 6 wind sectors 4 and 5) other than temperature changes due to topography.

Mexico City, Santa Fe site (Mountain slopes valley, west to Mexico City valley), this was an exceptional case, since the readings from, and the meteorological station were done just 400 meters below the level of the site. So Official readings from the meteorological station differ greatly from the readings on the site, and not direct correlation was found.

Wind Velocity

In addition to wind direction, wind magnitude is generally required. As a first approach we normally use is the average wind speed obtained by a wind rose figure for the wind sector required.

CONCLUSIONS

The presented methodology allows the designer to initiate a simplified weather analysis on the first stages of the integral design process of a natural or hybrid driven building.

In our experience there is repeatability on the analysis year by year, unless exceptional abnormal weather conditions are present.

Bitan, A. (1982) THE JORDAN VALLEY PROJECT-A CASE STUDY IN CLIMATE AND REGIONAL PLANNING. Energy and Buildings, Vol. 4. pp. 1-9

De Waal H.B. 1993 NEW RECOMMENDATIONS FOR BUILDING IN TROPICAL CLIMATES. Building and Environment Vol. 28-3 pp. 271-285.

Givoni Baruch and Givoni Bar. 1998 Climate Considerations in Building and Urban Design. Van Nostrand Reinhold. Goetz, L. (1982) INTEGRATION OF CLIMATE IN PLANNING AND BUILDING ILLUSTRATED IN A CASE OF EXTREME CLIMATIC CONDITIONS. Energy and Buildings, 4. pp. 51-65

Golany, G. SELECTING SITES FOR NEW SETTLEMENTS IN ARID LANDS: NEGEV CASE STUDY. Energy and Buildings, Vol. 4, pp.23-41

Jauregui, E. (1997) HEAT ISLAND DEVELOPMENT IN MEXICO CITY. Atmospheric Environment, Vol. 31. No. 22 pp. 3821-3831

Lippsmeier G. 1980. TROPENBAU-BUILDING IN THE TROPICS. Munchen: Callwey. ISBN 3-7667-0536-9

Long N.. Real-Time Weather Data Access Guide. July 2003 * NREL/BR-550-34303 National Renewable Energy Laboratory. NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute

Rahamimoff, A., and Bornstein, N. (1982) EDGE CONDITIONS--CLIMATIC CONSIDERATIONS IN THE DESIGN OF BUILDING AND SETTLEMENTS. Energy and Buildings, Vol. 4. pp. 43-49

Santamouris, M. and Asimakopolous. (1996) PASSIVE COOLING OF BUILDINGS. James and James (Science Publishers) Ltd. UK.

Servicio Metereologico Nacional, Mexico. http://smn.cna.gob.mx/

US Department of Energy. EnergyPlus Energy Simulation Software. http://www.eere.energy.gov/buildings/energyplus/cfm/weatherdata_int.cfm

Eric Hernandez Desentis

Associate Member

Eric Hernandez is a Mechanical Engineering in Mexico City. Visitor researcher at Aalborg University, Department of Civil Engineering--Indoor
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Author:Desentis, Eric Hernandez
Publication:ASHRAE Transactions
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2014
Words:2065
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