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A test protocol previously developed for automotive applications was adapted to evaluate the performance of a climate control system for a heavy-duty truck. Human subjects, as well as a test system composed of a high-resolution passive sensor manikin and a human thermal model, were employed to evaluate thermal comfort perception. Testing was performed in a climate-controlled wind tunnel equipped with a dynamometer. The truck’s HVAC system performance was evaluated in a −10 °C environment. Additionally, the test protocol was designed to explore a large range of thermal sensation and comfort states. Subjective responses, including thermal sensation and comfort, as well as thermo-physiological state information, quantified by skin temperatures measured across the body, were obtained from the human test participants and compared to that which was indicated by the test system.

Annual fuel use for sleeper cab truck rest period idling is estimated at 667 million gallons in the United States, or 6.8% of long-haul truck fuel use. Truck idling during a rest period represents zero freight efficiency and is largely done to supply accessory power for climate conditioning of the cab. The National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck thermal management systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In addition, if the fuel savings provide a one- to three-year payback period, fleet owners will be economically motivated to incorporate them.

Annual fuel use for long-haul truck rest period idling is estimated at 667 million gallons in the United States. The U.S. Department of Energy's National Renewable Energy Laboratory's CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck climate control systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In order for candidate idle reduction technologies to be implemented at the original equipment manufacturer and fleet level, their effectiveness must be quantified. To address this need, a number of promising candidate technologies were evaluated through experimentation and modeling to determine their effectiveness in reducing rest period HVAC loads.

Cab climate conditioning is one of the primary reasons for operating the main engine in a long-haul truck during driver rest periods. In the United States, sleeper cab trucks use approximately 667 million gallons of fuel annually for rest period idling. The U.S. Department of Energy's National Renewable Energy Laboratory's (NREL) CoolCab Project works closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining occupant comfort. Heat transfer to the vehicle interior from opaque exterior surfaces is one of the major heat pathways that contribute to air conditioning loads during long-haul truck daytime rest period idling. To quantify the impact of paint color and the opportunity for advanced paints, NREL collaborated with Volvo Group North America, PPG Industries, and Dometic Environmental Corporation.

In the United States, an intercity long-haul truck averages approximately 1,800 hrs per year for idling, primarily for sleeper cab hotel loads, consuming 838 million gallons of diesel fuel across the entire long-haul fleet [1]. Including workday idling, over 2 billion gallons of fuel are used annually for truck idling [2]. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is working on solutions to reduce idling fuel use through the CoolCab project. The objective of the CoolCab project is to work closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining the cab occupant comfort. NREL conducted an experimental test program at their Vehicle Testing and Integration Facility in collaboration with Volvo Trucks, Aearo Technologies LLC / E-A-R Thermal Acoustic Systems - a 3M company, 3M Corporation, and Dometic Environmental Corporation.

The most common fan model to use in commercial CFD software today is the Multiple Reference Frame (MRF) model. This is at least valid for automotive under hood applications. Within the industry, for this typical application, this model is commonly known to under predict performance. This under prediction has been documented by the authors' of this paper in SAE paper 2009-01-0178 and VTMS paper 2009-01-3067. Furthermore has this been documented by S.Moreau from Valeo in “Numerical and Experimental Investigation of Rotor-Stator Interaction in Automotive Engine Cooling Fan Systems”, ETC, 7th European Conference on Turbomachinery, 2007. In preceding papers a specific methodology of use has been documented and it has been shown that the MRF model under predicts performance for the airflow in a cooling system commonly with 14% in volumetric flow rate. This is for a system dominated by inertial effects.

A common fan model to use in automotive under hood simulations is the Multiple Reference Frame (MRF) model and within the industry, for this specific application, this model is well known to under predict performance. In this paper we have examined the possibilities of correcting this deficiency with a simple “speed correction”. This is done by testing and simulating a production fan in the Volvo Fan Test Rig for two operating speeds, 1200 rpm and 2400 rpm. Pressure rise, fan power and static efficiency are presented as functions of volumetric flow rate. The simulations verify that using the MRF model the common behavior of under predicting pressure rise and performance of the fan occur. In addition, this work shows that; although the MRF is not predicting fan performance correctly it constitutes a reliable fan modeling strategy.