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This presentation provides a high-level overview of NASA's Future ATM Concepts Evaluation Tool (FACET) with a high-level description of the system's inputs and outputs. This presentation is designed to support the joint simulations that NASA and the Chinese Aeronautical Establishment (CAE) will conduct under an existing Memorandum of Understanding.
Objectives of AePW: Assess state-of-the-art Computational Aeroelasticity (CAe) methods as practical tools for the prediction of static and dynamic aeroelastic phenomena and responses on relevant geometries. - Perform comparative computational studies on selected test cases - Identify errors & uncertainties in computational aeroelastic methods - Identify gaps in existing aeroelastic databases - Provide roadmap of path forward (additional existing data sets, new experimental data sets, and analytical methods developments).
The core idea behind using machine learning (ML) for defect detection is that it can be used to detect flaws as they are being formed in an AM part. As the part is being made, a near-infrared (NIR) sensor records each layer and creates an image of the entire build layer. These images, usually thousands, can be compiled into a ‘3D’ array of the entire part. ML tools, such as a convolutional autoencoder (CAE) can go through these images and highlight potential anomalous regions of your part.
Abstract - Biochemical sensors for continuous monitoring require dependable periodic self- diagnosis with acceptable simplicity to check its functionality during operation. An in situ self- diagnostic technique for a dissolved oxygen microsensor is proposed in an effort to devise an intelligent microsensor system with an integrated electrochemical actuation electrode. With a built- in platinum microelectrode that surrounds the microsensor, two kinds of microenvironments, called the oxygen-saturated or oxygen-depleted phases, can be created by water electrolysis depending on the polarity. The functionality of the microsensor can be checked during these microenvironment phases. The polarographic oxygen microsensor is fabricated on a flexible polyimide substrate (Kapton) and the feasibility of the proposed concept is demonstrated in a physiological solution. The sensor responds properly during the oxygen-generating and oxygen- depleting phases. The use of these microenvironments for in situ self-calibration is discussed to achieve functional integration as well as structural integration of the microsensor system.
Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.