Optimal Placement of Electronic Components to Minimize Heat Flux Nonuniformities

Hengeveld, Derek W.; Braun, James E.; Groll, Eckhard A.; Williams, Andrew D.

doi:10.2514/1.47507

Cited by 26 publications

(10 citation statements)

References 22 publications

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“…In several studies thermally optimized component placements were investigated both globally and locally. 26,27 On average, optimized component distributions reduced maximum temperatures, increased minimum temperatures, and reduced maximum temperature differences by 5.4 K, 7.1 K, and 12.6 K, respectively, over evenly distributed components. The largest and smallest maximum temperature difference reductions were 17.8 K (at 400 W) and 9.5 K (at 100 W), respectively.…”

Section: B Reduced Temperature Gradientsmentioning

confidence: 97%

Enabling Future Spacecraft Missions through Isothermal Bus Thermal Management

Hengeveld

2016

46th AIAA Thermophysics Conference

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The Isothermal Bus architecture is a novel idea focused on approaching a single thermal node S/C representation. In effect, heat is shared efficiently between cold and hot components and in combination with thermal control, spatial and temporal variations in temperature are minimized. The Isothermal Bus can enable higher capability systems along with providing schedule and cost savings. A nominal case study was provided that compared a traditional TCS to the Isothermal Bus concept with a 7:1 switching ratio. While the traditional TCS could accommodate 300 W of bus power, it would require survival heaters. However, the Isothermal Bus concept would enable a 167% increase in bus power while almost eliminating the need for survival heat. I. Nomenclature AI&T= Assembly, Integration, and Test ATT = Active Thermal Tile HTC = High Thermal Conductance ITEMS = Integrated Thermal Energy Management System LEO = Low Earth Orbit MLI = Multilayer Insulation PnP = Plug-and-play OLR = Outgoing Longwave Radiation S/C = Spacecraft SMARTS = Satellite Modular and Reconfigurable Thermal System TCP = Thermal Control Panel TCS = Thermal Control Subsystem VHT = Variable Heat Transfer II. Introduction PACE exploitation provides tremendous opportunities. Since 1957, spacecraft (S/C) have been developed to take advantage of this new high ground by providing communication, scientific observation, weather monitoring, navigation, remote sensing, surveillance, and data-relay services. 1 However, space presents extraordinary challenges. Consequently, S/C have become exceedingly complex and costly. Current S/C can take 1 Senior Engineer, AIAA Senior Member. 2 Senior Mechanical Engineer, Space Vehicles Directorate, AIAA Senior Member. 3 Mechanical Engineer, Space Vehicles Directorate, AIAA Member. S Downloaded by PURDUE UNIVERSITY on June 24, 2016 | http://arc.aiaa.org |

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Section: B Reduced Temperature Gradientsmentioning

confidence: 97%

Enabling Future Spacecraft Missions through Isothermal Bus Thermal Management

Hengeveld

2016

46th AIAA Thermophysics Conference

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“…The decoder first receives the input h W from the encoder, and then decodes a number at each step until generating a special [EOS] token. At step t, the decoded number y t is in the range of [1,100], which exactly indicates the positions in the layout. Therefore, the decoding process can be regarded as a classification task, where the total number of classes is 101, 100 numbers and a special [EOS] token.…”

Section: • Decodermentioning

confidence: 99%

“…Heat source layout design (HSLD) is encountered in many fields of engineering and science, particularly in the electronics [1]. The objective of HSLD is to design the reasonable source layout scheme to offer the expected thermal environment for the whole system.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Method for Heat Source Layout Inverse Design

Sun

Zhang²,

Zhang³

et al. 2020

IEEE Access

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Heat source layout design is an effective technique to enhance the thermal performance in the whole system, which has become a vital part in many engineering fields, e.g. satellite layout design and integrated circuit design. Traditionally, the optimal design is obtained by searching the design space with the optimization technique which repeatedly runs the thermal simulation to compare the performance of each scheme. Due to the extremely large computational burden with this method, the optimization is greatly limited. To overcome the challenge, heat source layout inverse design (HSLID) is proposed in this paper to directly generate the layout scheme with the given thermal performance requirement. A novel method for heat source layout inverse design, denoted as SAR-HSLID, is proposed based on the recently deep learning technique, Show Attend and Read (SAR) model. Firstly, regarding the mapping from the required temperature field to layout scheme as an image-to-location task, this paper introduces SAR model, which is good at sequence predicting, to generate the layout scheme. The trained SAR is capable of learning the underlying physics of the design problem, thus can efficiently predict the design under given requirement without any physical simulation. Secondly, to ensure that the designed heat source layout exactly satisfies the input temperature field requirement, based on the layout predicted by SAR, we further utilize a simple but efficient optimization process to conduct few post-processing. Finally, a heat source layout inverse design task in a typical two-dimensional heat conduction problem is investigated to demonstrate the feasibility and effectiveness of the proposed method.

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“…As an example, Cuco et al (2015) presented a multi-objective method for the three-dimensional layout of a spacecraft, which combined a computer-aided design and an optimization tool to find the optimum positions of the equipment in consideration of their mass, inertia, and thermal requirements. Another work on this topic was reported by Hengeveld et al (2011), where the optimal placement of equipment on satellite panels with the aim of desired heat flux distribution was investigated. A genetic algorithm was implemented to solve the underlying optimization problem.…”

Section: Public Interest Statementmentioning

confidence: 99%