Cooling an Array of High-Powered Miniature Robots Using Forced Air Convection
Bibliographic record
Abstract
A new cooling system for a fleet of scientific instruments in the form of miniature wireless robots designed for interactions at the nanometer-scale is assessed to determine its limitations. Unlike other approaches, the use of a cooling chamber allows us to remove an embedded cooling system and maintain the overall size of each robot to a minimum, hence increasing the density of instruments per surface area and resulting in enhanced performance of the platform. The goal of this paper is to assess the capacity of this cooling system; not only to remove heat but also to reduce temperature fluctuations and difference in temperature levels among the robots to maintain each robot within an operational temperature range of 0–70$^{\circ}{\rm C}$. One hundred dummy robots were therefore placed in a custom-built cooling chamber which uses forced air convection. The temperature levels of the dummy robots were recorded with power dissipations from 0 to 15 W/robot and a maximum air flow rate of 0.5 m/s. It was determined that the maximum range in difference in temperature levels among the dummy robots increases by$\sim{\hbox {20}}\;^{\circ}$C per additional 5 W/robot of power dissipation with an initial difference of$\sim{\hbox {40}}\;^{\circ}$C at 5 W/robot. An estimated total power dissipation of 10 W/robot was determined to be a safe limit in order to maintain the operating temperature range of the robots between 0–70$^{\circ}$C. For power dissipation over 10 W/robot, additional compensation methods are required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".