Resilience and Sustainability:
an engineer’s tale and take on statistical physics to social impact
Abstract:
Among the many perils of climate change, I fear that we got complacent to the yearly havoc caused by hurricanes, floods and wild fires to our communities. Shocked by the news flash, we move on an instant later in the hope that someone will come and fix it: call the plumber, the electrician, the builder,… and don’t forget the insurance. Did you call an engineer? No? Shouldn’t they have been able to avoid the havoc in the first place, but didn’t. Why?*
In this talk, I will show one way how to address resilience and sustainability in a quantitative way from the building to the community and city scale. Much of what I’ll show originates from some first principles of statistical physics, which my students and I aim to translate -by analogy- into engineering tools; from molecular-infused structural simulations to insufficient city-scale water adsorption phenomena in ground and stone, that entail flooding. Finally, by linking city-scale mechanistic results to census data, I hope that I’ll be able to convince you that there is a social/cultural meaning to resilience and sustainability, which is amplified by ever growing social inequality. Maybe you should call (yourself) an Engineer.
*Engineers only get fame, when things go wrong.
Bio:
Franz-Josef Ulm, professor at MIT since January 1999. Homebound during Covid, I am glad to be back in my office at MIT.
Summary:
Statistical physics: Theory of thermodynamic ensembles of individuals
Focus of talk: Resilience of buildings, communities and cities
Systems: cities, construction materials, social equity
Events: hurricanes, earthquakes, precipitation, fire
Hurricanes and City Texture
Classical engineering:
damage = drag coefficient * wind speed2
Can compute easily
But buildings built to code did not survive winds that were supposedly resistant
Simulations of wind through city streets show that streets amplify wind speeds
Generalize to arbitrary cities
Coordination number: Can approximate city density by looking at the distribution of distances between buildings
Related to definition of solids vs liquids in statistical physics
Cities that are more liquid/disordered are safer against hurricanes
Using this summary metric makes it possible to approximate hurricane risk without much data or compute power
Can be done anywhere in the world
Easy to communicate to stakeholders
Describing damage to buildings
Represent building as a molecular-type graph structure
Use physics summary representations to describe damage patterns
Define a kinetic temperature of structures based on theory of gases
Flooding
Classical approach: 3D models of water flow
Alternative: Lattice Density Functional Theory
Isotherm:
Put material at certain pressure
How much of liquid will be absorbed into material
Applied to precipitation
Look at water pressure from outside
How much water is absorbed from various city elements
Separate adsorption vs desorption curves: means that water may be temporarily stored in structures after precipitation stops
Fire
Sprinklers control temperature by cooling air, which cools structure
Can model in reduced form
Socioeconomic sustainability impact
Can map damage risk
Look for impact:
Economic
Monetary benefits from mitigating damage
Environmental
Social
Damage risk allocated inequitably across populations
E.g. Miami-Dade county is 17% exposed to damage but 44% exposed for financially challenged households