بررسي آزمايشگاهي كاربرد ذره ي هوشمند در توصيف احتمالاتي ديناميك انتقال رسوب بستر بعد از آستانه

Smart-particle application for describing the probabilistic particle transport dynamics above threshold conditions

استفاده از حسگرهاي برداشت پارامترهاي ديناميك ذره مي‌تواند به شناخت بيشتر از ديناميك ذرات در حال انتقال منتج شود. در پژوهش حاضر، از يك ذره‌ي كروي پيش‌ساخته )با چگالي‌هاي متفاوت( كه با حسگرهاي ميكروالكترومكانيكال تجهيز شده است )ذره‌ي هوشمند(، براي برداشت داده‌هاي شتاب، چرخش و جهت ذره، هنگام انتقال در مجاورت بستر تحت جريان آب در فواصل زماني بسيار كوتاه ) ثانيه در پژوهش حاضر( استفاده شده است. در اين راستا، يك سري آزمايش با دبي‌هاي مختلف كه شرايط انتقال ضعيف )نزديك آستانه( و انتقال قوي )بعد از آستانه با اعداد رينولذر به نسبت بزرگ‌تر( در آن پوشش داده شده است، انجام شده است. انرژي جنبشي ذره به عنوان انرژي تبادل‌يافته بين ذره با پيرامون )جريان آب، ارتباط ذره با ذرات بستر( بررسي شده است. بررسي ميزان دريافت و هدررفت لحظه‌يي انرژي ذره نشان داد كه تغييرات تبادل انرژي با افزايش عدد رينولدز ذره به‌صورت نمايي كاهش مي‌يابد. توابع توزيع احتمالاتي حاكم بر انرژي جنبشي ذره توليد و براساس نتايج مشخص شد كه تابع توزيع احتمالاتي ويبول در شرايط آستانه و تابع توزيع احتمالاتي لوگ ـ نرمال در شرايط انتقال قوي‌تر و تعادلي، رفتار ذره را بهتر توصيف مي‌كند.

Coarse particle motion behavior plays a crucial role in sediment and hydraulic engineering, though its physics is still not fully understood. Disregarding the inherently stochastic nature of the sediment transport leads to various equations for bedload transport which are now being challenged due to their poor results. By applying sensors, like accelerometers and gyroscopes, particle transport physics could be carried out in a more scrutinized approach. In this study, an instrumented synthetic particle ("so-called" the smart particle) was designed and applied (with different densities) in sets of laboratory experiments that covered a hydraulics domain between low transport regime (near- and above-threshold) and higher transport regime (above threshold with a relatively high Reynolds number) conditions. Using the instrumented particle (smart-particle) could bring opportunities to learn more about the physics of the bed particle transport in rivers for different regimes and could bring data in hand for instantaneous particle changes throughout time (hear 0.004 seconds used for data sampling). Therefore, the kinetic energy as a parameter that delivers the behavior of particle energy interaction between the exposed particle and its surroundings (flow and the bed particles) was chosen to be studied. Since the dynamic features of the particle in transport are stochastic, the probability distribution functions, which could describe the particle behavior, were selected (Weibull, Lognormal, Normal, and Gamma distributions). In this case, it was shown that the Weibull distribution best described the particle kinetic energy in lower transport regimes, while for a higher transport regime, the Log-normal distribution worked better. Furthermore, the energy signals of the particle moving throughout the flume for different transport regimes were derived, and it was shown that the average energy gain and loss of the particle decreased exponentially as the particle Reynolds number increased. The presented results here could also be applied in similar hydraulic conditions in eco-hydraulic topics, specifically macro-plastic movement as bedload in river courses and the Aeolian research.