Wire Robot Suspension Systems for Wind Tunnels
Bibliographic record
Abstract
In the past decade, the main focus in ship hydrodynamic simulation was the computation of the viscous flow around a ship at constant speed and parallel inflow to the ship longitudinal axis. Meanwhile, the numerical methods developed by extensive research allow to simulate the viscous flow around a maneuvering vessel. Having these methods at hand, experimental data are required for the validation of the applied simulation models. These data can be obtained e.g. by wind tunnel experiments. Here, particularly the velocity distribution around the body and forces of the flow during a predefined motion are of interest. The motion of the ship model can be realized by a superposition of longitudinal motion simulated through the inflow in the wind tunnel and a transverse or rotational motion of the ship realized by a suspension mechanism. Mechanisms for guiding a ship model along a predefined trajectory are known e.g. from towing tank applications. However, the design criteria for these mechanisms are totally different from a wind tunnel suspension system. In the towing tank, the weight of the studied vessel is compensated by the buoyancy force. On the other hand, the required forces to move the model along a trajectory are much higher due to the higher density and mass of the water in comparison with air. In the wind tunnel application, the mass of the model leads to gravity and inertia forces which have to be compensated by the suspension system. This chapter describes the development of a suspension system based on wire robot technology. Wire robots use wires for the suspension of their end effectors. In this application, this is very advantageous since wires have a relatively small aerodynamical footprint and allow for high loads. The system described within this chapter is installed at the Technical University Hamburg-Harburg, where ship models must be moved on defined trajectories within the wind tunnel, as described above (Sturm & Schramm, 2010). The application requires the motion of heavyweight payloads up to 100kg with a frequency of up to 0.5Hz for the translational degrees-of-freedom and up to 2.5Hz for the rotational degrees-of-freedom. Within this chapter, at first a short historical review of the very active wire robot research within the last years is given in section 2. Afterwards, an appropriate design of the wire robot system is discussed in section 3. Due to the adaptability of the wire robot concept, different geometries are possible. Based upon the mechatronic development process according to VDI (2004), two designs are investigated in section 3. Therefore, virtual prototypes using mathematical models and numerical simulation are developed in sections 3.1 and 3.2. Based on the simulation results, the two designs are compared in section 3.3. Using numerical 2
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".