TM DU 8501
Wind Environment
RODDAM NARAS1MHA
Technical Memorandum DU 8501
o
10
00
December 1985
Bangalore 560017 India
Errata
1.
Page 9 line L8 - read Figures 4c and Ad as "Figures 4c to
4f ".
2.
Page 10 - read equation (5) as "pr(V<v)=exp[-exp{-(v-y)/a]"
3.
Page 11 last line - read (Narasimha 1982) as "(Narasimha
1982a)".
4.
Page 20 last line - read (briefly
... 1982b)".
5.
Figure 6a - read average wind speed as "peak, wind speed".
6.
Page 26, 27 and 28 - list of references should include the
f ollowing.
Mani A, Mooley DA, 1983.
Publishers, New Delhi.
Wind
energy
...
data
1982a) as "(briefly
for
India.
Allied
ISI 19b5. Code of practice for structural safety of buildings,
loading standards. 13:875-1964. Indian Standards Institution,
New Delhi.
NBC 1970. National Building Code.
NewDelhi.
Indian Standards Institution,
Narasimha R and Shrinivasa U, 1984. Specification of design wind
loads in India. S'adhana, 7:259-274.
7.
Document sheet - read Contents ... 38r as "... 42r".
THE WIND ENVIRONMENT IN INDIA*
Roddam Narasimha
National Aeronautical Laboratory
and
Indian Institute of Science
Bangalore
SUMMARY
Knowledge of a wide variety of wind parameters is required
because of the diverse applications that need them. In wind
engineering, a major issue at the present time is to formulate a
rational method of estimating design wind loads in different
parts of the country: the building code in current use is not
consistent with published estimates of extremes, which in turn
are at best only in broad qualitative agreement with each other.
A thorough re-examination is therefore called for. The mean
winds appear well-understood, and are dominated by the monsoons
and the presence of the remnants of the Findlater jet that blows
across the Arabian Sea. The country is generally characterised
by a relatively high extreme/ mean ratio, posing difficult problems for the windmill designer. A variety of statistical data
needed for wind energy applications is now available, including
duration curves, probability distributions etc.: the need here
is now for detailed micro-surveys. The variation of monthly
means over the year can be compactly described in terms of two
empirically defined orthogonal modes, corresponding roughly to
the south-west and north-east monsoons. The meagre data available on gust frequencies required in aeronautical applications
show them to be relatively high. But a variety of other phenomena important in aviation, from mountain waves to microbursts,
still need to be investigated.
*Invited
lecture
at
the
Asia-Pacific
Engineering, Roorkee, 5-7 December 1985.
Symposium
on
Wind
1.
IHTRODUGTIOM
The
demand
for
data
on wind
parameters
of
a variety
of
different kinds is growing rapidly in this country. This demand
comes from
\ .*
diverse
users
with
very
different
applications in mind. First of all there is an urgent need to be
able to specify in a rational way the wind loads that structures
should be required to withstand: for bridges, high-rise
ings,
chimneys,
transmission
towers,
and a variety
build-
of
other
structures, wind loads may often be the design-driver. The
required here are usually on extremes,
more
sophisticated
information
on
data
but as design becomes
parameters
describing
fluctuation may a^Lso become significant. [For example, a
wind
propo-
sed manual for wind-tunnel testing of buildings and structures
(ASGE
1985)
requires both vertical mean velocity distribution
and longitudinal turbulence intensity and macroscale to be reproduced;, for long-span bridges, the vertical component of turbulence is also considered important.] There is of course also
some demand for information on wind from building designers from
a completely different point of view, namely that of comfort: in
a country like India where air conditioning is still not widespread,
it becomes important to make maximum possible use of
prevailing
comfort.
wind
and
other
atmospheric conditions
to enhance
The second major demand for wind data comes from the
designer of wind energy conversion systems, as the energy problem
becomes
severe
and
the
search
for
alternative
acquires urgency. The windmill designer needs all
the
sources
informa-
tion that the structural engineer does, but in addition he is
interested in the energy potential of the wind and its distribu-
tion over time,
to determine how reliable and efficient a wind
energy system is. going to be in a given area. The third major
class
of
users
come
from
aerospace
technology.
factors continue to play a substantial role in
Atmospheric
aircraft
opera-
tion and design; in particular wind data are required at heights
all the way from near the surface to altitudes of the order of
15 km for
aircraft
(and possibly much higher
for
Aeronautical engineers need to know not only mean
spacecraft).
and
seasonal
variations but also the gustiness of wind; for low-flying aircraft, for example, gust frequency and intensity are major parameters in design. The demand for wind data also comes
from
need to control pollution. Here
are
the
key
parameters
the
wind
speed and direction (often presented as wind roses) and turbulence and stability characteristics. There are perhaps a variety
of other areas in which wind data are required in some form or
other.
The publication in 1983 of a comprehensive survey and analysis of wind data for India by Mani and Mooley must be considered
a
significant
step
forward
in
meeting
these
demands.
Although these data are intended chiefly for wind energy
cations, there is a great deal of
information
there
appli-
for
wind
engineers as well. But, as I hope to establish during the rest
of this paper, there are still many open questions regarding the
wind environment of this country; a great many more studies need
to be carried out before we can be sure that the way these parameters are specifed is satisfactory from the point of view of
the applications
contemplated.
We should say a few words here about the data available for
deriving the parameters of interest. As on 1 January 1980, the
India
Meteorological
observatories
Department
maintained
(Mani and Mooley 1983).
a
network
of
544
These observatories have
counter anemometers and wind vanes, and measure either the total
run during the day, which yields the mean daily wind speed,
or
three-minute averages at two or more synoptic hours daily.
In
addition there are 66 stations with Dines pressure tube anemographs,
which
provide
continuous
records
of
wind
speed
and
direction. There are also 17 observatories at various airports
with
electric
daily.
anemographs,
The network of
but
these
data
anemograph stations
are
not
analysed
that Mani and Mooley
have used, numbering 37, are shown in Figure 1. There are also
25 radio-wind observatories and 62 pilot balloon stations, which
provide additional wind data at various altitudes.
We should include in this list four meteorological towers
which have been installed by various agencies in the country to
provide wind data for their own special appp^ications. These are
respectively at Thumba near Trivandrum (61 m high),
near
Madras
(91
m high),
Tarapur
Atomic
Power
Sriharikota
Station
(120 m
high) and the Visakhapatnam Steel Plant (64 m high). Some analyses of these data have been published,' but a coordinated programme
from
the
wind-engineering
viewpoint
could
be
of
great
value to the country.
While examining figures quoted in this paper, or elsewhere
for that matter, it must be realised that quite often different
authors
have
actually
analysed
somewhat
different
data
sets:
either because the data from all the stations were not available
or
because
the analyses
refer
to particular periods
of
time.
Furthermore the standard data from the meteorological observatories
are usually not corrected for height and exposure;
it is
well known that these two corrections are coupled. Often observatories located in cities might have been subjected to exposure
conditions that have varied with time,
e.g.
as buildings have
come up all around the station. It is likely that the uncertainties
from
all
these
recent on-the-spot
factors
are not
at all negligible,
survey by Tewari (1985) indicates.
meters quoted here must
as
a
The para-
therefore be seen as indicative of the
broad general features of the environment in the country, and no
more.
In the following sections we consider parameters governing
the mean winds,
their
their
probability
of
spatial
and
occurrence
temporal
on
distributions
various
different
and
time
scales. We shall later consider the data on extremes, which are
of
particular
touch
will
upon
importance
parameters
summarise our
in
this
connected
conclusions
Symposium,
with
gusts^.
and shall briefly
The
last
section
and make certain recommendations
about a programme for the country.
2.
MEAN WINDS
Data on mean winds have been studied for many years, begin-
ning
with a notable compilation by Iyer in 1935, for possible
applications
of
wind
energy
(see
a
recent
survey
1982), In the early 1960s Dr. P. Neelakantan and his
at
the
National
parameters
pioneering
Aeronautical
concerning
studies
winds
(see NAL
Laboratory
for
the
studied
same
by
Tewari
colleagues
a variety
application;
1985 for a bibliographic
of
these
compila-
tion) have been summarised by Sen Gupta (1972). Figure 2a, taken
from Ramakrishnan and Venkiteshwaran (1961), has
some
historic
interest: it indicated for the first time those areas where mean
winds were sufficiently high for wind energy exploitation to be
worthwhile. This map has been reproduced widely; although recent
analyses suggest that the map needs certain modifications (as we
shall see below), it is by and large a fairly accurate reflection of the general distribution of mean winds over the country.
A recent study of a variety of parameters concerning the
mean winds has been published by Gadgil, Narasimha and Savitha
(1985). It begins by noting the likely significance of the presence of the well-known low-level jet
(named
after
Findlater)
that blows across the Arabian Sea and over the Indian Peninsula
during the summer (Figure 2b). This jet, whose position varies
over the Peninsula, influences surface winds over most of South
India, where consequently the wind velocities tend to be somewhat higher than, say, in the northern plains and in particular
the Gangetic Valley.
Note however that the^ regions where the
mean winds are highest are the Gujarat Coast, the southern tip
of the country, around Tuticforin and Rameshwaram, and parts of
the east coast. The annual mean wind as analysed in this study
is
shown
in Figure
2c;
similar
diagrams for every alternate
month are available in the original report.
3.
TEMPORAL VARIATIONS
The wind velocity fluctuates on a variety of time scales,
from
fractions
of
a
second
through
diurnal
to
seasonal
and
interannual variations. For a. complete understanding of the wind
environment, it is necessary to be able to describe fluctuations
on all these scales,
but the data on shorter time scales are
still
scanty;
we
will
therefore
confine
our
attention
here
largely to variations on time scales from hours to months.
Figure 3a shows the variation in monthly mean wind velocities at various stations in Karnataka (Shrinivasa, Narasimha and
Govinda Raju 1979). The distributions here are typical of a fair
part of the country,
to the extent that a large peak is seen
during the south-west monsoon season. In general the wind velocities drop in winter, but it will be noticed that the data from
Mysore,
January.
for example,
At
show a second mild peak around December-
stations
subjected
to
the
north-east monsoon,
in
particular along the Coromandel (south-eastern) coast, a more
pronounced
peak
in
November-December.
the
winds
is
seen
during
the months
The most effective way of describing
of
wind
variations over these time scales is through use of empirical
orthogonal functions.
The idea here is to represent the wind
velocity V as the series
V(x,t) =
Z(j = 1 to 12) Aj(.x) Bj(t), t*= 1 to 12
(1)
where V(x,t) represents the long time mean wind at station x for
month
t
during
the
year,
B-(t)
are
the empirical orthogonal
functions of time and'A^(x) are the corresponding amplitudes or
*J
'Fourier' coefficients (determined as the jth eigenvector of the
co-variance matrix, Lorenz 1956) of the expansion. This analysis
has
been carried
out
for
17
stations
in India
(Gadgil et al
1985) and the results show that the first two coefficients
AI ,
Ao explain 84.4 per cent and 8.4 per cent of the variance of the
wind respectively.
shown in Figure 3b.
The corresponding orthogonal functions
It wilt
are
be seen that the first has a peak
during the south-west monsoon and the second during the northeast monsoon; 'it is therefore convenient to think of them loosely as the basic south-west and north-eas^t monsoon
modes.
When
both A^ and A2 are positive,the wind speed is more evenly distributed throughout the year; if A2 is negative, the wind speed
is more highly peaked during the south-west monsoon.
Figure 3c shows the values of A^ in the form of contours
for
the country.
Figure 3d does the same for the coefficient
A2« Knowing these two numbers, it is possible to get a substantially
correct
picture
of
the variation
of
the monthly
mean
speeds throughout the year at any given station.
4.
PROBABILITY DISTRIBUTIONS
A variety of different probability distributions are requi-
red in structural design and in wind energy applications.
The
windmill
the
designer
would
like
data
on
such parameters
as
probability of occurrence of lulls, the distribution of intervals between lulls and in general the wind-deration
curve.
formation on these has been published in the early NAL
some
more
Mooley.
recent
The
data
parameter
are
of
available
particular
in
reports;
the book by Mani
interest,
In-
and
especially for
windmill design, is the distribution of wind speeds. Winds below
about 8 km/h (2.22 m/s) contain so little energy that they can
be neglected; the distribution used must give
proper
weightage
to the energy-containing range of velocities. In a recent analysis (Gadgil et al. 1985), we have
several Indian stations
and
examined
fitted
them
the
to
wind
the
data
at
probability
density
p(V) = a6(V) + (1-a) (k/c) (x/c)^1 exp - (x/c)k
8
,(2)
where
6(V) is the Dirac delta function, inserted to account for
calm periods,
and the second term is a two-parameter
Weibull
distribution which has been found to be satisfactory in application in many areas of the world (e.g. Justus, Margraves, Mikhael
and Graber,
1976). The qualitative fit of
these
distributions
to the wind data may be judged by examining Figure 4a where the
data for Bangalore are shown. The Weibull parameters
have
been
obtained for monthly data at each of the 17 stations; the values
obtained are best described through a series of empirical
It is in general found that the parameters k and c are
ted between themselves and with
the
mean
wind
correla-
speed
satisfactorily; we may therefore represent them by
fits.
the
V
very
linear
expressions
, k = a2 + b 2 V.
c = a
The exponent k also
(3)
shows an approximately linear depen-
dence on c as shown in Figure 4b so that it is not a totally
independent parameter. The values of the regression coefficients
a i , b-,, an, bo over the country are indicated in Figures 4c and
4d.
When detailed data are either unnecessary
able,
or
not
obtain-
the following correlations are suggested for India as a
whole:
c = 1.09V + 0.90, k = 0.074V + 0.978.
5.
(4)
WIND LOADS
As already mentioned, wind loads and response to wind are
important design parameters for many
pioneering
studies
of
Davenport
structures,
(1964)
showed,
and,
need
as
to
the
be
s p e c i f i e d s t a t i s t i c a l l y . C u r r e n t building practice in
India
is
g o v e r n e d by the N a t i o n a l B u i l d i n g Code ( 1 9 7 0 ) or Indian S t a n d a r d
IS-875
(ISI
concept
of
1965),
which
"the maximum
are
entirely
(wind)
deterministic;
and
the
ever likely to occur" on which
they are b a s e d cannot be j u s t i f i e d , and d o e s n o t in g e n e r a l lead
to w e l l - d e f i n e d numbers. The codes provide maps of
specifying
"design w i n d p r e s s u r e "
the
country
in various zones (Figure 5a).
One m a p i n c l u d e s "short d u r a t i o n w i n d s " ; the o t h e r excludes s u c h
winds.
the
It appears,
wind
speeds
f r o m t h e notes accompanying these m a p s ,
expected
during
cyclones
have
played
a
that
major
r o l e in the f o r m u l a t i o n of the s p e c i f i c a t i o n s . A second a p p r o a c h
(which
should
be
more
satisfactory
if
it
is
feasible,
a d e q u a t e d a t a are a v a i l a b l e ) would be to u t i l i s e
statistics
likely
to
(e.g.
be
the
Gumbel
1958)
exceeded
with
country.
Some
parts
of
winds
(i.e. maximum wind
to
specify
wind speeds
speed
of
during
data
a
^ear)
(1983);
value
that
such
tion,
which
are
in
extreme
India
is
Goyal
(1972),
they p r o v i d e c o n t o u r s of
extremes
c o r r e s p o n d i n g t o d i f f e r e n t s p e c i f i e d " r e t u r n periods". The
have generally
if
in d i f f e r e n t
on
p r e s e n t e d by Sharma and Sehgal ( 1 9 6 8 ) , Ayyar and
and Mani and Mooley
extreme
s t a t e d probabilities
analysis
i.e.
data
been f i t t e d to a Fisher-Tippet Type I distribu-
gives,
for
the
probability
that
t h e extreme wind
speed Vs is less than v, the v a l u e
p r ( V < v) = exp
where p
(5)
[-exp{-v-u
is a location parameter and
Sharma and
Sehgal,
who
Q, > 0, a scale
parameter.
analysed data from only 7 stations in
north India, corrected their anemometer readings for variations
10
in height
before
fitting
them to
(5).
Jain (1971)
used Fisher-Tippet Type II distributions;
the
however has
values
that
derives for extreme winds are sometimes substantially
he
different
from those given by the others (e.g. the 50-year return speed at
Jodhpur is 220 km/h, whereas Ayyar and Goel give 155 km/h, and
Mani
and
Mooley
give
160
km/h).
Unfortunately
none
of
these
analyses gives any indication of how good the fits are; in particular
no
probability
paper
plots
or
chi-square values
are
available.
For four stations for which the raw data were kindly supplied by the India Meteorological Department, we show in Figure
5b
data
plotted
on
extreme probability paper
(from Narasimha
and Shrinivasa 1984). All that can be said from these plots is
that the fits are not too bad, but in general there are too few
sample points to be certain. In particular there
is
some
evi-
dence that at high velocities the data points do not follow the
straight
line characterising
the Fisher-Tippet Type I distribu-
tion: it is possible that (as we pointed out in the above paper)
mixed distributions of the kind studied by Thorn (1967) and Gomes
and Vickery (1977/8) are more appropriate. For these data sets,
we have
also
found from significance tests
(see Table 1)
that
there is not much to choose between Type I and Type II distributions:
general.
both provide good fits at a high significance level in
(The exception is Trivandrum, where Type II seems bet-
ter, but note that there are only 4 degrees of freedom available
here. )
While
towers
it
attempting
was
optimum
realised
structural
(Narasimha
11
1982)
designs
that
the
for
NBC
windmill
is
not
consistent with the data on extremes. Figure 5a shows e.g. the
100 year return period contours of Ayyar and Goyal superposed
over the NBC map which includes short duration winds (Narasimha
and
Shrinivasa
1984).
It
will
be
noticed
that,
according
to
Ayyar and Goyal, the highest winds are experienced not along the
east coast as indicated in the NBC, but in the eastern Gangetic
valley.
They
suggest
in
explanation
that
"the
short-period
squally winds associated with the thunderstorms and the duststorms of north India are stronger than even the gales associated with cyclonic storms which affect the coastal regions". This
view
is
also
(1983).
supported
Although
their
by
the
contours
analysis
of
of
extreme
Mani
wind
and
Mooley
speeds
Figure 5c for comparison) do not completely coincide with
(see
those
of Ayyar and Goyal, there is general agreement that the highest
values
for
the
extremes
are
found
in
the
eastern
Gangetic
valley.
Considering
the relatively small number of stations over
which data on extremes are available, and the fact that these
data have not been corrected for anemometer height and terrain,
Narasimha and
Shrinivasa
(1984), concluded
stage there is not enough justification
contours
at
small intervals
or
that at
to
to divide
the
provide
present
detailed
the country
into a
large number of different zones (in particular data for neighbouring stations can often be appreciably different, as may be
observed for Bangalore City and Airport in Figure 3a). However,
based on available information on extremes, they proposed tentatively two extreme wind zone maps for 50 and 100 year return
periods respectively. The criteria adopted in drawing these maps
12
were the following:
i.
Where there is disagreement on the precise value of the
extreme, make a conservative (i.e. safe) choice,
ii.
Use linear interpolation between neighbouring stations in
drawing zone boundaries.
iii. Select the number of zones to be specifed so that the distinction between them stands out above the uncertainties in
the data.
In retrospect, the resulting map perhaps erred on the side of
being
too conservative,
reliance
on
the
and also perhaps on an unduly strong
somewhat uncertain data on the extremes
then
available.
We have therefore recently re-examined the issue and would
make certain modifications to our earlier proposal. It will be
seen from Table 2 that there is appreciable disagreement between
different workers on extreme winds for a given station, but it
also
reveals
a
substantial
measure of
agreement
about
the
ranking of the stations. Thus, the top four stations in the two
estimates of Table 2, namely Jamshedpur, Allahabad, Jodhpur and
Delhi,
are identical.
These are followed by Calcutta, Nagpur
and Ahmedabad, which figure in both lists but in slightly different
order.
There
is also similar agreement in both analyses
that the extremes at Kodaikanal
and
Bangalore
are
among
the
lowest in the country.
(In the Ayyar-Goyal analysis Baroda has
the lowest
26.1
extreme
of
m/s.)
It is also clear from both
lists and in particular from the somewhat more extensive list in
the Ayyar-Goyal analysis that
extreme
winds
stations like Amritsar, Lucknow and Madras.
13
are
moderate
at
We need
prone
to take
east coast of
into account
the fact that the cyclone-
the country has often experienced severe
disasters, presumably due (at least in part) to high winds; and
the
cyclone
tracks
are most
numerous
just
south of
Calcutta,
near Balasore (Figure 5d). Reports of damage due to cyclones on
the Andhra Pradesh coast are not at all infrequent.
therefore seem desirable to us to propose
a
single
It would
high
wind
zone going all the way from Jodhpur and Ahmedabad on the west
through
Allahabad
and
Jamshedpur
in
the
Gangetic
basin
and
covering much of the coast between Madras and Calcutta; the high
winds
in
the
zone
are
however
caused
by
events. Correspondingly, a low speed zone
and
Bangalore
and
neighbouring
areas
Trivandrum and Tuticorin based on our own
data,
different
including
(which
kinds
Kodaikanal
could
analysis
of
of
include
extreme
see Figure 5b) -can also be defined without much diffi-
culty. There would be a problem in clearly demarcating zones at
intermediate speeds, but an
examination
of" 1 Table
2
will
we
believe generally support the zones mapped in Figure 5e. It will
be noticed that in this modification of our earlier proposal the
two high wind zones III in Figure 9 of Narasimha and Shrinivasa
(1984> are now joined and extended,
lower
wind
whole,
the
zones
new
are
basically
proposal
and the boundaries of the
smoothed
envisages
four
out somewhat.
On the
zones
counfry
in the
where specified extreme winds vary from 35 to 50 m/s, as shown.
Although we believe that the proposal made in Figure 5e is
reasonable, it is not claimed to be definitive: our purpose is
to demonstrate that the published data on extremes is inconsistent
with
the
present
code,
which
14
therefore
needs
re-
examination.
A definitive new wind
zone map can be drawn only
after a careful re-analysis of the raw data available with India
Meteorological Department, with an assessment of likely errors:
the point we wish to make is that such re-analysis is now over-
due.
6.
Gusts
The wind speed specified in most recent codes refers to a
time average
over
a
standard period,
variously two to three
seconds or one hour. The wind that is critical for the determination
of
structural
response
corresponds
however
to
a
time
scale that is in the neighbourhood of the natural period of the
structure. One therefore needs to be able to convert the data
available over a specified averaging period to a reference velocity for structural design that depends on the
structural
cha-
racteristics as well. This is usually done by providing curves
of dependence of gust velocity 6n averaging time, such as those
available in ESDU (1972) or Simiu (1977).
Data
on
such
considered it of
IISc
team made
^
averages are scarce in India;
we therefore
interest to analyse the measurements
off
that an
the Orissa .coast near Balasore during the
MONEX programme in 1979
(Narasimha et al.
1981),
to determine
how gust velocities varied with the averaging time under Indian
conditions. Preliminary results (Rao, Prabhu and Narasimha 1985)
are
shown
the
curves
in figure 6a,
provided
(1964) pointed out,
by
where we have also compared them with
Simiu
(1977).
In actual
fact,
as Hino
such gust factors increase with the rough-
ness of the surface and decrease -with height from the ground and
reference mean wind speed; therefore a universal
15
curve
of
the
gust factor against averaging time is only a rough guide; and
one needs to investigate the effects of other factors before an
acceptable standard can be specified.
There is an implicit assumption here that for a given exceedance probability,
the extreme wind load occurs at the extreme
wind. This is not in principle correct when high-frequency loads
are being considered,
because the (pressure) loading at a given
wind is also a stochastic variable; thus, it is possible, e.g.,
that a more probable lower wind, together with a less probable
higher aerodynamic, or gust coefficient, can produce, the critical
load. These facts indicate that gust factors
must
be
related
closely to site and structure (Cook & Mayne 1979, Gumley & Wood
1982): we shall return to this question briefly later.
It has long been realised that gusts -are also very important for aircraft design. The early airworthiness codes defined
a
design gust;
largely
during
abandoned
the
1950s
and 1960s,
this approach was
in favour of power spectral density methods
and statistical analysis. It is interesting that in recent times
arguments are being put forward to return to the specifications
of
a design gust,
will
play
codes.
a great
This
although this time statistical considerations
part in defining
it,
unlike in the earlier
rethinking has been inspired by the discovery that
turbulent flows in general, whether in
aircraft wings,
or
the
laboratory,
or
on
in the atmosphere, very often contain what
have come to be known as coherent structures. These developments
have
been
recently
Hussain (1983)
tried
to
reviewed
by,
for
example,
Cantwell
and Narasimha (1984). Jones (1980) has
analyse
the
problem of
16
gust
specification
(1981),
recently
from the
point of view of coherent structures,
taking into account the
inherently statistical nature of wind. It is not unlikely that
in the years to come some at least of the airworthiness codes in
the world will be rewritten to specify a design gust
that
will
be obtained possibly through statistical procedures in which the
characteristics of both
wind
and
load-bearing
structure
are
taken more fully into account. It will be interesting to find
out whether similar concepts would be applicable to the design
of terrestrial structures.
Although the
meagre,
it
is
of
information available
interest
on gusts
in India
is
to note the very early studies by
Ramamritham, Gurusahaney and Gupta (1961),
analysing load factor
data collected from civil aircraft flying over the major trunk
routes;
these studies already showed that gust frequencies in
India were linked to the monsoon
cycle,
and
were
appreciably
higher on an average than in many other parts of the world (see
Table 3) . Thus the number of equivalent vertical 10 ft/s gusts
encountered over the so called
Calcutta),
golden
triangle
(Delhi-Bombay-
and the Bombay-Madras-Colombo route, are three times
more frequent than in the U.S. This conclusion has been
confir-
med by more recent measurements in flight (ADE 1975) as indicated in Figure 6b. Whether similar conclusions are true at ground
level, and if so how they might affect design methods for terrestrial structures,
is an- issue which I think may need to be
studied.
7.
Conclusions
It should be clear from what we have said that the seasonal
pattern of winds in the country is dominated by the monsoons.
17
The
specification
admitted,
of
wind
loads
en
structures,
it
must
be
is now in an unsatisfactory state. The chief reason
for this is that the present National Building Code is inconsistent with published analyses of
the data on extremes.
appreciated that whatever data are available
on
the
It
is
extremes
have been interpreted in different ways by different scientists
and that there are still questions regarding the reliability
the data,
of
in the sense that corrections for height, exposure,
terrain and other factors have not been worked out in a satisfactory way. Nevertheless the inconsistencies stand
out
beyond
these uncertainties. The total extent in time of the data available is not as large as one would wish. For this reason it is to
be seriously considered whether much further refinement
analyses
of
the meagre
data available
is
in
the
the best method
solve the present problem. In these circumstances
two
to
alterna-
tives suggest themselves. One is to take an event-related view;
i.e.
consider
ponsible
appears
code;
second
high
winds
observed
in
each
area.
This
indeed
to have been the wise approach adopted in the current
but
cyclones
local
for
different meteorological phenomena that are res-
the
phenomena
however
must
not
only
include
the
that everybody is familiar with, but also such other
phenomena
exercise
as
norwesters,
that
would
thunderstorms
be worthwhile
is
and
to
so
on.
examine
The
those
situations where damage may be attributed to the action of wind.
There are various reports about such accidents but it is highly
desirable
to
make
a
systematic
and
scientific
study
whether the specification of the winds was appropriate
to
in
see
each
particular case. It seems imperative that the Indian Standards
18
Institution
and
the India Meteorological Department,
with the
cooperation of experts elsewhere, reconsider the problem immediately and propose a new code; a revision of the present building
code is now long overdue.
At the same time, we must be aware that the thinking on the
specification of extreme wind loads is still changing:
turn out that, in the not-too-distant future,
it may
specification
of
wind loads will not be in the straight and simple form of maximum winds and fixed aerodynamic coefficients that is now common.
Thus, a critical design condition might be defined taking into
account the nature of the structure and even the nature of the
fluctuating field that usually accompanies
specified
(low)
high
winds.
Or
a
probability of failure may be demonstrated by
combining appropriately the distributions of wind, aerodynamic
coefficient,
deterioration and other processes through a Monte
Carlo procedure, of the kind that has been used in certain airworthiness studies
sayanam,
Narasimha
(Narasimha and Ananthasay*anam 1978, Ananthaand
Ramani
1978),
using
the
concept
of
stochastic corrective processes (Narasimha 1977).
Many characteristics of the' mean wind pattern in India are
now reasonably well described. Energy content, probability distributions,
necessary
and
for
a
variety
design
of
of
wind
other
energy
example, are beginning to be defined
characteristics
conversion
better
and
that
are
systems,
for
better.
What
remains to be done here, keeping especially wind energy in view,
is
the
task of
conducting many
local surveys. A beginning has
been made here (Tewari 1985), and the preliminary results are so
promising that further studies are surely worthwhile.
19
Some
in
interesting
India
pointed
may
out
be
implications
noted.
that
Narasimha
for
wind
and
Shrinivasa
the windmill designer,
at low structural loading,
energy
exploitation
(1984)
who demands
have
high power
should seek a low extreme/mean ratio.
Whereas this ratio is less than 10 over 50% of the land area of
the U.S.,
the corresonding fraction in India is only 15%.
ing
idea,
this
we note
that for a family of
similar
Pursu-
windmills,
an interesting parameter is the ratio
—Q .A. O
EPF x VVV Z T
where
EPF
is
speed,
Vrp
moment
the v a r i a t i o n s
is
the
the
energy
extreme
pattern
for
in E P F ,
factor,
V
return period
is
T.
the
mean
Ignoring
wind
for
we see t h a t it is i n t e r e s t i n g
a
to
study the characteristic velocity
—O
"^O
W = VJ/VZT
which is a m e a s u r e of the " f r i e n d l i n e s s " of $he w i n d e n v i r o n m e n t
to
the
load).
windmill
Values
(or
of
listed in Table 41
84
mm/s
at
of
the
the
kilowatts
"friendliness"
available
per
for various
Newton
stations
it v a r i e s f r o m about 2 mm/s at A l l a h a b a d
Sagar I s l a n d ;
in N o r t h e r n C a l i f o r n i a , w h e r e
of
are
to
several
wind f a r m s a r e n o w o p e r a t i n g s u c c e s s f u l l y , it is of the o r d e r of
250 m m / s ! While a v a r i e t y of o t h e r p a r a m e t e r s are also i m p o r t a n t
in
determining
(including
the
the v i a b i l i t y of wind power as an energy
the v a r i a b i l i t y of
economics
of
alternative
the w i n d ,
sources
technology
of
energy
source
available,
etc.),
it
is
i n t e r e s t i n g to s p e c u l a t e t h a t the c h e q u e r e d h i s t o r y of w i n d m i l l s
in India
( b r i e f l y d i s c u s s e d in N a r a s i m h a 1982a)
20
may
in
large
part be due to the "unfriendliness" of the wind revealed in this
analysis!
Let me hasten to add that this is not meant to indi-
cate that wind energy has no promise, but site selection through
micro-surveys is certainly very important.
For aerospace applications (where there is now an accepted
standard
atmosphere,
Ananthasayanam and Narasimha 1979,
1983),
wind parameters need more attention; there is some evidence that
gusts
in a tropical country like India are much more frequent
than at higher latitudes. A more systematic study of such gusts
is still required, and one hopes it will soon be taken up. Aerospace applications also call for a variety of other parameters
whose
study has
not
begun in the country;
among such are the
occurrence of wind shear and microbursts.
I
Prabhu,
must
thank
my
colleagues
Dr. K.N. .Rao, Dr.
U.
Prof.
S.
Gadgil,
Prof.
A.
Shrinivasa, Mrs. Savitha and Dr.
S.K. Tewari for the effort they have put into wind studies that
has made this
survey possible.
-
21
Table 1:
C h i - s q u a r e v a l u e s for F i s h e r - T i p p e t T y p e I and II
d i s t r i b u t i o n s f i t t e d t o e x t r e m e wind d a t a
Type
I
nation
Observed Critical @
significance
level
Mu
Sigma
D.F.
Indor e
87.4
11.65
11
1.60
2.60 @ 0.995*
Trivandrum
60.9
14.28
5
1.29
1.61 @ 0.900
Tiruchirapalli
98.7
13.95
13
2.03
3.65 @ 0.995
Tuticorin
89.4
6.13
11
0.82
2.60 @ 0.995
Cochin
84.9
11.27
9
0.95
1.74 @ 0.995
Type II
D.F. Observed Critical @
significance
level
Mu
Sigma
Gamma
Indore
62.0
24.53
3.05
10
0.26
2.16 @ 0.995*
Trivandrum
30.5
29.09
2.87
4
0.29
0.30 @ 0.990
Tiruchirapalli
0.0
97.96
7.82
12
2. 96
3.07 ,@ 0.995
Tuticorin
0.0
89.14
15.23
10
0.97
2.16 @ 0.995
12.5
71.39
7.28
8
0.79
1.34 @ 0.995
Cochin
D i s t r i b u t i o n s : T y p e I, exp [-exp{~(v-y ) / cr} ]
Type II, e x p [ - { ( v - p ) / a } Y]
*Indicates the level of significance at which the observed chisquare are expected; except for Trivandrum, where there are very
few data points, the two distributions give similar fits.
22
Table 2:
Station
Comparison of 50 year return period.
(m/s) estimated by various authors
Ayyar & Goyal
(1972)
Station
extremes
Mani & Mooley
(1983)
Jamshedpur
51.4
Jamshedpur
48.6
Allahabad
49.4
Allahabad
46.4
Jodhpur
48.6
Jodhpur
44.2
Delhi
47.8
Delhi
42.2
Ahmedabad
45.8
Calcutta
41.9
Calcutta
45.8
Nagpur
41.1
Hyderabad
45.6
Ahmedabad"
40.8
Jaipur
43.9
Sagar Island
38.3
Nagpur
43.6
Vizag
38.3
Gaya
42.5
Gaya
36.4
Gopalpur
42.5
Pune
35.6
Sagar
40.8
Madras
35.3
Amri tsar
40.8
Kodaikanal
33.6
Madras
40.6
Gopalpur ^
31.7
Port Blair
39.7
Bombay
30.8
Veraval
39.7
Bangalore
28.1
Pune
38.6
Lucknow
38.1
Bombay
37 .2
Jagdalpur
37.2
Bhopal
36.9
Kodaikanal
33.6
Bangalore
31.4
Baroda
26.1
Island
•
23
Table 3:
N-miles travelled per equivalent vertical gust of
10 ft/s at mean cruise altitude of 17,000 ft
Region
N.M./Gust
Remarks
U.S.A.
300
Europe
200
For land and sea routes
Australia
170
For land routes
Pacific Ocean
157
For land and sea routes
Far East
157
For land and sea routes
Indian Ocean
146
For land and sea routes
Indian Trunk Routes
95
For land routes
For land routes
24
Table 4:
The energy "friendliness" of wind
V (m/s)
V 5 Q (m/s)
"Friendliness" (mm/s)
Allahabad
1.8
49.8
2
Bangalore
3.2
31.4
33
Sagar Island
5.2
40.8
-84
Veraval
4.6
36.1
75
N.
6.2+
31.1
246 +
Station
California
Notes :
For
Indian
A
stations, V C Q ,
the
extreme wind
for
a
50-year return period, is taken from Ayyar and Goyal (1972), and
the means from Narasimha and Shrinivasa (1984). The numbers for
N.
California are rough values read from a chart
(1981).
25
in Mikhail
Figure 1
Network of Dines
sure tube anemograph
stations in India.
*AMft
Figure 2a
Map of annual mean winds,
published by Ramakrishnan
and Venkiteswaran (1961);
based on climatologicai
tables of IMD (1960).
Figure 2b
Mean winds at 1 km during February and July
over the Arabian Sea,
India and the Indian Ocean,
( a f t e r Findiater 1976).
Mean winds at 1 k m during February and July
over fhe Arabian Sea,
India and the Indian Ocean,
( a f t e r Findlater 1976).
Figure 2c
Map of annual mean wind
speeds (Gadgil et al 1985).
ANNUAL
< 8 Kmph
8-10 Kmph
10-15 Kmph15-20 Kmph
20-30 Kmph
>30 Kmph
Figure 3a
S e a s o n a l variation of
monthly mean wind speed
at certain stations in
Karnataka (Shrinivasa
et al 1979).
Bangalore
(airport)
Bangalore
-Mercara
U-south west-4
1
rnnttCf\An
I
months
Figure 3b
The first two eigenvectors obtained by princip a l component analysis
of . mean monthly wind
speeds (Gadgil et al 1985).
Figure 3c
Spatial variation of the
amplitude A. ( G a d g i 1
et al 1985).
20 < At 4 40
40 < At 4 60
> 60
Figure 3d
Spatial variation
of
the
amplitude A 2 ( G a d g i i
et al 1985).
BANGALORE
Figure fra
The observed cumulative
f r e q u e n c y (calm periods
omitted) and the fitted
two-parameter 'Weibull
distribution for the Central
Observatory at Bangalore
(Gadgil et al 1985).
C 0.
MARCH
FEBRUARY
JANUARY
t.OO
0.50
JUMC
MAY
APRIL
1.00
5
0.50
a
03
o
e
3L
AUGUST
JULY
P 1.00
'0.50
OCTOBER
1.00
0.5<
I
i
20
t
I
40
0
20
40
0
WIND SPEED (hmph)
20
40
Figure 4b
•
The Weibull distribution
parameters k, c for Indian
stations (Gadgil et ai
1985).
30
0
r
r i
70°
75C
80'
9Q V
85'
95'
Figure 4c
Values
of
the
regression
coefficient a, (Gadgil et
ai 1985).
35°
35'
3Qf-
30'
• LKN
1.273
©ALB
0-976
25'
25*
®GYA
0767
1.476
0-962
CAL*
VVL
20 -
20°
r-9
5
!<?-
10W
70*
75'
80'
85*
90'
95'
70*
Figure frd
Values of the regression
coefficient b^ (Gadgil
et al 1985).
®LKN
0-019
•
* ALS
0-052 . *OYA
0-062
• BMP
0-06
Q.Q53
CAL
Figure »e
Values of the regression
c o e f f i c i e n t a 2 (Gadgil
et al 1985).
-35 s
1.25
«LKN
-
1-32
® A L B 1.34
Figure frf.
Values of the regression
c o e f f i c i e n t b 2 (Gadgii
75
70"
35'
et al 1985).
8MB
-0-696
T «PNE
2-29
70
Figure 5a
R e f e r e n c e wind speeds
(m/s) and wind pressure
(Pa) based on NBC map
compared with contours
of extremes (Ayyar &
Goyai, 1972) for a 100
year return period. The
r e f e r e n c e w i n d speed
is defined as (p/0.76) 1/2,
p in Pascals being the
w i n d p r e s s u r e quoted
by NBC. (Narasimha &
Shrinivasa 1984).
reference wind speeds (pressure
based on N B C mop
50.6
m/s ( 1 9 4 6 Pa)
43.0 m/s
(t465Pa)
3 5 . 8 m/s
( 9 7 4 Pa)
100 year r e t u r n
V4n.au
EXTREME
PROBABILITY
Figure 5b
Some extreme wind speed
data V. = a+by^, V-L is
the ith smallest annual
extreme wind speed from
available data, y , = ln[-ln
(i-l/2)/n]; n is the number
of annual extreme wind
speed data points and
r is the coefficient between
V i and y r CHN-Cochin,
TRP-Tiruchirapalli, TRVTrivandrum, TTC-Tuticorin
(Narasimha and Shrinivasa
1984-).
V(m/s)
PAPER
Figure 5c
Comparison
of
Contours; SO year return
period «xtr«m«s
contours
of 50 year period extremes
from Ayyar <5c Goyai (1972)
and Mani <3c Moolay (1983).
—.— Ayyar & Ooyat
Haiti & Mootey
10
- 10
TRACKS OF CYCLONIC STORMS
Figure 3d
Cyclone tracks
(Rao 1976).
during
July
68*
Figure 3e
Tentative proposal for design
wind zones for India - 50
year return period.
72"
76'
SO*/
84*
88*
92*
96'
overage wind speed
Figure 6a
Gust
with
velocities
the
variation
averaging
o Balasore dato»
Rao et aL(1985)
24 June 1979, 1650 h
Simiu 1977t
open terrain
time
measured in Balasore, 1979
(Rao et al 1985), compared
with curve proposed by Simiu
(1977).
1.4-
6.78m/s
LO
O.t
102
averaging firm
I03s
Figure 6b
T
Gust frequencies in different
parts of the world (ADE
1975).
X INDIA
^ UK
: ADE, 1975
:
TAYLOR, 1953
^3 NORTH AFRICA : BULLEN, 1959
o
10
-I
lo
3
U.
° 10
d
*
10
To
'0 ,
•
20
VEUDCITY OF GUST,
L^
fc
J
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j
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28
^>^'u National
>(C^<3S Aeronautical
\^r Laboratory
Title
Document Sheet
The Wind Environment in India
Sheet Class
UNRESTRICTED
Report No.
TM DU 8501
Authors)
R Narasimha
Type
Tech. Memorandum
Divisions)
Directori:s Unit •
Date Dec 85
Sponsor
None
Class
Unrestricted
Participating
Agencies
None
Project No.
Approval
Director
No. of copies -jOO
Keywords
Wind data, extremes, gusts, wind
energy, environment
Contents
2.lp, 4t, 21f, 38r
Abstract Knowledge of a wide variety of wind parameters is required
because of the diverse applications that need them. In wind engineering,
a major issue at the present' time is to formulate a rational method
of estimating design wind loads in different parts of the country:
the building code in current use is not consistent- with published estimates of extremes, which in turn are at best only, in broad qualitative
agreement with each other. A thorough re-examination is therefore called
for. The mean winds appear well-understood, and are dominated by the
monsoons and the presence of the remnants of the Findlater jet that
blows across the Arabian Sea. The country is generally characterised
by a relatively high extreme/mean ratio, posing difficult problems
for the windmill designer. A variety of statistical data needed for
wind energy applications is now available, including duration curves,
probability distributions etc.: the need here is now for detailed microsurveys. The variation of monthly means over the year can be compactly
described in terms of two empirically defined orthogonal modes, corresponding roughly to the south-west and north-east monsoons. The meagre
data available on gust frequencies required in aeronautical applications
show them to be relatively high. But a variety of other phenomena important in aviation, from mountain waves to microbursts, still need to
be investigated.