Today the field of numerical simulation in is faced with increasing demands for data-intensive investigations. On the one hand Engineering tasks call for parameter-studies, sensitivity analysis and optimization runs of ever-increasing size and magnitude. In addition the field of Artificial Intelligence (AI) with its notorious hunger for data, urges to provide ever more extensive, numerically derived learning-, testing- and validation input for training e.g. Artificial Neural Networks (ANN). On the other hand the current ‘age of cloud computing’ has set the stage such that nowadays any user of simulation software has access to potentially limitless hardware resources.
In the light of these challenges and opportunities, Zurich University of Applied Sciences (ZHAW) and Kaleidosim Technologies AG (Kaleidosim) have developed a publically available Massive Simultaneous Cloud Computing (MSCC) platform for OpenFOAM. The platform is specifically tailored to yield vast amounts of simulation data in minimal Wall Clock Time (WCT).
Spanning approximately nine-man-years of development effort the platform now features: 
•	An instructive web-browser-based user interface (Web Interface);
•	An Application Programming Interface (API);
•	A Self-Compile option enabling users to run self-composed OpenFOAM applications directly in the cloud; 
•	The Massive Simultaneous Cloud Computing (MSCC) feature which allows the orchestration of up to 500 cloud-based OpenFOAM simulation runs simultaneously;
•	The option to run Paraview in Batch Mode such that (semi-) automated cloud-based post-processing can be performed;
•	The Katana File Downloader (KFD) allowing the selective download of specific output dat

Boiger, Gernot Kurt

Boldrini, Marlon

Drew, Dominic

Lienhard, Viktor

Sharman, Darren

Siyahhan, Bercan

ZHAW digitalcollection

Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	1	OpenFOAM	Conference	2021	A	Massive	Simultaneous	Cloud	Computing	Platform	for	OpenFOAM		G.	Boigera,	D.	Sharmana,b,	B.	Siyahhana,	V.	Lienharda,	M	.	Boldrinia,	D.	Drewb		a)	ZHAW	Zurich	University	of	Applied	Sciences,	ICP	Institute	of	Computational	Physics;	Wildbachstrasse	21,	8401	Winterthur,	Switzerland;	Prof.	Dr.;	+41	(0)58	934	77	93;	gernot.boiger@zhaw.ch	b)	Kaleidosim	Technologies	AG,	Zurlindenstrasse	80,	8003	Zurich,	Switzerland		1 Introduction	Today	 the	 field	 of	 numerical	 simulation	 in	 is	 faced	 with	 increasing	 demands	 for	 data-intensive	 investigations.	 On	 the	 one	 hand	 Engineering	 tasks	 call	 for	 parameter-studies,	sensitivity	analysis	and	optimization	runs	of	ever-increasing	size	and	magnitude.	In	addition	the	field	of	Artificial	Intelligence	(AI)	with	its	notorious	hunger	for	data,	urges	to	provide	ever	more	extensive,	numerically	derived	learning-,	testing-	and	validation	input	for	training	e.g.	Artificial	Neural	Networks	(ANN).		On	 the	 other	 hand	 the	 current	 ‘age	 of	 cloud	 computing’	 has	 set	 the	 stage	 such	 that	nowadays	 any	 user	 of	 simulation	 software	 has	 access	 to	 potentially	 limitless	 hardware	resources.		In	 the	 light	 of	 these	 challenges	 and	 opportunities,	 Zurich	 University	 of	 Applied	 Sciences	(ZHAW)	and	Kaleidosim	Technologies	AG	(Kaleidosim)	have	developed	a	publically	available	[2]	Massive	Simultaneous	Cloud	Computing	(MSCC)	platform	for	OpenFOAM.	The	platform	is	specifically	 tailored	 to	 yield	 vast	 amounts	 of	 simulation	 data	 in	 minimal	Wall	 Clock	 Time	(WCT).		Spanning	approximately	nine-man-years	of	development	effort	the	platform	now	features:		• An	instructive	web-browser-based	user	interface	(Web	Interface)	(see	[1]	and	[2]);	• An	Application	Programming	Interface	(API)	(see	[3]);	• A	Self-Compile	option	enabling	users	 to	 run	self-composed	OpenFOAM	applications	directly	in	the	cloud;		• The	 Massive	 Simultaneous	 Cloud	 Computing	 (MSCC)	 feature	 which	 allows	 the	orchestration	of	up	 to	500	 cloud-based	OpenFOAM	simulation	 runs	 simultaneously	(see	[1]	and	[4]-[6]);	• The	option	to	run	Paraview	in	Batch	Mode	such	that	(semi-)	automated	cloud-based	post-processing	can	be	performed;	• The	Katana	File	Downloader	(KFD)	allowing	the	selective	download	of	specific	output	data	(see	[2]);	2 Massive	Simultaneous	Cloud	Computing	(MSCC)	Work	Flow	In	 essence	 the	MSCC	workflow	 is	 about	 the	 simultaneous	orchestration	of	 any	number	of	OpenFOAM	simulation	runs	on	any	number	of	cloud-based	computers	within	a	public	cloud	Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	2	(e.g.:	 Google	 Cloud).	 For	 now	 the	 actual	 platform	 has	 been	 tested	 and	 released	 to	accommodate	a	maximum	number	of	500	simultaneous	cloud	simulation	runs.	The	 MSCC-workflow	 as	 provided	 within	 the	 UI	 of	 the	 platform	 [2],	 encompasses	 the	following	steps	(see	also	Fig.1):		• Step	1:	Since	the	web-platform	is	explicitly	not	meant	to	be	an	OpenFOAM	GUI,	users	proceed	 to	 prepare	 their	 OpenFOAM	 Base	 Case(s)	 locally	 with	 whatever	 pre-processor	they	see	fit	(see	Fig.1	Set	Base	Case).			• Step	2:	Users	can	copy	source	code	of	self-composed	OpenFOAM	applications	and/or	Python	 post-processing	 applications	 [7]	 (e.g.:	 for	 running	Paraview	 in	 Batch-Mode)	into	the	OpenFoam	Base-Case	folder.		• Step	 3:	 Users	 zip	 their	 OpenFOAM	 Base	 Case(s)	 and	 transfer	 (see	 Fig.1:	 Transfer	Cases)	to	the	cloud	via	the	Web	Interface	(see	Fig.1:	Web	Interface).		• Step	4:	Within	the	Web	Interface	users:	o select	any	OpenFOAM	version;	o choose	either	an	OpenFOAM	Standard	Solver	[8]	or		o compile	self-composed,	to-be-uploaded	OpenFOAM	applications;	o select	cloud-based	hardware	with	respect	to	the	number	of	virtual	CPU	cores	(vCPUs)	and	required	RAM;	o choose	either	the	automatically	created	Run	Script	or	o compose	 any	 Bash	 Script	 featuring	 e.g.	 OpenFOAM	 terminal	 command	function(s);		• Step	5:	Users	then	apply	the	MSCC	Run	Creator	(se	Fig.1:	MSCC	Run	Creator,	[1])	to	set	parameter	 ranges	 in	order	 to	multiply	 the	Base	Case	 such	as	 to	perform	e.g.	 a	parameter-study	with	up	to	500	individual	simulation	cases.			• Step	6:	Users	initiate	MSCC	(see	Fig.1:	Massive	Simultaneous	Computing)	where	the	cloud	platform	simultaneously:		o reserves,		o sets-up	and		o initializes	one	 virtual	machine	per	prepared	 simulation	 case	 yielding	 chosen	hardware	features	and	chosen	OpenFOAM	software	version;	Subsequently	the	platform	runs:	o pre-processing,		o processing	and		o post-processing,	as	dictated	by	the	Run	Script.		As	long	as	the	total	amount	of	simulation	cases	stays	≤500,	the	total	Wall	Clock	Time	(WCT)	(see	Fig.1:	T*)	of	the	entire	operation	will	stay	within	a	maximum	of	150%	of	WCT	required	to	execute	one	single	simulation	yielding	a	Net-Speed-up-Factor	(NSF)	of	up	to	100.		• Step	7:	The	platform	returns	simulation	results	from	individual	virtual	machines	to	a	cloud	database	such	as	to	ensure	availability	via	the	Web	Interface	(see	Fig.1:	Return	Results).	Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	3		• Step	 8:	 Users	 can	 apply	 the	 Katana	 Downloader	 feature	 (see	 Fig.1:	 Katana	Downloader)	 to	 selectively	 download	 any	portion	of	 data	 (e.g.:	 log-files,	 field	 data,	JPEGs	from	Paraview	post-processing,	etc.)	from	any	sub-group	of	computed	cases.			• Step	 9:	Users	 apply	 any	desired	 additional	 local	 post-processing	 procedure(s)	 (e.g.:	Paraview-based,	Python-based,	OpenFOAM-based),	(see	Fig.1:	Post-Process	Locally).				Fig.1:	Scheme	of	MSCC	workflow	within	novel	cloud	platform.	T*	signifies	the	Wall	Clock	Time	per	individual	simulation	run	(see	also	[2]).	3 Application	Programming	Interface	Users	retain	the	option	to	conduct	the	entire	MSCC	workflow	as	depicted	within	Fig.1	via	an	Application	Programming	Interface	(API),	accessible	via	[3].	Thus	the	necessity	to	access	the	Web	 Interface	 can	 be	 omitted.	 In	 this	 context	 user-defined	 scripting	 can	 allow	 for	 further	elevated	levels	of	cloud-machine-orchestration.		4 Fields	 of	 Application:	 Parameter	 Studies,	 Optimization	 and	Artificial	Neural	Network	Training	While	the	most	obvious	application	of	the	platform	lies	within	the	swift	execution	of	 large	Parameter	Studies	(see	[1]	and	[4]-[6]),	the	API	functionality	can	significantly	extend	the	field	of	 application.	More	 specifically	 API	 scripting	 can	 be	 used	 to	 arrange	 sequences	 of	MSCC	runs	 in	 loops	such	as	to	enable	e.g.	Optimization	Algorithms	of	a	novel	generation.	Where	common	optimization	procedures	in	CFD	(see	[9])	would	rely	on	relatively	many	sequential	iteration	 steps	 of	 relatively	 low	 order	 (see	 Fig.2:	 Sequential	 Optimization),	 the	 presented	platform	now	allows	the	application	of	relatively	few	sequential	iteration	steps	of	relatively	high	 order	 (see	 Fig.2:	 MSCC	 Optimization).	 While	 the	 overall	 amount	 of	 required	computation	would	 thus	 not	 necessarily	 decrease,	 global	 optima	 could	 be	 identified	with	higher	confidence	and	the	total	WCT	of	optimization	algorithms	could	potentially	be	reduced	quite	dramatically.		Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	4		Fig.	2:	Basic	illustration	of	comparison	of	Sequential-	vs.	MSCC-based	Optimization	schemes.	T*	signifies	the	Wall	Clock	Time	per	individual	simulation	run.		API	based	scripting	can	also	turn	the	OpenFOAM	cloud	platform	into	a	comprehensive	tool	for	 producing	 potentially	 vast	 amounts	 of	 simulation	 data	 for	 the	 training,	 testing	 and	validation	of	Artificial	Neural	Networks	(ANNs).	Fig.	3	depicts	 the	basic	 idea.	Thereby	 the	web-based	MSCC	Run	Creator	would	be	used	 to	conduct	 a	 multitude	 of	 simultaneous	 OpenFOAM	 simulation	 runs	 whose	 outcome	 would	provide	 testing-,	 validation-	 and	 training	data	 for	ANNs.	As	 shown	 in	 Fig.3,	 it	would	make	sense	to	add	a	database,	which	would	store	process	specific	data	(e.g.:	boundary	conditions	BCs)	as	well	as	weights	of	trained	networks.	Thus	trained	ANNs	could	potentially	take	over	specific	 functionalities	 of	 the	 original	 numerical	 solver	 and	 could	 be	 integrated	 into	accelerated	later	version	of	the	same.			Fig.	3:	Workflow	scheme	for	using	 the	MSCC	cloud	platform	to	simultaneously	produce	vast	amounts	of	 training	data	 for	ANNs.			Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	5	5 Case	Studies	Two	case	studies	shall	hereby	be	presented	 (see	5.1	and	5.2)	 in	order	 to	demonstrate	 the	potential	of	the	presented	MSCC	platform.	Further	application	examples	of	the	platform	are	documented	in	[4],	[5]	and	[6].	5.1 Case	Study	A:	Motorbike	-	WCT	and	Cost	Comparison	of	Parameter	Studies		Example	 A	 focuses	 on	 the	 well-known	Motorbike	 tutorial	 case	 of	 OpenFOAM	 5.x	 in	 the	context	of	a	steady	state	simpleFoam	parameter	study.	The	original	tutorial	case	geometry	was	re-built,	parameterized	with	respect	to	four	geometric	parameters	(see	Φ1	–	Φ4	in	Fig.4)	and	the	grid	was	refined	up	to	a	total	of	approximately	1.6x107	cells	as	compared	to	2.2x105	cells	 of	 the	 original	 tutorial	 case.	 In	 addition	 the	 onset	 airflow	 velocity	 vector	 was	parameterized	 with	 respect	 to	 its	 magnitude	 Φ5,	 its	 frontal-	 Φ6	 as	 well	 as	 its	 lateral-	 Φ7	angle-of-attack.	No	other	simulation	parameters	were	modified	as	compared	to	the	original	OpenFOAM	Motorbike	 tutorial	 case.	 Each	 of	 the	 seven	Degrees	 of	 Freedom	 (DOF)	Φi	was	then	 varied	 by	 three	 to	 four	 settings.	 The	 full	 bandwidth	 of	 possible	 combinations,	amounting	 to	a	 total	of	approximately	10’000	 individual	 simpleFoam	 simulation	cases	was	then	 run.	 The	 idea	 of	 this	 relatively	 extensive	 study	was	 not	 to	 gain	 engineering-relevant	insight	but	to	be	able	to	estimate	cost-	and	time	consumption	in	a	direct	comparison	of:		• Running	the	full	workflow	on	the	modestly	sized	ZHAW	internal	cluster	yielding	128	Intel	Xeon	8164/8270	vCPUs	with	2.0	–	3.7	GHz,	parallelizing	each	case	on	32	vCPUs.	• Running	the	full	workflow	on	the	MSCC	cloud	platform	yielding	up	to	500	AMD	Epyc	Rome,	2.25-2.7	GHz	machines	per	batch,	where	only	machines	with	32	vCPU	cores	were	chosen	for	the	same	level	of	parallelization	as	the	one	used	on	the	local	cluster.	When	 working	 on	 the	 internal	 ZHAW	 cluster	 only	 100	 out	 of	 10’000	 cases	 were	 actually	conducted.	 Batches	 of	 four	 cases	 each	 were	 uploaded,	 run	 and	 evaluated	 (i.e.:	 log-files	containing	cd	and	cl	values	were	downloaded)	by	employees.	Costs	and	time	consumption	were	extrapolated.	Working	on	the	MSCC	cloud	platform	the	full	parameter	study	of	12’000	cases	was	conducted	where	a	total	of	20	batches	of	500	cases	each	were	created,	run	and	evaluated	 by	 employees	 in	 analogy	 to	 the	workflow	 at	 the	 ZHAW	 cluster.	 Costs	 and	 time	consumption	were	monitored.	The	 core-results	 are	 shown	 within	 Fig.	 5	 and	 Fig.	 6	 where	 the	Wall	 Clock	 Time	 (WCT)	consumption	 for	 running	100	cases	as	well	as	Human	Resource	Costs	 (HRC)	 for	 running	all	10’000	cases	are	compared	respectively.		The	 results	 presented	 in	 Fig.	 6	 are	 basically	 an	 extension	 of	 those	 shown	 in	 Fig.5.	 They	demonstrate	 that	 the	 total	WCT	of	 running	10’000	 simpleFoam	 1.6x107	 cells	 cases	on	 the	ZHAW	cluster	(blue-dashed)	would	have	amounted	to	3356h,	while	running	on	MSCC	cloud	platform	 (red-dashed)	 amounted	 to	 a	 total	 of	 merely	 58h.	 In	 order	 to	 estimate	 the	 HRC	according	 to	 Fig.6,	 the	 following	 assumptions	 were	 made:	 hourly	 rates	 of	 100€/h	 were	chosen,	30	seconds	working	time	per	case	for	the	ZHAW-cluster	workflow	and	240	seconds	working	time	per	case-batch	for	MSCC-platform	workflow	were	assumed.	On	this	basis	the	HRC	of	running	the	entire	study	on	the	ZHAW	cluster	(green-full)	would	have	amounted	to	a	total	of	8317€	where	the	comparable	number	for	running	on	the	MSCC	platform	(purple-full)	would	have	been	a	mere	133€.		In	 essence	 the	 numbers	 demonstrate	 that	 the	 MSCC	 concept	 makes	 the	 conduction	 of	parameter	studies	as	vast	as	the	chosen	example	possible	in	the	first	place.	While	running	on	a	 (modestly	 sized)	 local	 cluster	 would	 invoke	 working-	 and	 computing	 times	 of	 1000s	 of	Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	6	hours	 and	 thus	 be	 practically	 unfeasible,	 the	 hereby	 presented	 concept	 would	 actually	enable	the	completion	of	the	same	work-load	within	two	to	three	days.			Fig.	4:	Motorbike	tutorial	case	of	OpenFOAM	5.x,	re-built	to	yield	a	1.6x107	cells	mesh	instead	of	a	2.2x105	cells	mesh.			Fig.	5:	Wall	Clock	Time	(WCT)	against	number	of	cases	run	on	ZHAW	cluster	(blue)	and	on	MSCC	cloud	platform	(red).	Black	candlesticks	signify	run-time	of	individual	simulation	cases	(approximately	2h	per	case).	On	the	internal	cluster,	batches	of	a	maximum	of	four	cases	at	a	time	can	be	run,	while	the	cloud	platform	does	not	require	any	batch	turn-over	in	this	example,	since	maximum	batch	sizes	of	up	to	500	cases	can	be	realized.	Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	7		Fig.	6:	Wall	Clock	Time	(WCT)	in	hours	(left	axis)	and	Human	Resource	Costs	in	Euro	(right	axis)	against	number	of	cases	run	within	parameter	study.			5.2 Case	Study	B:	Motorbike	–	360°	Angle	of	Attack	Example	B	is	about	the	same	basic	simulation	case	as	example	A	(Motorbike,	OpenFOAM	5.x,	simpleFoam,	2.2x105	cells).	This	 time	however	a	 full	360°	aerodynamic	parameter	study	of	the	drag-	 and	 lift-	 coefficients	was	 conducted.	 In	 this	 context	 the	 case	was	parameterized	such	that	the	angle-of-attack	of	the	onset	air	velocity	as	well	as	the	entire	surrounding	wind-channel	was	laterally	rotated	step	by	step.	Step	sizes	of	3.6°	and	a	full	rotation	of	360°	were	choose.	The	zero-degree	Base	Case	was	uploaded	to	the	Web	Interface	and	the	MSCC	Run	Creator	 was	 applied	 such	 that	 100	 individual	 simulation	 cases	 were	 created	 and	 semi-automatically	 run	 in	 the	 cloud.	 A	 prepared	 Python	 post-processing	 utility	 was	 uploaded	along	 with	 the	 Base	 Case	 and	 executed	 such	 that	 one	 Paraview	 snapshot	 of	 the	 fluid-dynamic	situation	was	created	per	simulation	case	(see	e.g.	9	out	of	100	selected	snap-shots	in	 Fig.	 7).	 Another	 prepared	 Python	 utility	 was	 applied	 to	 subsequently	 parse	 the	downloaded	 simpleFoam	 logs,	 to	 extract	 and	 to	 evaluate	 one	 pair	 of	 drag-	 and	 lift	coefficients	per	 simulation	case.	The	whole	procedure	yielded	 i)	 a	 series	of	100	 Paraview-created	JPGs	and	a	full	360°	chart	of	the	aerodynamic	properties	of	the	Motorbiker	(for	180°	result	see	Fig.	8).		The	remarkable	point	in	this	context	is	not	so	much	the	result	itself	but	the	fact	that	the	whole	work-flow	could	be	conducted	within	a	mere	20min.		Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	8		Fig.7:	Nine	out	of	100	individual	snapshots	of	the	Motorbike	simpleFoam	tutorial	cases	computed	with	OpenFOAM	5.x	in	the	MSCC	 cloud	 platform,	where	 the	 angle-of-attack	 of	 the	 onset	 relative	 airflow	 velocity	 as	well	 as	 the	wind-channel	were	automatically	 rotated	by	a	 total	of	360°	around	 the	Motorbike.	100	 individual	 cases	were	 conducted	and	 thus	evaluated	simultaneously	within	20min.				Fig.	8:	 Lift	 coefficients	 (cl)	 versus	drag	coefficients	 (cd)	of	OpenFOAM	5.x,	 simpleFoam	tutorial	 case	Motorbike	with	onset	relative	airflow	velocity	as	well	as	entire	wind	channel	being	rotated	step-by-step	counter-clock	wise	around	the	Motorbike.	Results	derived	from	semi-automatically	conducted	20min	parameter	study	using	MSCC	platform	depict	180°	out	of	a	total	of	360°	computed.		Academics	Abstract	OpenFOAM	Conference	2021,		G.Boiger,	et.al.,	ICP-ZHAW,	Winterthur	July	2021	9	6 References		[1] Boiger,	G.;	Sharman,	D.;	Boldrini,	M.;	Everitt,	V.;	Dominic,	D.,	2020.	Introducing	Massive	Simultaneous	 Cloud	 Computing	 (MSCC)	 for	 Multiphysics-Simulation	 Applications	(MSCC).	Multiphysics	2020.	15th	International	Conference	of	Multipysics,	Online,	10-11	December	2020.	International	Society	of	Multiphysics.	ISSN	(online)	2409-1669.	[2] https://app.kaleidosim.com/	[3] https://api.kaleidosim.com/	[4] Boiger,	 G.;	 Buff,	 V.;	 Sharman,	 D.;	 Boldrini,	 M.;	 Lienhard,	 V.;	 Dominic,	 D.,	 2020.	Simulation-based	investigation	of	tar	formation	in	after-treatment	systems	for	biomass	gasification	 (2020).	 Biomass	 Conversion	 and	 Biorefinery.	 2020.	 pp.	 1-18.	 DOI:	10.1007/s13399-020-00915-7		[5] Boiger,	G.;	Boldrini,	M.;	Lienhard,	V.;	Siyahhan,	B.;	Khawaja,	H.;	Moatamedi,	M.,	2019.	Multiphysics	Eulerian-Lagrangian	Electrostatic	Particle	Spray-	And	Deposition	Model	for	OpenFoam®	and	KaleidoSim®	Cloud-Platform	(2020).	 Int.Journal	of	Multiphysics.	14(1),	pp.	1-15.	DOI:	10.21152/1750-9548.14.1.1	[6] Boiger,G.,	 Bercan,S.;	 Viktor,L.;	 2020.	 Advancing	 the	 Validation	 and	 Application	 of	 a	Eulerian-Lagrangian	 Multiphysics	 Solver	 for	 Coating	 Processes	 in	 Terms	 of	 Massive	Simultaneous	 Cloud	 Computing.	Multiphysics	 2020.	 15th	 International	 Conference	 of	Multipysics,	Online,	 10-11	December	 2020.	 International	 Society	 of	Multiphysics.	 ISSN	(online)	2409-1669.	[7] https://www.paraview.org/Wiki/ParaView/Python_Scripting	[8] https://www.openfoam.com/documentation/user-guide/a-reference/a.1-standard-solvers	[9] Thévenin	 D.,	 Janiga	 G.;	 2008.	 Optimization	 and	 computational	 fluid	 dynamics.	 Berlin,	Heidelberg:	Springer,	2008.	DOI:	10.1007/978-3-540-72153-6		

A massive simultaneous cloud computing platform for OpenFOAM

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A massive simultaneous cloud computing platform for OpenFOAM

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