Abstract: A method of forming a gas turbine engine component, the method including forming a plurality of cooling apertures in a preform structure of the component, the plurality of cooling apertures of the preform structure comprising a first cooling aperture and a second cooling aperture, wherein cross-sectional shapes of the first and second cooling apertures of the preform structure are different from one another, as measured in a same relative plane; and applying a coating to at least a portion of the preform structure to form the component, wherein a cross-sectional shape of the first and second cooling apertures of the component are approximately the same as one another, as measured in the same relative plane.

Abstract: A turbine vane in a gas turbine engine includes an inner platform, an outer platform, and a vane airfoil positioned therebetween. The vane airfoil includes a first cooling passage extending between the outer platform and the inner platform, and a second cooling passage extending between the outer platform and the inner platform. The second cooling passage is arranged downstream of the first cooling passage with respect to a flow direction. The turbine vane includes a jumper tube disposed between the second cooling passage and the inner platform. The jumper tube includes an inlet, an outlet, and a tube wall enclosing a hollow interior. The inlet is positioned a distance within the second cooling passage. The outlet is positioned at least partially through an aperture of the inner platform.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.