• Green Infrastructures (GIs) are being used more today to advance urban infrastructure systems performance, reduce associated costs, and diminish greenhouse gasses emission to achieve urban sustainability.

• GIs provide control at the area of generation, so there won’t be capital and performance cost and emissions in conveyance and storage facilities (examples in Fig 1).

Fig 1: Rainwater Harvesting Cistern as an example of GIs (left) and CSO storage tunnel as an example of traditional urban water facilities (right)

• However, choosing an intellectually robust plan among the millions of possible LID implementation plans according to the variation in type, size and location through long-term continuous simulation is a computational challenge.

Accelerating Stormwater Infrastructure Design With High-throughput Computing Using HTCondor

Hassan Tavakol1, Steven Burian1, Maryam Imani2, Andrew Duncan2, Raziyeh Farmani2, David Butler2 1Urban Water Group, University of Utah; 2Centre for Water Systems, University of Exeter

RESULTS INTRODUCTION METHODS

is the combined sewer network of the East Side Interceptor System at the City of Toledo (4th most populous city in Ohio, US). This system extends along the east side of the Maumee River and serves a total of 25,000 acres, of which 2,200 acres are combined. As a part of LTCP (Long Term Control Plan), a model was developed in the US-EPA SWMM (Storm

Water Management Model) 5.0 (Fig 3). 5years continuous rainfall (1997-2001) was Selected as the representative period.

was performed as the 1st scenario to accelerate the optimization. In this scenario, all the 4 physical cores (Core i7 – 2600 CPU@3.44GHz) were used to evaluate the objective function through the parfor function in MATLAB 2013b.

high-throughput computing framework - 8.0.5

CONCLUSION AND FUTURE WORKS

• Application of the presented approach in the studied area could lead to the same CSO control with 2.2 Million $ less!!!

• More objective functions, more GIs, bigger systems?!

REFERENCE

Deb K, Pratap A, Agarwal S, (2002) A fast and elitist multiobjective genetic algorithm NSGA-II[J]. Evolutionary Computation. 6(2): 182-197

Graham Saunders, Dr. Defne Apul, Andy Stepnick, Jay Devkota

ACKNOWLEDGEMENTS

STUDY AREA

• This research is guided by the question: “How Multi-Objective Optimization of RainWater Harvesting (RWH) storage to minimize the Combined Sewer Overflow (CSO) volume and implementation cost can be accelerated in a complex system and long-term continuous data?”

• In such system, evaluating the objective functions takes more than 99% of the time of optimization. Therefore, parallelization of CSO modeling for different individuals in each generation of evolutionary algorithm (illustrated in Fig 2) was tested as an answer of the above question.

Fig 2: Parallelizing CSO modeling for different individuals during the evolutionary algorithm generations

RESEARCH QUESTION

Fig 3: SWMM model of the studied combined sewer network

Parallelization for different individuals in same generation

Fig 4: CPU cores usage in regular un-parallelized optimization process (left) and in parallelized case (right)

Fig 5: Schematic of the HTCondor network

edition - was used as the 2nd scenario. It provided 68 potential nodes through 4 researchers’ computers and 5 undergrad’s computer site at university of Exeter (Fig 5) when their CPU was idle for 15 min. The jobs that were suspended (due to the owner use) were resubmitted to different machines to avoid holding the generation progress.

(Non-dominated Sorting Genetic

Objective Functions

- Total CSO volume - RWH implem. cost

Decision Variables

41 (RWH storage in each subcatchment)

Max Gen 500

Pop Size 40

framework was developed in this research to connect NSGA-II to SWMM, and automate the job submission process (Fig 6).

was also used based on one 200Lit rain barrel per building for comparison purposes.

for generation#i

GA operations %selection,…

for individual#j

create SWMM_input(i,j)

submit condor_job(i,j)

end

wait until all jobs are done

for individual#j

update cost objfun(i,j)

read SWMM_output(i,j)

update CSO objfun(i,j)

end

end

Table1: NSGA-II details

Fig 6: Developed framework

Algorithm) was used via NGPM-1.4 (NsGa-II Program in Matlab) developed by Mathwork (details in Table1).

0

200

400

600

HTCondor CPU Parallelization

Tim

e (

hr)

• HTCondor could accelerate the optimization remarkably (Fig 7), especially during weekend. • Studying the convergence process (Fig 8) showed that Pareto front after 175th generation has had minimal improvement.

0

1

2

3

4

5

6

7

8

9

10

1200 1250 1300 1350 1400

Imp

lem

en

tati

on

Co

st (

Mill

ion

$)

CSO Volume (Million Galon)

Initial Generation Generation#50 Generation#100 Generation#150 Generation#175 Generation#200

Fig 7: Comparing the elapsed time in the two accelerating methods

Fig 8: Convergence process, Pareto front in the last generation, and the selected individual for comparison purposes (the one with the grey filling colour)

Selecting the best individual among the Pareto front is not a goal of this research. Stakeholders can choose it base on the financial constraints, water quality requirements, and the

weights that they consider for the two objectives. The one with closest CSO to Simple Design is selected just for comparison (Fig 9).

0

1

2

3

4

0

500

1000

1500

2000

Mill

ion

$

Mill

ion

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lon

CSO Volume Cost

Fig 9: Comparing design approaches