Rheological Models for Describing Fine-laden Debris Flows: Grain-size Effect
Bibliographic record
Abstract
본 연구는 토석류의 유통성과 관련하여 세립토의 흐름특성, 유변학적 모델들의 적용가능성 및 액성상태 의존성 유변학적 특성들을 비교 분석하였다. 입자크기에 따른 유변학적 특성을 살펴보고자 점토질이 풍부한 지중해 해저점토와 실트질이 풍부한 캐나다 동부 뉴펀들랜드 와부시 호수에서 채취한 광미에 대한 물성특성을 분석하였다. 점토질이 풍부한 세립토의 경우 전형적인 전단담화(shear thinning) 거동을 보이는 반면, 실트질 광미의 경우는 전단담화와 Bingham 유체 거동을 함께 보인다. 후자의 경우, 전단변형률속도를 높임에 따라 Bingham 유체처럼 거동하였다. 이러한 현상학적 차이는 입자크기에 따른 유동특성곡선의 차이에서 기인한 것이다. 항복응력과 소성점도의 결정은 전단변형에 의한 유동 입자들의 구조적 변화와 응력상태와 관련되기 때문이다. 세립토(< 0.075mm)를 다량 함유한 토석류의 유동성을 역해석하고자 할 때, 퇴적형상(흐름 양상, 퇴적층의 모양, 두께 및 길이 등)은 항복응력과 소성점도에 의해 결정된다. 항복응력과 소성점도는 액성지수의 함수로 나타낼 수 있으므로, 토석류의 발생가능지역에서 액성상태에 따른 토석류의 유동성을 평가할 지표로 활용할 수 있다. This paper presents the applicability of rheological models for describing fine-laden debris flows and analyzes the flow characteristics as a function of grain size. Two types of soil samples were used: (1) clayey soils - Mediterranean Sea clays and (2) silty soils - iron ore tailings from Newfoundland, Canada. Clayey soil samples show a typical shear thinning behavior but silty soil samples exhibit the transition from shear thinning to the Bingham fluid as shear rate is increased. It may be due to the fact that the determination of yield stress and plastic viscosity is strongly dependent upon interstructrual interaction and strength evolution between soil particles. So grain size effect produces different flow curves. For modeling debris flows that are mainly composed of fine-grained sediments (<0.075 mm), we need the yield stress and plastic viscosity to mimic the flow patterns like shape of deposition, thickness, length of debris flow, and so on. These values correlate with the liquidity index. Thus one can estimate the debris flow mobility if one can measure the physical properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".