In this work, we will use a combination of theoretical and computational studies to develop improved insight into the flow physics of the unsteady aerodynamics of leading- and trailing-edge flows, which often involves vortex shedding. A key objective is to develop low-order predictive methods for such flows on unsteady airfoils, wings, and rotor blades. The improved understanding and low-order modeling capability will be useful in advancing the predictive capability for unsteady flows for rotorcraft applications.

Optimal performance of commercial swine populations depends on the interaction of several determinants including infectious diseases and factors related to management and environment such as mixing pigs from different sources, space allowance, and nutrition. Producers capture vast amounts of data but store them in disconnected databases. Thus, there is a tremendous opportunity to pursue synergizing swine data. We will leverage ongoing initiatives and resources to develop, deploy, and promote the Predictors of Swine Performance (PROSPER), a digital platform to capture, integrate, analyze, and visualize data of multiple sources in an ongoing and automated fashion. Causal models for observational dataset will be developed and implemented allowing producers to identify and measure the effect of various factors on swine performance under their specific field conditions. We will also implement forecasting models to help producers to strategically allocate resources as needed to improve swine health & productivity of commercial flows. Strategic collaborations and extension activities within various swine industry stakeholders will target effective dissemination of knowledge generated in this proposal, driving the productivity of the swine industry forward. The process and models herein developed can be adapted to poultry, cattle, and other livestock. In summary, the project will develop, deploy, and promote the Precision Animal Agriculture concept in swine. Activities will cultivate the implementation of technologies and applied knowledge to support producers making data-driven decisions to significantly improve swine performance, strengthening the sustainability and the competitiveness of the US pork production.