Abstract One of the major challenges in the development of micro-combustors is heat losses that result in flame quenching, and reduced combustion efficiency and performance. In this work, a novel thermal barrier coating (TBC) using hexagonal boron nitride (h-BN) nanosheets as building blocks was developed and applied to a Swiss roll micro-combustor for determining its heat losses with increased temperatures inside the combustor that contributes to improved performance. It was found that by using the h-BN TBC, the combustion temperature of the micro-combustor increased from 850 K to 970 K under the same thermal loading and operational conditions. This remarkable temperature increase using the BN TBC originated from its low cross-plane thermal conductivity of 0.4 W m−1 K−1to mitigate the heat loss from the micro-combustor plates. Such a low thermal conductivity in the h-BN TBC is attributed to its interfacial resistance between the nanosheets. The development of h-BN TBC provides an effective approach to improve thermal management for performance improvements of gas turbine engines, rocket engines, and all various kinds of micro-combustors.
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An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach
Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.
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- Award ID(s):
- 1920011
- NSF-PAR ID:
- 10297574
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 13
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 4375
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21134375
