Engineering topics

O'Malley, Brian

Publications and source records attributed to O'Malley, Brian.

3 records

DOE OSTI2024

Optimization of R290 variable geometry heat exchangers

Air-to-refrigerant heat exchangers (HX) are vital components in Heating, Ventilating and Air Conditioning and Refrigeration (HVAC&R) equipment. Recent literature has proposed utilizing shape-optimized, non-round tubes to reduce component size and refrigerant charge, thus enabling the adoption of low-GWP natural refrigerants like R290. However, most designs are restricted to fixed tube configurations, limiting the overall performance potential. In this work, multi-objective optimization is used in a staged approach to develop variable geometry multi-pass air-to-R290 HXs with minimal volume and airside pressure drop. Non-round tubes are used throughout the domain and each pass may have different tube arrangements to maximize HX performance. Compared to conventional fin-tube HXs, the optimized designs demonstrate more than 60% reductions in envelope volume, 30% reductions in face area, and up to 24% reduction in airside pressure drop. Additionally, the refrigerant charge reduction was up to 64%, thus enabling safe use of flammable and mildly-flammable refrigerants.

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DOE OSTI2024

Machine Learning Based Prediction of Airflow Maldistribution in A-Type Heat Exchangers

Airflow maldistribution is one of the primary causes of performance degradation in air-to refrigerant heat exchangers (HX) and has been shown to decrease heat transfer by as much as 35%. As a result, many units are oversized to meet the target capacity, resulting in increased system cost and refrigerant charge. Several studies have explored how characteristics like package type and HX geometry impact the flow profile, but results are restricted to a limited range of parameters and cannot be extrapolated to new designs. In this work, a machine learning (ML) model is trained to predict the inlet flow profile of dry air entering A-type HXs across a broad range of geometries and conditions. Flow profiles are generated using a porous media CFD model and used to train an Artificial Neural Network (ANN) which exhibits maximum and average relative L2 norm errors of 0.48 and 0.05. Additionally, these predictions take less than a second to generate resulting in a speed up factor of 2.42E5 compared to CFD. Component-level simulations are conducted to determine the performance degradation resulting from the predicted airflow maldistribution profiles. The new ML model will enable rapid and accurate prediction of performance degradation resulting from airflow maldistribution in A-type HXs, allowing for more accurate and cost-effective HX design.

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DOE OSTI2023

Review of Variable Geometry Air-to-Refrigerant Heat Exchangers

Air-to-refrigerant heat exchangers (HXs) are fundamental components to HVAC&R systems and as a result there is an ever-increasing need for performance and packaging improvements. Typically, HX’s are designed in uniform arrangements with the goal of providing the necessary heat transfer for minimal material costs. Recent research on fin tube heat exchangers (FTHX) with variation in one or more key parameters has shown significant potential for increasing heat transfer, reducing pressure losses, and making HXs more resilient to environmental externalities like airflow maldistribution and frost accumulation. In particular, researchers have increased heat transfer by 7.6% and j/f by 16% via non-uniform tube and winglet shapes. Additionally, multiple cases have shown that variable geometry heat exchangers (VGHX) are able to match the performance of conventional HXs with reductions in material mass and envelope volume of up to 33% and 40%, respectively. This paper presents a literature review of VGHXs specifically with regards to non-uniform FTHX’s.

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