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Mini-DAQ: A lightweight, low-cost, high resolution, data acquisition system for wave energy converter testing

HardwareX

Bosma, Bret; Coe, Ryan G.; Bacelli, Giorgio B.; Brekken, Ted; Gunawan, Budi G.

As part of the development process, scaled testing of wave energy converter devices are necessary to prove a concept, study hydrodynamics, and validate control system approaches. Creating a low-cost, small, lightweight data acquisition system suitable for scaled testing is often a barrier for wave energy converter developers’ ability to test such devices. This paper outlines an open-source solution to these issues, which can be customized based on specific needs. This will help developers with limited resources along a path toward commercialization.

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TYPE Conference Poster YEAR 2022
DOIOSTIScopus

Perspectives on the integration between first-principles and data-driven modeling

Computers and Chemical Engineering

Bradley, William; Kim, Jinhyeun; Kilwein, Zachary A.; Blakely, Logan; Eydenberg, Michael S.; Jalvin, Jordan; Laird, Carl; Boukouvala, Fani

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus
Results 3501–3525 of 96,771
Results 3501–3525 of 96,771