Ultra-power-dense heat exchanger development through genetic algorithm design and additive manufacturing

Moon, Hyunkyu; McGregor, Davis J.; Miljkovic, Nenad; King, William P.

doi:10.1016/j.joule.2021.08.004

Cited by 40 publications

(18 citation statements)

References 44 publications

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“…However, with the wide design freedom provided by AM, new enhanced features can be easily integrated inside the coolant flow channel of the AM condenser in a single manufacturing process to further increase overall performance. [27,28,56,57,58]…”

Section: Fabrication Of a Codesigned Nanostructured Additively Manufa...mentioning

confidence: 99%

Ultrascalable Surface Structuring Strategy of Metal Additively Manufactured Materials for Enhanced Condensation

Rabbi

Khodakarami

et al. 2022

Advanced Science

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Metal additive manufacturing (AM) enables unparalleled design freedom for the development of optimized devices in a plethora of applications. The requirement for the use of nonconventional aluminum alloys such as AlSi10Mg has made the rational micro/nanostructuring of metal AM challenging. Here, the techniques are developed and the fundamental mechanisms governing the micro/nanostructuring of AlSi10Mg, the most common metal AM material, are investigated. A surface structuring technique is rationally devised to form previously unexplored two-tier nanoscale architectures that enable remarkably low adhesion, excellent resilience to condensation flooding, and enhanced liquid-vapor phase transition. Using condensation as a demonstration framework, it is shown that the two-tier nanostructures achieve 6× higher heat transfer coefficient when compared to the best filmwise condensation. The study demonstrates that AM-enabled nanostructuring is optimal for confining droplets while reducing adhesion to facilitate droplet detachment. Extensive benchmarking with past reported data shows that the demonstrated heat transfer enhancement has not been achieved previously under high supersaturation conditions using conventional aluminum, further motivating the need for AM nanostructures. Finally, it has been demonstrated that the synergistic combination of wide AM design freedom and optimal AM nanostructuring method can provide an ultracompact condenser having excellent thermal performance and power density.

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Section: Fabrication Of a Codesigned Nanostructured Additively Manufa...mentioning

confidence: 99%

Ultrascalable Surface Structuring Strategy of Metal Additively Manufactured Materials for Enhanced Condensation

Rabbi

Khodakarami

et al. 2022

Advanced Science

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“…202 By combing a genetic algorithm design and additive manufacturing, Moon et al fabricated a heat exchanger with optimal fin geometry and achieved a power density of 26.6 W/ cm 3 , as shown in Figure 12b. 196 On the other hand, based on a small set of experimental data on shell-tube heat exchangers, Luo et al trained ANN using normal backpropagation and different architectures for the prediction of heat-transfer rates in segmental baffles and continuous helical baffles. 203 Their results show a better prediction of heat-transfer rate than empirical correlations, which is the preliminary stage for ML-enabled optimization.…”

Section: Optimization Of Thermal Energy Applicationsmentioning

confidence: 99%

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Dai

2022

ACS Energy Lett.

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Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

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“…Advancement in heat exchangers: The bleak prospective for heat engines as waste heat converters for lower temperature assumes the cost of heat exchangers follows the historical heat exchanger price trend. Major innovations 45,46 in heat exchanger design are being made with new manufacturing techniques that could provide disruptive cost advantages. If the cost of heat exchangers can be significantly decreased, there is far more opportunity for heat engines in the waste heat conversion space.…”

Section: Advancement In Heat Enginesmentioning

confidence: 99%

Techno-economic analysis of waste-heat conversion

Geffroy¹,

Lilley²,

Parez³

et al. 2021

Joule

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Ultra-power-dense heat exchanger development through genetic algorithm design and additive manufacturing

Cited by 40 publications

References 44 publications

Ultrascalable Surface Structuring Strategy of Metal Additively Manufactured Materials for Enhanced Condensation

Ultrascalable Surface Structuring Strategy of Metal Additively Manufactured Materials for Enhanced Condensation

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Techno-economic analysis of waste-heat conversion

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