Heat Transfer in an Airfoil Shaped Strut

The heat transfer from a hot primary flow stream passing over the outside of an airfoil shaped strut to a cool secondary flow stream passing through the inside of that strut was studied experimentally and numerically. The results showed that the heat transfer on the inside of the strut could be reliably modeled as a developing flow and described using a power law model. The heat transfer on the outside of the strut was complicated by flow separation and stall on the suction side of the strut at high angles of attack. This separation was quite sensitive to the condition of the turbulence in the flow passing over the strut, with the size of the separated wake changing significantly as the mean magnitude and levels of anisotropy were varied. The point of first stall moved by as much as 15% of the chord, while average heat transfer levels changed by 2-5% as the inlet condition was varied. This dependence on inlet conditions meant that comparisons between experiment and steady RANS based CFD were quite poor. Differences between the CFD and experiment were attributed to anisotropic and unsteady effects. The coupling between the two flows was shown to be quite low - that is to say, heat transfer coefficients on both the inner and outer surfaces of the strut were relatively unaffected by the temperature of the strut, and it was possible to predict the temperature on the strut surface quite reliably using heat transfer data from decoupled tests, especially for CFD simulations.

Estimating Dynamic Treatment Effects from Project STAR (Working Paper 36)

This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservable is crucial and necessary with data from Project STAR