Mario R Mondaca

Feasibility of a Dual Cooling System for Dairy Cows in Arizona

For many decades, systems that combine forced convection with evaporative cooling have been widely used to control temperature within greenhouses located in hot and arid regions. Evaporative cooling systems have also been proven to increase the productivity of dairy cows in arid and semi-arid regions. To enhance the capability of this kind of microclimate control operation and depending on the availability of the low-temperature groundwater source, we propose the addition of a cooling unit, placed in the bedding area where cows typically lie down. We hypothesize that direct conduction through such a mat could serve as an efficient and energy-saving cooling mechanism. To test this hypothesis, we use conjugate heat transfer analyses to examine the feasibility of dual cooling strategies. We base our test conditions on climate data typical of Arizona’s summer season. As our primary purpose, we seek to determine the efficacy of using a conduction cooling system to alleviate heat stress in cows. We test systems under various ambient air conditions because differences in air temperature directly affect the proportion of cooling that can be attributed to conduction. For example, under cool air conditions (18°C), conduction contributes 20 % of the cooling effect, whereas under hot air conditions (38°C) the contribution increases to nearly 90%. In addition to varying air temperature, we evaluate different bedding thicknesses and heat exchanger locations. Direct contact between the cow’s hide and the cooling mat only contributes to cooling significantly under hot air (38 °C) conditions.

Development of a cost function of water distribution systems for residential subdivisions

This study aims to create a cost function for residential subdivisions based on three key variables: population density, area, and slope. The cost function was developed by minimizing the capital cost of representative residential water distribution networks through a genetic algorithm and a heuristic search method known as the greedy algorithm. To test the proposed cost function and determine if a more efficient design would reduce cost, two subdivisions in Tucson, Arizona, were compared to an equivalent theoretical oblong network. The greedy algorithm required a fraction of the time demanded by the genetic algorithm and arrived at subdivision network costs that were consistently equal to or lower than the best solutions found by the genetic algorithm. Both optimization methods obtained results indicating that area has the greatest effect on cost and that the effect of population density is negligible when dealing with small areas.

Mixing at junctions in water distribution systems: an experimental study

Abstract The accurate prediction of solute transport in water distribution systems has become a critical component of efforts aimed at delivering clean drinking water safely to large urban populations. One of the central assumptions that water quality models have traditionally relied upon dictates that, in a four-way junction, two incoming flows of differing quality will mix perfectly to produce two outgoing flows of equal quality, and that such complete mixing occurs irrespective of the specific assemblage characteristics of any particular four-way junction. In this study, laboratory experiments were conducted in order to characterize solute mixing patterns at double-tee and wye junctions, both of which are commonly found in urban water distribution systems. Results show that mixing at double-tee junctions tends to be less than complete when the tee connectors are located adjacent to each other and mix to a greater degree than flows at cross junctions.

Development of a Cost Function for Residential Subdivisions through Genetic Algorithms

The study of urbanization, and the water distribution networks that supply water to urban areas, has led to new approaches for evaluating the distribution systems that serve residential subdivisions. The present study aims to expand the evaluation method commonly applied by using Genetic Algorithms to add the actual hydraulic constraints required by Tucson Water in Arizona to an optimization model. Furthermore, an alternative calculation method utilizing a heuristic pre-optimization tool coupled with a greedy algorithm is compared to the genetic algorithm results. The improved model is capable of achieving a near optimal solution, one that is comparable to the genetic algorithm results and should minimize the cost of constructing and operating a water distribution network. Preliminary results show that population density has little effect on the total cost and that area is the driving factor in cost. In addition, the slope increases the rate at which these 2 parameters increase the cost, making high density areas much more cost effective with respect to the operation of water distribution systems. Finally, the main assumption, which considers residential subdivisions as rectangular networks, is explored by comparing the generated networks against their realistic counterparts. Results showed that the realistic networks cost more than the generated networks.

Enhancing artificial neural networks applied to the optimal design of water distribution systems

Achieving an optimal design for a typical water distribution system (WDS) essentially involves determining which combination of pipes and arrangements will produce the most efficient and economical network. Solving the problem is a complex process, one well suited to computationally intensive heuristic methods. Including water quality constraints can pose a special challenge due to the demanding, extended-period simulations involved. Employing artificial neural networks (ANNs) can reduce the amount of computation time needed. ANNs can in fact approximate disinfectant concentrations in a fraction of the time required by a conventional water quality model. This study presents a methodology for improving the accuracy of ANNs applied to the optimal design of a WDS by means of a probabilistic approach based on the fast finding of a network similar to the optimal WDS. This work also presents a methodology to find such a network. ANNs trained with the probabilistic dataset generated using the proposed approach were shown to be more accurate than their counterparts trained with a random dataset.

Computational modeling of a conductive cooling system to alleviate heat stress in dairy cows

Methods for alleviating heat stress in dairy cattle are currently limited to convective and evaporative cooling. These methods are effective in hot, dry conditions, but their performance decreases as the relative humidity of the environment increases. A conductive cooling system that circulates water through bedding materials could serve as an efficient method for relieving heat stress because it would not be directly affected by the environmental conditions that reduce the effectiveness of convective and evaporative cooling. While a conductive cooling system’s effectiveness is limited by water temperature, the conductivity of the bedding material, and the depth of the heat exchanger, the water used in such systems can be recovered and used for other applications or even recirculated through the conductive cooling system. This study uses Computational Fluid Dynamics (CFD) to evaluate the efficiency of the heat exchanger under various conditions. The study’s comprehensive heat and mass transfer model is capable of simulating changes in humidity and temperature, as well as several physiological characteristics or responses, such as sweating, heat generation, and heat transfer through skin and hair. Results show that in very hot and humid conditions conductive cooling accounts for up to 70% of the body heat given off by a cow.