[Chapter 2 of the lecture CAD to SFT.
Click here for the start of the start of the lecture]
Contents
4.1 The requirements: realism; economy; balance
4.4 The IMMERSOL model for radiation
4.5 The MFM turbulence model, for turbomachines and combustors
Click here for a complete lecture on the Multi-Fluid Model of Turbulence
The just-described two-metal-block problem can also be used to exemplify the practical difficulties of simulating turbulence, and heat transfer by radiation and convection, in practical circumstances; and indeed, fortunately, means of surmounting them.
The difficulties result from the facts that:-
Thus, it is pointless to expend large resources on elaborate low- Reynolds number turbulence models if the grid fineness is hopelessly inadequate; or on complex geometrical view-factor calculations if fluid-participation and wave-length dependences are totally ignored.
Considerations of balance led to not-yet-conventional methods being used in the above example. They will now be discussed.
Computing this was not easy until the introduction of the LTLS method (Spalding, 1994), in which the wall-distance (and also the distance between walls) was computed by solving the equation:
div_grad L = - 1
This elliptic linear equation for a single variable is very easy to solve. In PHOENICS, the solution is carried out swiftly at the start of the computation, and the results are stored for subsequent use.
The following pictures show the resulting distributions of wall- distance and the distance-between-walls, for the two-block problem.
The results are exactly correct wherever the quantities in question have precise meanings; and elsewhere they are "plausible". [The quotes imply a need for discussion, for which there is no space here]
Fig 4.2-1 The distance from the wall
Fig 4.2-2 The gap between walls
As has been shown by Aganofer, Liao and Spalding (1996), conventional low-Reynolds-number models (eg Lam and Bremhorst, 1981) are computationally expensive and of doubtful realism.
However, there exists a simpler, more economical, and (in these circumstances) equally realistic model, which is described in that paper, and used here. This is the so-called LVEL model, which derives the local effective viscosity from the wall distance, the distance between the walls, and the local velocity.
The following picture shows the effective-viscosity distribution computed for the two-block problem.
Once again, it can be proved that the predicted distribution is precisely correct in simple circumstances, and plausible elsewhere.
Fig 4.3-1 The effective viscosity
Click here for the PHOENICS Encyclopaedia article on LVEL
If turbulence modelling in domains which are crowded with immersed solids is difficult, no less so is the computation of the radiative heat exchange between the solids and the intervening medium.
In the results presented above, use was made of the IMMERSOL (ie IMMERsed SOLids) method (Spalding, 1996). This represents radiative transport of energy by way of a diffusion equation for radiosity, in which the diffusivity is proportional to:
1 / [ A + S + 1 / WGAP ]
where A and S are the absorption and scattering coefficients of the medium per unit length, and WGAP is the distance between solid walls.
Further formulae represent the radiosity jump at phase boundaries, and so enable the radiosity in the medium and the temperature in the solids to be computed from the solution of one (non-linear) equation.
In the following pictures, contour diagrams will be presented. These will display, in order:-
The computations were completed within a few minutes, by the PHOENICS computer code mounted on a Pentium personal computer.
The results are plausible; but experimental verification is needed.
Fig 4.4-1 Gas and solid temperature
Fig 4.4-2 The radiosity temperature
Fig 4.4-3 y-direction radiation flux
Fig 4.4-4 x-direction radiation flux
Contents of section 4.5
Approach (2), of Dopazo/O'Brien type, is followed by some combustor specialists; but its expense deters all but the wealthiest.
Approach (3), of the same type, has been little publicised; but it is economical, easy to use, and contains the necessary physics.
Axial-flow compressors and turbines, as used in aircraft propulsion and in ground- (or sea-) level power production, are characterised by the rapid passing of one blade row behind another.
The slower-moving boundary-layer fluid from the upstream row becomes a "wake" of slower-moving fluid fragments, which are distributed across the entrance plane of the downstream row.
The turbulent mixture which passes from row to row through a turbo- machine is therefore best represented as a population of fluids, with (say) axial velocity as their distinguishing characteristic.
Approach (3), ie use of MFM, is a practicable means of calculating the population distribution and its influence on the mean flow.
Research on the exploitation of this possibility is only now starting; but its promise appears to be very great. Further research on Kolmogorov-type models is now hard to justify.
The lowest member of the MFM family is the two-fluid model (Spalding, 1987), with which some recent studies have been made.
There follow two pictures which show how the time-mean velocity distribution of a blade row differs according to whether a two- fluid or (as is customary) a single-fluid model is presumed.
The differences are qualitatively similar; but the small quantitative differences are what counts when blade-row losses are to be computed.
If two-fluid calculations can already provide meaningful guidance to turbo-machinery designers, much more can be expected from the full MFM treatment.
Unfortunately, most turbo-machinery researchers still follow each other down the approach-1 tunnel, with no light at the end!
Fig 4.5-1 Radial-velocity contours at outlet ; 1 fluid
Fig 4.5-2 Radial-velocity contours at outlet ; 2 fluids
Combustion-chamber designers need to be concerned that their designs not only burn their fuels efficiently but also reduce to the minimum the production of atmospheric pollutants such as smoke and oxides of nitrogen.
To try one design variant after another is hopelessly expensive of time and money; so computer simulation is their main recourse.
Computer simulation may, of course, be MISleading; and it is likely to be so if the models built into the computer code do not embody the best physical knowledge about the relevant processes.
A realistic MFM model of gas-turbine combustion would supposes that the gases at any location constitute a population distinguished at least two-dimensionally, the dimensions being:
Fig 4.5-3: a two-dimensional (reactedness/fuel-air ratio) population
The random coloured circles on the right illustrate the physical conception underlying MFM; each colour represents a different discrete-fluid state.
The proportions of fluid in each state are represented by the fullnesses of the 2D array of boxes on the left. They are what MFM calculates; and each of the 100 fluids considered here has its own temperature, smoke- and NOX-production rate, velocity, and so on.
A conventional single-fluid model would work out the average fuel- air ratio and the average degree of reactedness, and deduce the smoke- and NOX-production rates from those quantities; but it would be wrong. The reason is that the rate expressions are non-linear.
In mathematical terms:
the average of (A x B) is NOT equal to the average of (A) x the average of (B) .
In human terms, a day-worker wife and a night-worker husband may NEVER meet sufficiently to have offspring.
This subject is a large one, which cannot be sufficiently discussed in the present context. It must therefore suffice to state that predictions of smoke (or NOX) production are totally different for single- and for multi-fluid models. An example follows.
This concerns smoke production in an imaginary 3D combustor, into which is injected a fuel-rich gaseous mixture at one location and pure air at a succession of other locations.
The following picture shows the distributions of smoke concentrations at the outlet cross-section based on:-
Fig 4.5-4 Smoke predicted by a conventional single-fluid model
Fig 4.5-5 Smoke predicted by 5-fluid model
Fig 4.5-6 Smoke predicted by 10-fluid model
Fig 4.5-7 Smoke predicted by 20-fluid model
Comparison between the diagrams shows that there is very little difference between the smoke predictions for 10 and for 20 fluids; so it will better to use the smaller number, to save computer time.
Computer times are, in any case, not very large, that for 20 fluids being only three times that for a single fluid.
However, when 100 fluids (say) do prove to be necessary on grounds of accuracy, there are many available means of reducing the computer times.
For example, there is no need to employ the same number in all parts of the field; instead, the number can be varied according to the local behaviour of the solution.
Once, indeed, that it is recognised that MFM entails nothing more than discretizing dependent variables in the same way as is routine for independent ones (space and time), the well-known techniques of grid-adaptation become available.
New turbulence models need to be tested, by comparison of predictions with experiments, before they can be relied upon as the basis for serious engineering designs.
Performing the tests may be expensive, in man-power at least; so the case for committing the expenditure must be closely argued.
The case for testing MFM rests on three considerations, namely:-
Consideration (2) is easily understood by scientists, but less easily by those for whom novelty is a synonym for danger. It is, unfortunately (but for good reasons) the latter who are usually put in charge of decisions about money.
It is for them that consideration (3) has been cited; for it implies that MFM is not totally novel, and therefore not extremely dangerous; and it indicates that there is money (currently being spent on Monte-Carlo) which can be saved.
The author's view is that, within ten years, MFM will have become
accepted, fashionable and (probably too-credulously) widely used;
it certainly needs serious attention from aeronautical engineers
right now; and researchers into direct numerical simulation could
assist by casting their results in MFM form.
