Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) is widely used in industry and academic research to investigate complex fluid flow. The bottleneck of a realistic CFD simulation is its long simulation time. The simulation time is generally reduced by massively parallel Central Processing Unit (CPU) clusters, which are very expensive. In this paper, it is shown that the CFD simulation can be accelerated significantly by a novel hardware called General Purpose Computing on Graphical Processing Units (GPGPU). GPGPU is a cost-effective computing cluster, which uses the Compute Unified Device Architecture (CUDA) of NVIDIA devices to transform the GPU into a massively parallel processor. The paper demonstrates the faster computing ability of GPU compared to a traditional multi-core CPU. Two scenarios are simulated; one is a 2-dimensional simulation of regular wave and another one is a 3-dimensional motion of a floating ship on a regular wave. A smoothed particle hydrodynamics (SPH) based CFD solver is used for simulating the complex free-surface flow. The performance of a single GPU is compared against a commonly used 16 core CPU. For a large simulation of 6 degrees of freedom (DOF) ship motion simulation, the comparative study exhibits a speedup of more than an order of magnitude, reducing simulation time from 30 hours to about 2 hours. This indicates a CUDA enabled GPU card can be used as a cost-effective computing tool for a reliable and accurate SPH-based CFD simulation. The cost-benefit analysis of GPU over a CPU cluster is also discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".