All Work Published on Sciences (Social, Health, Biological, Physical)
This study aims to understand the impact of instabilities and turbulence arising from canopy mixing layers on wind-driven wildfire spread. Using an experimental flume (water) setup with model vegetation canopy and thermally buoyant plumes, we study the influence of canopy-induced shear and turbulence on the behavior of buoyant plume trajectories. Using the length of the canopy upstream of the plume source to vary the strength of the canopy turbulence, we observed behaviors of the plume trajectory under varying turbulence yet constant cross-flow conditions. Results indicate that increasing canopy turbulence corresponds to increased strength of vertical oscillatory motion and variability in the plume trajectory/position. Furthermore, we find that the canopy coherent structures characterized at the plume source set the intensity and frequency at which the plume oscillates. These perturbations then move longitudinally along the length of the plume at the speed of the free stream velocity. However, the buoyancy developed by the plume can resist this impact of the canopy structures. Due to these competing effects, the oscillatory behavior of plumes in canopy systems is observed more significantly in systems where the canopy turbulence is dominant. These effects also have an influence on the mixing and entrainment of the plumes. We offer scaling analyses to find flow regimes in which canopy induced turbulence would be relevant in plume dynamics.
This study aims to understand the impact of instabilities and turbulence arising from canopy mixing layers on wind-driven wildfire spread. Using an experimental flume (water) setup with model vegetation canopy and thermally buoyant plumes, we study the influence of canopy-induced shear and turbulence on the behavior of buoyant plume trajectories. Using the length of the canopy upstream of the plume source to vary the strength of the canopy turbulence, we observed behaviors of the plume trajectory under varying turbulence yet constant cross-flow conditions. Results indicate that increasing canopy turbulence corresponds to increased strength of vertical oscillatory motion and variability in the plume trajectory/position. Furthermore, we find that the canopy coherent structures characterized at the plume source set the intensity and frequency at which the plume oscillates. These perturbations then move longitudinally along the length of the plume at the speed of the free stream velocity. However, the buoyancy developed by the plume can resist this impact of the canopy structures. Due to these competing effects, the oscillatory behavior of plumes in canopy systems is observed more significantly in systems where the canopy turbulence is dominant. These effects also have an influence on the mixing and entrainment of the plumes. We offer scaling analyses to find flow regimes in which canopy induced turbulence would be relevant in plume dynamics.
Stanford scientists in Senegal hunting for schistosomiasis—a parasitic disease infecting 200+ million people worldwide—used AI to transform local field work into satellite-powered disease mapping.
Stanford scientists in Senegal hunting for schistosomiasis—a parasitic disease infecting 200+ million people worldwide—used AI to transform local field work into satellite-powered disease mapping.
Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.
Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.
From Privacy to ‘Glass Box’ AI, Stanford Students Are Targeting Real-World Problems
An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.
An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.
QuantiPhy is a new benchmark and training framework that evaluates whether AI can numerically reason about physical properties in video images. QuantiPhy reveals that today’s models struggle with basic estimates of size, speed, and distance but offers a way forward.
QuantiPhy is a new benchmark and training framework that evaluates whether AI can numerically reason about physical properties in video images. QuantiPhy reveals that today’s models struggle with basic estimates of size, speed, and distance but offers a way forward.
