Optimal Damping Design of Heavy Vehicle with Interconnected Hydro-Pneumatic Suspension
Bibliographic record
Abstract
The optimal damping design of roll plane interconnected hydro-pneumatic suspension is investigated, in order to improve vertical ride and road-friendliness of heavy vehicles, while maintaining enhanced roll stability. A nonlinear roll plane vehicle model is developed to study vertical as well as roll dynamics of heavy vehicles. The damping valves and gas chamber are integrated within the same suspension strut unit to realize compact design. The influence of variations in damping valve threshold velocity on relative roll stability is explored, under centrifugal acceleration excitations arising from steady turning and lane change maneuvers, as well as crosswind. The effects of damping valve design parameters on the vertical ride vibration and vehicle-road interaction characteristics are also investigated under a medium rough road input and two different vehicle speeds. A performance index is developed based on RMS sprung mass bounce acceleration and dynamic load coefficients (DLC) due to left and right tire loads. The design parameters of compression and rebound damping valves are optimized to minimize the performance index, while remaining improved roll stability. The benefits of the optimal damping designs on the vertical ride and road-friendliness of heavy vehicles are demonstrated, subjected to a wide range of road roughness and vehicle speed inputs. The shock isolation characteristics due to the baseline and optimal damping designs are further compared under discrete road bump inputs.
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".