N.B. My apologies for the absence of recent posts. It’s been a bit busy here in HPCDan’s world.
What can we learn from the oil well leak in the Gulf of Mexico? That deep water drilling is more common than most people realized? Yes, because much of the near surface oil has already been extracted. That a complex engineering tasks, such as capping an oil well one mile below the ocean’s surface, are difficult? Yes, that is why we call them complex. That all engineering projects involve risks? Yes, for all designs involve tradeoffs and error margins. That our civilization is overly dependent on petrolem as an energy source? Yes, but we will forget that lesson again. That nature is not infinitely resilient to human folly? Yes, though we still struggle to believe it. That local events have global consequences? Yes, but we should know that already.
Coupled Models
Though all of these rethorical questions have answers in the affirmative, I suspect all of us in computational science looked at the disaster in the Gulf and saw something else – a devilishly complex multidisciplinary system with emergent behaviors across a wide range of temporal and spatial scales. We saw a modeling problem whose complete solution lies beyond our current reach at a time when we all hoped to be of more help.
Let’s begin with the reservior models of subsurface attributes (e.g., porosity, permeability and water mobility) that guide selection of drilling sites. Obtaining accurate numerical solutions from the resulting discretizations remains a challenging parallel computation. In some larger sense, however, this is the easy, relatively well understood problem.
Next up are the computational fluid dynamics (CFD) models of oil dispersion by wind and waves, and their interactions with shallow water features and barrier islands. Turbulence and mixing, viscosity, dispersants, complex geometries and multiple time scales expose the theoretical and practical limits of our parallel, numerical solutions to the Navier-Stokes equations. But we are just getting started.
Next come biophysics models and interactions among surface illumination, bacterial breakdown of oil droplets, and the effects of effulent driven ocean deoxygenation. These coupled models must be embedded in the CFD models to produce time varying estimates of microbiological effects.
Now it is time to get serious.
Next up are the species-specific models of petroleum ingestion and interaction, the effects on organism behavior and longevity, individual population dynamics and their interplay in the food chain. These are followed by long-term ecological models of wetlands and marine estuaries and statistical models of the possible benefits and adverse effects of human remediation. Finally, we reach models of human physiology and public health. These are followed by a host of societal and economic models, ranging from fishing and tourism through the global energy markets to the broader economic markets.
Solution Methodologies
Even the most cursory examination of the models and problems enumerated above exposes three very sobering truths. First, our ability to couple discrete and continuous models, each with their own approximations and limitations, across such enormous temporal and spatial ranges pales before the magnitude and complexity of this problem. We can only solve pieces of it. Second, even given the numerical tools, we lack the software engineering and programming methodologies to assemble, test and verify an integrated solution. Third, even given the first two, the computational demands of an integrated, fully multidisciplinary, parametric simulation study of the oil spill and its effects would make accurate climate modeling seem like child’s play on an abacus by comparison.
Let us be humbled by what we cannot do and challenged by how far we have to go. We have much work to do. These are computational science problems of the very highest order.
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