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Corrugated-core sandwich structures with integrated acoustic resonator arrays have been of recent interest for launch vehicle noise control applications. Previous tests and analyses have demonstrated the ability of this concept to increase sound absorption and reduce sound transmission at low frequencies. However, commercial aircraft manufacturers often require fibrous or foam blanket treatments for broadband noise control and thermal insulation. Consequently, it is of interest to further explore the noise control benefit and trade-offs of structurally integrated resonators when combined with various degrees of blanket noise treatment in an aircraft-representative cylindrical fuselage system. In this study, numerical models were developed to predict the effect of broadband and multi-tone structurally integrated resonator arrays on the interior noise level of cylindrical vibroacoustic systems.

Two tests of composite shells loaded under half-cosine impulsive loadings are discussed. One cylinder which included no other materials was modeled successfully such that calculated results matched test results out to late times. The other cylinder, which included an inner annulus of an elastomeric material, was less successfully modeled, even though the composite material was modeled similarly in both instances. A viscoelastic model and an elastic model were both used to model the elastomeric material, and the viscoelastic model produced significantly better results.

This paper1 explores some of the important considerations in devising a practical and consistent framework and methodology for working with experiments and experimental data in connection with modeling and prediction. The paper outlines a pragmatic and versatile “real-space” approach within which experimental and modeling uncertainties (correlated and uncorrelated, systematic and random, aleatory and epistemic) are treated to mitigate risk in modeling and prediction. The elements of data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. The considerations and options are many, and a large variety of viewpoints and precedents exist in the literature, as surveyed here. Rationale is given for the various choices taken in assembling the novel real-space end-to-end framework.

A consortium of interested parties has conducted an experimental characterization of two Tau parallel kinematic machines which were built as a part of the EU-funded project, SMErobot1. Characteristics such as machine stiffness, work envelope, repeatability and accuracy were considered. This paper will present a brief history of the Tau parallel machine, the results of this testing and some comment on prospective application to the aerospace industry.

This paper describes the development of a novel data model for storing and sharing data obtained from engine experiments, it then outlines a methodology for automatic model development and applies it to a state-of-the-art engine combustion model (including chemical kinetics) to reduce corresponding model parameter uncertainties with respect engine experiments. These challenges are met by adopting the latest developments in the semantic web to create a shared data model resource for the IC engine development community. The relevant data can be extracted and then used to set-up simulations for parameter estimation by passing it to the relevant application models. A methodology for incorporating experimental and model uncertainties into the model optimization procedure is presented.

An infrared laser absorption technique has been developed to measure in-cylinder concentrations of CO in an optical, automotive HCCI engine. The diagnostic employs a distributed-feedback, tunable diode laser selected to emit light at the R15 line of the first overtone of CO near 2.3 μm. The collimated laser beam makes multiple passes through the cylinder to increase its path length and its sampling volume. High-frequency modulation of the laser output (wavelength modulation spectroscopy) further enhances the signal-to-noise ratio and detection limits of CO. The diagnostic has been tested in the motored and fired engine, exhibiting better than 200-ppm sensitivity for 50-cycle ensemble-average values of CO concentration with 1-ms time resolution. Fired results demonstrate the ability of the diagnostic to quantify CO production during negative valve overlap (NVO) for a range of fueling conditions.

The use of Selective Catalytic Reduction (SCR) is becoming increasingly more popular as a way of reducing NOx emissions from heavy duty vehicles while maintaining competitive operating costs. In order to make efficient use of these systems, it's important to have a complete system approach when it comes to calibration of the engine and aftertreatment system. This paper presents a simplified model of a heavy duty SCR catalyst, primarily intended for use in combination with an engine-out emissions model to perform model based offline optimization of the complete system. The traditional way of modelling catalysts using a dense discretization of the catalyst channels and non-linear differential equation solvers to solve the heat and mass balance equations, requires too much computational power in this application. The presented model is also useful in other applications such as model based control.