What’s New in OpenPNM V3¶

It’s been a long process, much longer than we would’ve liked, but OpenPNM Version 3.0 is finally here. Version 2.0 was released in August 2018, and version 2.8.2, the last of the V2 series, was released in September 2021. This 3 year stretch saw a lot growth in the OpenPNM userbase, which if we use the Github star count as a proxy, showed a 300% increase over the period!

A lot has happened over these four years, and it’s been quite a blur so it’s hard to remember which milestones to highlight. One major event or theme was the PMEAL group receiving funding from CANARIE to support the growth of OpenPNM (and PoreSpy). This funding allowed for the hiring of many highly talented students who really took OpenPNM to the next level.

Overview of the Biggest Change¶

There have been a LOT of useful and powerful additions to V3, but perhaps the biggest change is what’s missing:

V3 has removed the concept of Geometry and Physics objects. This “workflow” was invented on the whiteboard during our initial planning sessions for V1 in 2011, and looked sensible at the time, but over the past 10+ years it became clear that this created a lot of confusion for users and a friction for the developers.

For comparison let’s look at how this changes the scripts. In version 2 you’d initialize 4 objects:

import openpnm as op

pn = op.network.Cubic(shape=[5, 5, 5], spacing=1e-5)
geo = op.network.SphersAndCylinders(network=pn, pores=pn.Ps, throats=pn.Ts)
air = op.phases.Air(network=pn)
phys = op.physics.Standard(network=pn, geometry=geo, phase=air)



While in version 3 the same result is obtained as follows:

import openpnm as op

pn = op.network.Cubic(shape=[5, 5, 5], spacing=1e-5)
air = op.phase.Air(network=pn)



This difference results in several important changes:

Back End Simplicity: The ‘back-end’ code complexity was massively reduced, which will make it far easier to maintain OpenPNM moving forward. There was quite a lot of machinery dedicated to tracking which pores and throats were assigned to geo, and that phys was associated with geo. For instance, in cases where multiple domains were used, such as geo1 and geo2 a lot of checks were done behind the scenes to ensure they did not conflict with each other and that all pores and throats were accounted for. There was also functionality required for ‘redefining’ which pores and throats they were applied to, and so on. Easily thousands of lines of code were deleted by this change.

Fewer Objects to Think About: Speaking of multiple domains, most users probably did not use this capability, but for those who did, this change will result in a much simpler experience. In most cases the effects of multiple domains are only relevant to geometries, so one might specify geo1 and geo2 each with different pore size distributions, but probably the same physics applied to each domain. In the old approach, you had to create a unique Physics object associated with each subdomain, but now you can just add a single set of physics models to a phase and they can apply everywhere.

Emphasizes Pore-Scale Models: Although we’ve attempted to make this new approach as smooth as possible, there is no such thing as a free lunch, and the user must still invest some effort in defining which models to assign (and where). We have created collections to facilitate this process, which are demonstrated above. In V2 when you initialized a StickAndBall object it added all the models as part of the initialization. In V3 you create a network, then add all the models yourself, which is where the collections come in handy. A positive take of this new approach is that users are now forced interact with pore-scale models, which are the foundation of OpenPNM’s calculations, so hopefully this encourage them to write and use their own models.

Easy Multidomain Control: We have created a new way defining multiple domains, or more to the point, we created a way to assign different pore-scale models to specific pores. In the pn.models dictionary you will find all the models defined on the network, but now each model name also includes the domain where it applies, so instead of 'pore.diameter', it’s 'pore.diameter@<xyz>', where @ is a delimiter and <xyz> is the name of a label on the network. The default is '@all', meaning the model applies everywhere. To create two domains with different pore sizes you now define different models such as pn.models['pore.diameter@domain1'] and pn.models['pore.diameter@domain2']. Conveniently, you don’t need to specify different models for other properties like 'pore.volume' (unless you want to).

The best way to illustrate this change is by example. The code snippets below will create a domain with 2 different pore sizes, but all other models the same. It will then change the domains so that different pores are assigned to each domain.

import openpnm as op
pn = op.network.Cubic(shape=[5, 5, 1], spacing=1.0)
Ps1 = pn.coords[:, 0] < 2
pn.set_label(label='domain1', pores=Ps1)
pn.set_label(label='domain2', pores=~Ps1)
ax = op.visualization.plot_coordinates(network=pn, pores=pn.pores('domain1'), c='r', s=100)
ax = op.visualization.plot_coordinates(network=pn, pores=pn.pores('domain2'), c='b', s=100, ax=ax)
op.visualization.plot_connections(pn, c='k', ax=ax);


Now let’s retrieve the stick_and_ball geometry model collection, create a copy, alter the pore sizes on them, then assign each to different pores:

mods1 = op.models.collections.geometry.spheres_and_cylinders
mods2 = mods1.copy()

pn.add_model_collection(mods1, domain='domain1')


Let’s replace the model used for computing the pore diameter with a random number between some limits, which we’ll use to create a layer of small and large pores.

pn.add_model(propname='pore.diameter',
model=op.models.geometry.pore_size.random,
num_range=[0.01, 0.2],
domain='domain1')
model=op.models.geometry.pore_size.random,
num_range=[0.8, 0.99],
domain='domain2')

ax = op.visualization.plot_coordinates(network=pn, s=500, size_by=pn['pore.diameter'], edgecolor='k')
op.visualization.plot_connections(pn, c='k', ax=ax);


This new approach may take some getting used to, but hopefully it has enough upsides to make it worth it. We don’t dive into all of these here, but let’s just look at one feature which will hopefully make it clear why this new approach simplifies the back-end. Lets change which pores belong to domain1 and domain2 just by adjusting the labels. In the previous version this required calls to the set_locations methods of all the geometry objects. In V3 we just need to change some pore labels and regenerate the models:

pn.set_label(label='domain1', pores=pn.coords[:, 0] < 3, mode='overwrite')
pn.set_label(label='domain2', pores=pn.coords[:, 0] > 3, mode='overwrite')
pn.regenerate_models()
ax = op.visualization.plot_coordinates(network=pn, s=500, size_by=pn['pore.diameter'], edgecolor='k')
op.visualization.plot_connections(pn, c='k', ax=ax);


The key here, from a back-end perspective is that OpenPNM did literally nothing behind the scenes. Only the labels were changed by the user, and OpenPNM just adapted.

Other Important Changes¶

There have been a handful of other breaking changes including:

• Renamed of the phases module to phase. This was purely cosmetic so that all modules have consistant pluralization.

• Removed the term “Generic” from the numerous class names to avoid overly complicating things. Instead of instantiating a GenericPhase it is now just Phase.

• Moved of the plotting tools to a new visualization module. Previously they were in topotools which was not really obvious.

• Overhaul of the io module to be a collection of clearly named functions like network_from_statoil rather than categorized functions like statoil.load. This is more consistant with other packages like pandas which offer functions like from_csv.

• The Bravais network generator has been removed in favor of specificly named classes like FaceCenteredCubic and BodyCenteredCubic.

• A lot of minor functions and features were removed. This was done in the spirit of keeping the code base small and manageble, which we’d grown to realize was a very important design consideration.

• Subdictionaries now work as expected, so that pn['pore.conduit_length']['pore1'] works the same as pn['pore.conduit_length.pore1']. We also updated many of the models to return Nt-by-3 arrays of conduit properties rather than a dictionary.

• Migrated to a new documentation engine and website generator, which creates very beautiful example pages from jupyter notebooks, making it super easy to write a high quality and detailed tutorial for the website. This paved the way for the next few items…

• Removed the metrics module, in favor of creating detailed examples on the website. We created these metrics as a way to make OpenPNM even easier to use, but they only obscurred from the user what were relatively simple calculations.

• Removed the materials module, which will also be replaced by detailed examples on the website.

• Transient solvers have been extended substantially. In V2 we’d implemented the basic Implicit and Crank-Nicolson methods, while in V3 we leverage the scipy.integrate.ivp_solve library to access their high-order explicit solvers. In V3.0 we have created wrappers for RK45, but more are planned. These wrappers are in the new integrators module.

• Redesigned the numerical solver framework. We added a new module called solvers containing classes which define how to wrap any arbitrary numerical solver library. At present we have wrappers for pypardiso and scipy.linalg.spsolve, but more are planned.

• A framework for estimating physical properties of mixtures. The property calculations are based on the chemicals package and provides a first approximation for computing properties that are functions on composition which may change during a simulation.

• Complete rewrite of the ordinary percolation algorithm, now called Drainage.

• Definition of pore size factors which contain all the information about the shape of pores and throats. This means that computing of diffusive flow through an element is done as $$N_A = D_{AB} \cdot f_{size} \Delta C_A$$ where the length and area of the element are embedded in $$f_{size}$$ which includes a rigorous analyztical description of the changing cross-sectional shape.

Full Changelog on Github¶

During this past 3 years we’ve also migrated all of our continuous integration testing and release process to Github Actions. One of the great side effects of this that we can now generate very detailed changelogs from the commit messages for each merge onto the dev branch. The full change can be viewed here.