Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. A specific opportunity is to leverage Digital Thread to more efficiently design the next generation of products. This task is a data-driven design and decision problem under uncertainty. This paper explores this problem through three objectives: 1) Provide a mathematical definition of Digital Thread in the context of a specific engineering design problem. 2) Establish the feedback coupling of how information from Digital Thread enters the design problem. 3) Develop a a data-driven design methodology that incorporates information from Digital Thread into the next generation of product designs. The methodology is illustrated through an example design of a structural fiber steered composite component.

Acoustic diffraction allows sound to travel around opaque objects and therefore may allow beyond-line-of-sight sensing of remote sound sources. This paper reports simulated and experimental results for localizing sound sources based on fully shadowed microphone array measurements. The generic geometry includes a point source, a solid 90° wedge, and a receiving array that lies entirely in the shadow defined by the source location and the wedge. Source localization performance is assessed via matched-field (MF) ambiguity surfaces as a function of receiving array configuration, and received signal-to-noise ratio for the Bartlett and minimum variance distortionless (MVD) MF processors. Here, the sound propagation model is developed from a Green's function integral treatment. A simple 16 element line array of microphones is tested in three mutually orthogonal orientations. The experiments were conducted using an approximate 50-to-1-scaled tabletop model of a blind city-street intersection and produced ambiguity surfaces from source frequencies between 17.5 and 19 kHz that were incoherently summed. The experimental results suggest that a sound source may be localized by the MVD processor when using fully shadowed arrays that have significant aperture parallel to the edge of the wedge. However, this performance is reduced significantly for signal-to-noise ratios below 40 dB.

Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. Though Digital Thread is gaining traction as a digital communication framework to streamline design, manufacturing, and operational processes in order to more efficiently design, build and maintain engineering products, a principled mathematical formulation describing the manner in which Digital Thread can be used for critical design decisions remains absent. The contribution of this paper is to present such a formulation from the context of a data-driven design and decision problem under uncertainty. This formulation accounts for the fact that the design process is highly iterative and not all information is available at once. Output design decisions are made not only on what data to collect but also on the costs and benefits involved in experimentation and sensor instrumentation to collect that data. The mathematical formulation is illustrated through an example design of a structural fiber-steered composite component. In this example, the methodology highlights how different sequencing of small-scale experimentation with manufacturing and deployment lead to different designs and different associated costs.

This paper presents methodology to enable path planning for an unmanned aerial vehicle that uses dynamic data-driven flight capability estimation. The main contribution of the work is a general mathematical approach that leverages offline vehicle analysis and design data together with onboard sensor measurements to achieve dynamic path planning. The mathematical framework, expressed as a Constrained Partially Observable Markov Decision Process, accounts for vehicle capability constraints and is robust to modeling error and disturbances in both the vehicle process and measurement models. Vehicle capability constraints are incorporated using Probabilistic Support Vector Machine surrogates of highfidelity physics-based models that adequately capture the richness of the vehicle dynamics. Sensor measurements are treated in a general manner and can include combinations of multiple modalities such as GPS/IMU data as well as structural strain data of the airframe. Results are presented for a simulated 3-D environment and point-mass airplane model. The vehicle can dynamically adjust its trajectory according to the observations it receives about its current state of health, thereby retaining a high probability of survival and mission success.

Digital Thread is a data-driven architecture that links together information generated from across the product lifecycle. A specific opportunity is to leverage Digital Thread to more efficiently design the next generation of products. This task is a data-driven design and decision problem under uncertainty. This paper explores this problem through three objectives: 1) Provide a mathematical definition of Digital Thread in the context of a specific engineering design problem. 2) Establish the feedback coupling of how information from Digital Thread enters the design problem. 3) Develop a a data-driven design methodology that incorporates information from Digital Thread into the next generation of product designs. The methodology is illustrated through an example design of a structural fiber steered composite component.