Comparison Between ANSYS Fluent and Solidworks Internal Flow Simulation for Analysis of A Fuzzy Logic Controller-Based Heating/Cooling System in A Mobile Robot Design
Bibliographic record
Abstract
Nowadays, Computational Fluid Dynamics (CFD) software's have become important tools to study the behavior of fluids in the design phase of robots in various applications. This includes fluid behavior around underwater remotely operated vehicles, aerodynamic characteristics of mobile robots/drones in extreme outdoor environments, and internal flow simulation to determine the heating/cooling time of an enclosed space and estimate the system's energy requirements. For this paper, two CFD software's; ANSYS Fluent and Solidworks 2022 are used to conduct a CFD analysis on the heating/cooling of the internal space of a mobile robot design aimed to operate in extreme temperatures ranging from −40 °C to 50 °C. Heating time is determined by using a constant power magnitude of 8138683 W/m3emitted from four different heating elements located at different parts of the robot body and two fans rotating at 1800 rpm. Moreover, the cooling time of the internal space is evaluated based on fans operating at 6000 rpm over the electronics and three outlets that direct the warm air to the outside environment. For both software's, the same boundary conditions were applied to the robot body to obtain fair results for comparison. Based on the simulation results, the desired temperature of 8 °C from an initial temperature of −40 °C was reached within 44 s for ANSYS Fluent and 650 s for Solidworks. For cooling, 8 °C was obtained within 6 s for ANSYS Fluent and 24 s for Solidworks. The difference in the results between the two software's is mainly due to the method in which the fluid domain is defined. In the case of Solidworks, it considers the material thickness on the boundary between solid/fluid regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".