Estimating the air exchange rates in naturally ventilated cattle houses using Bayesian-optimized GBDT

It is challenging to estimate the air exchange rate(AER)dynamically in naturally ventilated livestock buildings such as dairy houses due to the influence of complex and variable outdoor environmental factors,large opening ratios,and the confusion of inflow and outflow at openings.This makes it difficult to efficiently regulate the opening ratio to meet the ventilation requirements in naturally ventilated livestock buildings.In this study,the air exchange rates of naturally ventilated cattle houses(NVCHs)in different seasons and opening ratios were obtained through field measurements and computational fluid dynamics(CFD)simulations.A fast and efficient machine learning framework was proposed and examined to predict AER based on the gradient boosting decision tree(GBDT)combined with Bayesian optimization.Compared with commonly used machine learning models such as multilayer perceptrons(MLPs)and support vector machines(SVMs),the proposed GBDT model has higher prediction accuracy and can avoid falling easily into local optima.Compared with the existing mechanical model based on the Bernoulli equation,the proposed GBDT model showed a slightly higher prediction than the mechanistic model and was much easier to use in AER estimation when inputting easily collected environmental factors in practical applications.Using Bayesian optimization could dramatically reduce the computing time when determining the optimal hyperparameter for establishing the GBDT model,dramatically saving on computing resources.Based on the Bayesian optimized GBDT model,the desirable opening ratio of the side curtain can be determined for automatically regulating the AER of cattle houses in future applications.

Numerical investigation on effects of side curtain opening behavior on indoor climate of naturally ventilated dairy buildings

The side-curtain is popular in cattle buildings to regulate indoor climates and ventilation rates by adjusting the opening ratio.It normally had three different adjusting strategies relate to the position of rollers,i.e.central roller(S1),top roller(S2)and bottom roller(S3),which result in different opening behaviors to generate the same opening ratio but different opening positions in the side wall for a full-curtain house.Numerical simulations were conducted using computational fluid dynamics(CFD)to investigate the effects of the eight potential opening behaviors of side curtains on the indoor climates and airflow rates in winter for a typical naturally ventilated dairy house in China when the opening ratio were 8.5%and 17%.Airflow patterns,wind chilled temperature(WCT)and age of air were analyzed in the animal occupied zone(AOZ)by taking reference planes.Openings at the very bottom of side walls had more efficient ventilation due to the younger air age,more effective air disturbing,more uniformly distributed indicators in AOZ.However,it will result in a lower WCT in AOZ although a lower ventilation rate was observed in this case.Openings on the very top of side wall would generate a better thermal comfort in AOZ but with very poor air quality and nonuniformly distributed airflows in the dairy house.S1 was not recommended to the practical application due to the poor indoor climate and the higher cost of the mechanical structure.Based on the comprehensive evaluations of the analytic hierarchy process,the most satisfaction opening positions were at the bottom of the side curtains and the optimized adjusting strategy is S2.