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TOPIC T3: DIMENSIONAL ANALYSIS AUTUMN 2013
Objectives
(1) Be able to determine the dimensions of physical quantities in terms of fundamental dimensions. (2) Understand the Principle of Dimensional Homogeneity and its use in checking equations and reducing physical problems. (3) Be able to carry out a formal dimensional analysis using Buckingham’s Pi Theorem. (4) Understand the requirements of physical modelling and its limitations.
- What is dimensional analysis?
- Dimensions 2.1 Dimensions and units 2.2 Primary dimensions 2.3 Dimensions of derived quantities 2.4 Working out dimensions 2.5 Alternative choices for primary dimensions
- Formal procedure for dimensional analysis 3.1 Dimensional homogeneity 3.2 Buckingham’s Pi theorem 3.3 Applications
- Physical modelling 4.1 Method 4.2 Incomplete similarity (“scale effects”) 4.3 Froude-number scaling
- Non-dimensional groups in fluid mechanics
References
White (2011) – Chapter 5 Hamill (2011) – Chapter 10 Chadwick and Morfett (2013) – Chapter 11 Massey (2011) – Chapter 5
1. WHAT IS DIMENSIONAL ANALYSIS?
Dimensional analysis is a means of simplifying a physical problem by appealing to dimensional homogeneity to reduce the number of relevant variables.
It is particularly useful for: presenting and interpreting experimental data; attacking problems not amenable to a direct theoretical solution; checking equations; establishing the relative importance of particular physical phenomena; physical modelling.
Example. The drag force F per unit length on a long smooth cylinder is a function of air speed U , density ρ, diameter D and viscosity μ. However, instead of having to draw hundreds of graphs portraying its variation with all combinations of these parameters, dimensional analysis tells us that the problem can be reduced to a single dimensionless relationship cD f (Re)
where cD is the drag coefficient and Re is the Reynolds number.
In this instance dimensional analysis has reduced the number of relevant variables from 5 to 2 and the experimental data to a single graph of cD against Re.
2. DIMENSIONS
2.1 Dimensions and Units
A dimension is the type of physical quantity. A unit is a means of assigning a numerical value to that quantity.
SI units are preferred in scientific work.
2.2 Primary Dimensions
In fluid mechanics the primary or fundamental dimensions, together with their SI units are: mass M (kilogram, kg) length L (metre, m) time T (second, s) temperature Θ (kelvin, K)
In other areas of physics additional dimensions may be necessary. The complete set specified by the SI system consists of the above plus electric current I (ampere, A) luminous intensity C (candela, cd) amount of substance n (mole, mol)
Example.
Since μ
ρ Re
UL
is known to be dimensionless, the dimensions of μ must be the same as
those of ρ UL ; i.e.
[μ ] [ρ UL ](ML^3 )(LT^1 )(L)ML^1 T^1
2.5 Alternative Choices For Primary Dimensions
The choice of primary dimensions is not unique. It is not uncommon – and it may sometimes be more convenient – to choose force F as a primary dimension rather than mass, and have a {FLT} system rather than {MLT}.
Example. Find the dimensions of viscosity μ in the {FLT} rather than {MLT} systems.
Answer: [μ] = FL–^2 T
3. FORMAL PROCEDURE FOR DIMENSIONAL ANALYSIS
3.1 Dimensional Homogeneity
The Principle of Dimensional Homogeneity
All additive terms in a physical equation must have the same dimensions.
Examples: 2 2 s ut ^1 at – all terms have the dimensions of length (L)
z H g
V
g
p ρ 2
2
- all terms have the dimensions of length (L)
Dimensional homogeneity is a useful tool for checking formulae. For this reason it is useful when analysing a physical problem to retain algebraic symbols for as long as possible, only substituting numbers right at the end. However, dimensional analysis cannot determine numerical factors; e.g. it cannot distinguish between ½ at^2 and at^2 in the first formula above.
Dimensional homogeneity is the basis of the formal dimensional analysis that follows.
3.2 Buckingham’s Pi Theorem
Experienced practitioners can do dimensional analysis by inspection. However, the formal tool which they are unconsciously using is Buckingham ’ s Pi Theorem^1 :
Buckingham’s Pi Theorem
(1) If a problem involves n relevant variables m independent dimensions then it can be reduced to a relationship between n – m non-dimensional parameters Π 1 , ..., Π n-m.
(2) To construct these non-dimensional Π groups: (i) Choose m dimensionally-distinct scaling variables (aka repeating variables ). (ii) For each of the n – m remaining variables construct a non-dimensional Π of the form Π ( variable )( scale 1 ) a^ ( scale 2 ) b ( scale 3 ) c where a , b , c , ... are chosen so as to make each Π non-dimensional.
Note. In order to ensure dimensional independence in {MLT} systems it is common – but not obligatory – to choose the scaling variables as: a purely geometric quantity (e.g. a length), a kinematic (time- but not mass-containing) quantity (e.g. velocity or acceleration) and a dynamic (mass- or force-containing) quantity (e.g. density).
(^1) Buckingham, E., 1914. The use of Π comes from its use as the mathematical symbol for a product.
Π μ D aVb ρ c 3 In terms of dimensions:
c ab c b
a b c
1 1 3 1
0 0 0 1 1 1 3
M L T
MLT (MLT )(L) (LT ) (ML )
Equating exponents: M: 0 = 1 + c c = – 1 T: 0 = – 1 – b b = – 1 L: 0 = – 1 + a + b – 3 c a = 1 – b + 3 c = – 1 Hence,
ρ VD
μ Π 3 (Check: OK – this is the reciprocal of the Reynolds number)
Step 6. Set out the non-dimensional relationship. Π 1 f (Π 2 ,Π 3 ) or
ρ
μ ( , ρ
d
d D (^2) D VD
k f V
x
p
^ s (*)
Step 7. Rearrange (if required) for convenience. We are free to replace any of the Πs by a power of that Π, or by a product with the other Πs, provided we retain the same number of independent dimensionless groups. In this case we recognise that Π 3 is the reciprocal of the Reynolds number, so it looks better to use Π 3 (Π 3 )^1 Reas the third non-dimensional group. We can also write
the pressure gradient in terms of head loss: L
h g x
p (^) f ρ d
d . With these two modifications
the non-dimensional relationship (*) then becomes
2 ( ,Re) D
k f LV
gh (^) f D s
or
( ,Re)
2
D
k f g
V
D
L
h (^) f s
Since numerical factors can be absorbed into the non-specified function, this can easily be identified with the Darcy-Weisbach equation
g
V
D
L
hf 2
λ
2
where λ is a function of relative roughness ks / D and Reynolds number Re, a function given (Topic 2) by the Colebrook-White equation.
Example. The drag force on a body in a fluid flow is a function of the body size (expressed via a characteristic length L ) and the fluid velocity V , density ρ and viscosity μ. Perform a dimensional analysis to reduce this to a single functional dependence cD f (Re)
where cD is a drag coefficient and Re is the Reynolds number.
What additional non-dimensional groups might appear in practice?
Notes. (1) Dimensional analysis simply says that there is a relationship; it doesn’t (except in the case of a single Π, which must, therefore, be constant) say what the relationship is. For the specific relationship one must appeal to theory or, more commonly, experimental data.
(2) If Π 1 , Π 2 , Π 3 , ... are suitable non-dimensional groups then we are liberty to replace some or all of them by any powers or products with the other Πs, provided that we retain the same number of independent non-dimensional groups; e.g. (Π 1 )–^1 , (Π 2 )^2 , Π 1 /(Π 3 )^2.
(3) It is extremely common in fluid mechanics to find (often after the rearrangement mentioned in (2)) certain combinations which can be recognised as key parameters such as the Reynolds number ( Re ρ UL /μ) or Froude number ( Fr U / gL ).
(4) Often the hardest part of the dimensional analysis is determining which are the relevant variables. For example, surface tension is always present in free-surface flows, but can be neglected if the Weber number We = ρ U^2 L /σ is large. Similarly, all fluids are compressible, but compressibility effects on the flow can be ignored if the Mach number (Ma = U / c ) is small; i.e. velocity is much less than the speed of sound.
(5) Although three primary dimensions (M,L,T) may appear when the variables are listed, they do not do so independently. The following example illustrates a case where M and T always appear in the combination MT–^2 , hence giving only one independent dimension.
4. PHYSICAL MODELLING
4.1 Method
If a dimensional analysis indicates that a problem is described by a functional relationship between non-dimensional parameters Π 1 , Π 2 , Π 3 , ... then full similarity requires that these parameters be the same at both full (“ prototype ”) scale and model scale. i.e. (Π 1 ) m (Π 1 ) p (Π 2 ) m (Π 2 ) p etc.
Example. A prototype gate valve which will control the flow in a pipe system conveying paraffin is to be studied in a model. List the significant variables on which the pressure drop across the valve would depend. Perform dimensional analysis to obtain the relevant non-dimensional groups.
A 1/5 scale model is built to determine the pressure drop across the valve with water as the working fluid.
(a) For a particular opening, when the velocity of paraffin in the prototype is 3.0 m s–^1 what should be the velocity of water in the model for dynamic similarity?
(b) What is the ratio of the quantities of flow in prototype and model?
(c) Find the pressure drop in the prototype if it is 60 kPa in the model.
(The density and viscosity of paraffin are 800 kg m–^3 and 0.002 kg m–^1 s–^1 respectively. Take the kinematic viscosity of water as 1.0 10 –^6 m^2 s–^1 ).
Solution. The pressure drop Δ p is expected to depend upon the gate opening h , the overall depth d , the velocity V , density ρ and viscosity μ.
List the relevant variables: Δ p , h , d , V , ρ, μ
Write down dimensions: Δ p ML–^1 T–^2 h L d L V LT–^1 ρ ML–^3 μ ML–^1 T–^1
Number of variables: n = 6 Number of independent dimensions: m = 3 (M, L and T) Number of non-dimensional groups: n – m = 3
Choose m (= 3) scaling variables: geometric ( d ); kinematic/time-dependent ( V ); dynamic/mass-dependent (ρ).
Form dimensionless groups by non-dimensionalising the remaining variables: Δ p , h and μ.
Π Δ pd aVb ρ c 1
c ab c b
a b c
1 1 3 2
0 0 0 1 2 1 3
M L T
MLT (ML T )(L) (LT ) (ML )
M: 0 = 1 + c c = – 1 T: 0 = – 2 – b b = – 2 L: 0 = – 1 + a + b – 3 c a = 1 + 3 c – b = 0
(^1 ) ρ
Π Δ ρ V
p pV
d
h Π 2 ^ (by inspection, since^ h^ is a length)
Π μ d aVb ρ c 3 ^ (probably obvious by now, but here goes anyway ...)
c ab c b
a b c
1 1 3 1
0 0 0 1 1 1 3
M L T
MLT (MLT )(L) (LT ) (ML )
M: 0 = 1 + c c = – 1 T: 0 = – 1 – b + 0 b = – 1 L: 0 = – 1 + a + b – 3 c a = 1 + 3 c – b = – 1
Vd
d V ρ
μ Π 3 μ ^1 ^1 ρ^1
Recognition of the Reynolds number suggests that we replace Π 3 by
μ
ρ Π 3 (Π 3 )^1 Vd
Hence, dimensional analysis yields Π 1 f (Π 2 ,Π 3 )
i.e.
) μ
ρ ( , ρ
2
Vd d
h f V
p
(a) Dynamic similarity requires that all non-dimensional groups be the same in model and prototype; i.e.
p V m
p V
p
1 ^22
ρ
ρ
p d m
h d
h
Π 2 (automatic if similar shape; i.e. “geometric similarity”)
4.2 Incomplete Similarity (“Scale Effects”)
For a multi-parameter problem it is often not possible to achieve full similarity. In particular, it is rare to be able to achieve full Reynolds-number scaling when other dimensionless parameters are also involved. For hydraulic modelling of flows with a free surface the most important requirement is Froude-number scaling (Section 4.3)
It is common to distinguish three levels of similarity.
Geometric similarity – the ratio of all corresponding lengths in model and prototype are the same (i.e. they have the same shape).
Kinematic similarity – the ratio of all corresponding lengths and times (and hence the ratios of all corresponding velocities) in model and prototype are the same.
Dynamic similarity – the ratio of all forces in model and prototype are the same; e.g. Re = (inertial force) / (viscous force) is the same in both.
Geometric similarity is almost always assumed. However, in some applications – notably river modelling – it is necessary to distort vertical scales to prevent undue influence of, for example, surface tension or bed roughness.
Achieving full similarity is particularly a problem with the Reynolds number Re = UL /ν. Using the same working fluid would require a velocity ratio inversely proportional to the length-scale ratio and hence impractically large velocities in the scale model. A velocity scale fixed by, for example, the Froude number (see Section 4.3) means that the only way to maintain the same Reynolds number is to adjust the kinematic viscosity (substantially).
In practice, Reynolds-number similarity is unimportant if flows in both model and prototype are fully turbulent; then momentum transport by viscous stresses is much less than that by turbulent eddies and so the precise value of molecular viscosity μ is unimportant. In some cases this may mean deliberately triggering transition to turbulence in boundary layers (for example by the use of tripping wires or roughness strips).
Surface effects
Full geometric similarity requires that not only the main dimensions of objects but also the surface roughness and, for mobile beds, the sediment size be in proportion. This would put impossible requirements on surface finish or grain size. In practice, it is sufficient that the
surface be aerodynamically rough: u τ k s/ν 5 , where u (^) τ τ w /ρ is the friction velocity and
ks a typical height of surface irregularities. This imposes a minimum velocity in model tests.
Other Fluid Phenomena
When scaled down in size, fluid phenomena which were negligible at full scale may become important in laboratory models. A common example is surface tension.
4.3 Froude-Number Scaling
The most important parameter to preserve in hydraulic modelling of free-surface flows driven
by gravity is the Froude number, Fr U / gL. Preserving this parameter between model ( m )
and prototype ( p ) dictates the scaling of other variables in terms of the length scale ratio.
Velocity (Fr ) m (Fr) p
m gL p
U
gL
U
1 / 2
p
m p
m L
L
U
U
i.e. the velocity ratio is the square root of the length-scale ratio.
Quantity of flow
Q ~ velocity area
5 / 2
p
m p
m L
L
Q
Q
Force
F ~ pressure area
3
p
m p
m L
L
F
F
This arises since the pressure ratio is equal to the length-scale ratio – this can be seen from either hydrostatics (pressure height) or from the dynamic pressure (proportional to (velocity)^2 which, from the Froude number, is proportional to length).
Time
t ~ length velocity
1 / 2
p
m p
m L
L
t
t
Hence the quantity of flow scales as the length-scale ratio to the 5/2 power, whilst the time-scale ratio is the square root of the length-scale ratio. For example, at 1:100 geometric scale, a full-scale tidal period of 12.4 hours becomes 1.24 hours.
Example. The force exerted on a bridge pier in a river is to be tested in a 1:10 scale model using water as the working fluid. In the prototype the depth of water is 2.0 m, the velocity of flow is 1.5 m s–^1 and the width of the river is 20 m.
(a) List the variables affecting the force on the pier and perform dimensional analysis. Can you satisfy all the conditions for complete similarity? What is the most important parameter to choose for dynamic similarity?
(b) What are the depth, velocity and quantity of flow in the model?
(c) If the hydrodynamic force on the model bridge pier is 5 N, what would it be on the prototype?
