FACULTY OF ENGINEERING
M A I Khan C J Noakes V V Toropov
Pathogen Control Engineering Institute

•Airborne pathogens and disease spreading
•Infectious diseases and ventilation in hospital wards
•Computational Fluid Dynamics
•Why numerical Optimization
•RSM and GA optimization
•Results
•Conclusions and discussions

•There is strong evidence suggesting a link between ventilation and the transmission/spread of infectious diseases in indoor environments (Li et al 2007).
•The recent H1N1 pandemic is just one example of the threat that airborne pathogens pose to human health.
•In order to reduce the risk of airborne infection it is vital to understand the mechanisms dictating pathogen transport through the air.
0.001 to 1000 m m
•Especially important in hospital environments where patients with weakened immune systems are particularly vulnerable.
•Exhalation flow from patients with airborne infectious diseases can impose health risks to caretakers and visitors
•Hence, effective ventilation in hospital wards is very Health Tech
Memorandum (HTMO) important in order to control respiratory disease transmission through air (Tang et al. 2006 ).
• Spanish Influenza (1918-1920) 50 - 100 million deaths
• Asian flu (1957-1958) 1.5 - 2 million deaths

• Ventilation in healthcare environments has a dual purpose
• Provides a comfortable indoor environment for occupants
• Controls the distribution of contaminants, particularly the transmission of airborne infectious particles.
• Design of appropriate ventilation depends on the level of risk, heat loads in the space and occupant activity.
• Understanding how to balance these sometimes conflicting requirements is increasingly important, particularly as the energy demands of the system are also now a critical factor.

•Application of computational approaches to study ventilation airflow patterns in enclosed spaces such as hospital wards, office rooms etc. has attracted considerable interest among engineers and scientists over the last few decades
(Yam et al., 2011)
•Most of the work to date uses Computational Fluid Dynamics (CFD) for parametric study of the influence of the airflow on the transport of heat and contaminants, including airborne pathogens (Noakes et al., 2006; Ho et al. 2009) in enclosed spaces.
•While such studies indicate that certain ventilation regimes or rates may be better than others for a particular scenario they do not formally seek an optimum design. In addition, running multiple CFD simulations at design stage can be time consuming and prohibitively expensive for many organisations .
•Numerical optimisation approaches offer the potential to both find the best design in a particular scenario and also create design tools that allow a more robust selection of parameters in a given case.
•Application of optimisation techniques to airflows in building environment control is a more recent area of development

•CFD is one of the branches of fluid mechanics, which uses numerical methods and algorithms to solve and analyse fluid flows.
•CFD is used in various domains, such as oil and gas reservoir uncertainty analysis, aerodynamic body shapes optimization (e.g. planes, cars, ships, sport helmets, skis), natural phenomena analysis, numerical simulation for weather forecasting or realistic visualizations.
•CFD problem is very complex and needs a lot of computational power to obtain the results in a reasonable time.
  i
 t


 x j
  i j


 x j

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

 x j
  i




S
 i
; j

1 , 2 , 3

Define geometry and computational mesh
Physics of Flow
Flow conditions (fluid properties, turbulence, buoyancy)
Boundary conditions (inlets,outlets, heat and contaminant sources)
Model specific parameters (eg.contaminant transport and removal)
Solve for velocity, pressure, temperature and other variables
Visualise and evaluate results

Big whorls have little whorls
That feed on their velocity,
And little whorls have lesser whorls
And so on to viscosity--- L F Richardson
( N~Re 9/4 )

 u i


 t x j where i

1, 2,3
 
1


 p


 x x i j

 



 u x i j


 u x i j

 
T ij


T ij

T ij
 u u j
 u u j
2

S
T ij
where

T
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2 C S
( )

( )
 
( )
 
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 
)


•Numerical optimisation is increasingly popular in many fields of engineering
•Most optimisation techniques can be broadly classified as either deterministic (gradient based)
(Deb 2001) or stochastic (gradient free).
•The nonlinear nature of flow phenomena inside enclosed spaces, such as rooms, leads to discontinuous outputs being generated which in turn causes problems for gradient-based methods
(Wetter et al., 2003).
•In contrast, gradient-free methods, also referred to as global methods are based on stochastic
•
Find a set of design variables X (such as temperature, velocity etc.) which optimizes
(minimise of maximise) an objective/cost function f(X) (Rao S S., 2009) approaches and are better suited to building or indoor environment applications.
( ); X

R n
•One of the most popular in this category and a widely accepted global optimization technique is the
•
Subjected to the following inequality
( )

0; j

1,2 , m j
Genetic Algorithm (GA) method (Holland 1975).
• and equality constraints k
( )

0; k

1,2 , r

• Inspired from Darwin’s theory of natural selection, this method has demonstrated its capability to handle discontinuous variables and also noisy objective functions
(Wright et al., 2002).
•
But GAs, require hundreds or sometimes thousands of evaluations of the objective functions to search for the optimal solutions (Magnier et al., 2010).
• In building or indoor applications, where evaluation of the objective function comes from computationally expensive and time consuming CFD simulations.
•
The optimization process could therefore take a prohibitively long time to achieve its goal.
•
Hence, in order to save computational time associated with GA, a surrogate based
Response Surface Approximation method is used to mimic the behavior of the system (in our case the indoor air flow field) response with respect to the change in design variables.
•
The surrogate models which are constructed from high-fidelity simulations provide fast approximations of the objective and constraint functions at new design points
•
Thereby saving computational time and making optimization studies using GA feasible (Queipo et al., 2005).

PARAMETER
Number of CFD responses used as building points
Number of CFD responses used as
Validation points
R 2 Building points
R 2 validation Points
R 2 Merged
RMS Error Build
RMS
Validation
Error
RMS Error Merged
VALUE
15
5
0.9932
0.9931
0.9947
0.0108
0.0091
0.0092
Problem formulation
Find Min of f(x), subject to g(x) ≤0
Design of Experiments (DOE)
Optimal Latin hypercube (OLH)
Numerical simulation (CFD)
Determination of objective function at each DOE point
Construction of surrogate
Moving least squares method (MLSM)
PARAMETER
Maximum
Iteration
Minimum
Iteration
Coding Type
Population size
Discrete States
Mutation Rate
Global search
Elite Population
%
Random Seed
Number of
Contenders
Penalty
Multiplier
Penalty Power
VALUE
200
25
Real
20
1024
0.01
2
10%
1
2
2.0
1.0
Surrogate model validation
Evolutionary Algorithm
Invoking GA to search for the min of the surrogate of f(x)
Optimal Solution

•PACE Bio-test chamber
•Two sets of inlet and outlet
•The dimensions of the chamber
L=4.2m, H=2.26m and W=3.36m
•6 air changes per hour (ACH)
Standard ventilation rate used in hospitals

•We simulate air, temperature and pathogen concentration inside the test chamber
•We use the finite-volume code FLUENT for all our flow simulations
•Walls and ceiling assumed as constant temperature surfaces
• We solved the flow using RANS method
•Buoyancy effect assumed negligible
•Turbulence model used RNG based k e
•Transport equations for a scalar pathogen concentration and temperature were solved simultaneously with the flow
• Scalar sources representing the infection source were inserted inside the room and the corresponding monitoring regions representing healthcare workers were used for optimisation study

• Normalised scalar or pathogen concentration on plane normal to the inlet for two different outlet positions.
L values Simulation
Parameters
6 ACH
Re=u in
D/
;
D=4A/P
T in
T wall
Number of cells
32m 3 /hrs
21,000
300K
295K
0.53 million
Z
X
Y
( )
 w C
 w
T res
T res
, where T res
X

T r

T a
10
1

10 v v

outlet position
.
W
H

System response parameters with source S1 and two different monitoring regions A1 (top plots) and A2 (Bottom plots)

Response curve using moving least squares
GA convergence history for two different monitoring regions A1 and A2 same source S1

Monitoring region A1 Monitoring region A2
Source S1
Source S2

Source S1 Source S2

•We have used numerical optimization techniques to address the problem of infection control together with comfort inside a hospital room/ward.
•Our results show that, the optimum design configuration of the ventilation system in a simple test room taking into account both infection control and patient comfort is attainable.
•However, the sensitivity of the weights chosen by the designer and also on the choice of monitoring regions has a substantial influence on the results.
•CFD simulation in a realistic hospital room will still be expensive
•Accelerate CFD simulations via massively parallel simulations or use simplified or coarse flow solvers!!
•Further work is necessary to understand dynamic environments such as moving and heat generating healthcare worker and patients, doors opening etc.
•Non spherical aerosol transport and deposition.

[1] Y. Li, G. M. Leung, J. W. Tang, X. Yang, C.Y.H Chao, J. Z. Lin, J. W. Lu, P. V.
Nielsen, J. Niu, H. Qian, A. C. Sleigh, H.-J.J Su, J. Sundell1, T. W. Wong and P. L.
Yuen, Role of ventilation in airborne transmission of infectious agents in the built environment a multidisciplinary systematic review.Indoor Air, 17, 218 (2007).
[2] R. Yam, P. L. Yuen, R. Yung and T. Choy, Rethinking hospital general ward ventilation design using computational uid dynamics, Journal of Hospital Infection, 77,
31-36 (2011).
[3] C. J. Noakes, P. A. Sleigh, A. R. Escombe and C. B. Beggs, Control of Infections :
Original Paper Use of CFD Analysis in Modifying a TB Ward in Lima, Indoor and Built
Environment., 15, 41-47 (2006).
[4] J. A. Wright, H. A. Loosemore, R. Farmani, Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 43, 959
(2002).
[5] FLUENT documentation, version 12.1, ANSYS Inc, (2009).
[6] HyperWorks v10, Altair Engineering Inc, USA, (2009).
[7] CIBSE Guide A Environmental Design, The Chartered Institution of Building Services Engineers, London, (2006).