The objective of this research was to develop a non-invasive method to detect an emotional response of a horse to novelty during physical activity. Two horses performed 20 trials each, in which the horse's heart rate (HR) and physical activity were continuously measured. The relationship between the horse's physical activity and HR was described by a mathematical model allowing online decomposition of the horse's HR into a physical component and a component containing information about its emotional state. Exposure to the novel object resulted in an increase in the emotional component of HR, which allowed automatic detection of an emotional response of the horse in 33/40 trials. In the remaining seven trials no stable model could be built or data were missing. The results show that model-based decomposition of HR can be a useful tool for quantification of certain aspects of temperament.

Despite the augmented safety offered by wearing a cyclist crash helmet, many cyclists still refuse to wear one because of the thermal discomfort that comes along with wearing it. In this paper, a method is described that quantifies the ventilation characteristics of a helmet using tracer gas experiments. A Data-Based Mechanistic model was applied to provide a physically meaningful description of the dominant internal dynamics of mass transfer in the imperfectly mixed fluid under the helmet. By using a physical mass balance, the local ventilation efficiency could be described by using a single input-single output system. Using this approach, ventilation efficiency ranging from 0.06 volume refreshments per second (s(-1)) at the side of the helmet to 0.22s(-1) at the rear ventilation opening were found on the investigated helmet. The zones at the side were poorly ventilated. The influence of the angle of inclination on ventilation efficiency was dependent on the position between head and helmet. General comfort of the helmet can be improved by increasing the ventilation efficiency of fresh air at the problem zones.

The performance of climate control systems in vehicles becomes more and more important, especially against the background of the important relationship between compartment climate and driver mental condition and, thus, traffic safety. The performance of two different types of climate control systems, an un-air-conditioned heating/cooling device (VW) and an air-conditioning climate control unit (BMW), is compared using modern and practical evaluation techniques quantifying both the dynamic 3-D temperature distribution and the local air refreshment rate. Both systems suffer from considerable temperature gradients: temperature gradients in the U-AC (VW) car up to 8-9 degrees C are encountered, while the AC (BMW) delivers clear improvement resulting in temperature gradients of 5-6 degrees C. The experiments clearly demonstrate the effect of the presence of even a single passenger on the thermal regime, increasing the existing thermal discrepancies in the compartment with 15% independent of ventilation rate. Furthermore, in terms of air refreshment rates in the vehicle compartment, an air-conditioning unit halves the air refreshment time at all positions in the vehicle cabin, delivering a significant improvement in terms of human comfort. Similarly, extra air inlets in the back compartment of a car deliver progress in terms of cabin refreshment rate (93 s down to 50 s).

In literature, local mean age of air is used as an important index to evaluate indoor air quality in ventilated rooms. In this research, a data-based mechanistic approach is used to model the spatial–temporal mass distribution in an imperfectly mixed forced ventilated installation. A first-order transfer function model has proved to be sufficiently good in describing the mass transfer dynamics (Rt2=0.987) of the system. Furthermore, it was possible to fully understand the physical meaning of the model parameter. The parameter is found to be an inverse of the age of air. This Data-Based Modelling approach proved to be more robust when dealing with measurement noise. Finally, the modelled age of air was validated with a classical step up determination of the age of air for experimental data. Good correlation (Rt2=0.77) was found between both results, which proved the physical background of the model parameter.

A functional link between thermoregulation and sleep onset has long been recognised and thoroughly reported. In this work, we present results of a study in which it is evaluated whether this typical functional link still holds in situations where sleepiness is present but subjects are struggling to stay awake (e.g. driver sleepiness) and hence sleep onset (Stage 1 NREM) is not reached. Eight men and six women aged between 20 and 35 years (M = 27, SD = 5.5) participated in the experimental phase which consisted of 42 driver simulator experiments. During simulator driving, Core Body Temperature, proximal and distal skin temperatures, environmental temperature, ECG and EEG are recorded. These experiments revealed that increasing peripheral heat loss is also present in situations of increasing sleepiness without eventually reaching NREM sleep. These results enhance the strong link between thermoregulatory changes and the onset of sleep.

An on-line mathematical approach was used to model the spatio-temporal temperature distribution in an imperfectly mixed forced ventilated room. A second order model proved to be a sufficiently good description of the temperature dynamics (R² = 0.929) of the system. Furthermore, it was possible to fully understand the physical meaning of the second order model structure. Using this model, a Model Based Predictive (MBPC) climate controller was developed for a Single Input Single Output (SISO) system. The controller was able to follow the mean temperature of 4 points, and to robustly react to a random local disturbance. The results presented in this paper show that Model Based Predictive Control using Data-Based Mechanistic modeling can be of significant importance in the development of a new generation of climate controllers.

In this paper a data based mechanistic (DBM) model is proposed using a simplified heat balance formulation for modelling the temperature distribution inside a full scale ventilated room. The model has a number of parameters which are physically meaningful and determined using time temperature data obtained from experiments for several inlet air flow rates. At the inlet a step input in air temperature is applied and temperature responses at 36 sensor locations were recorded. For all ventilation rates used, the parameters of the model are extracted using statistical identification technique. Later, model based predictive control (MBPC) algorithm is developed to control temperature profiles on pre-selected sensor locations. The developed DBM model is compact in structure and found to capture the temperature distribution with high accuracy. The MBPC, which is distinguished by explicit use of process models, is robust for disturbance and noise effects. Besides it has high tracking capability of the reference trajectory.

An on-line mathematical approach was used to model the 3-D spatio-temporal temperature distribution in an imperfectly mixed forced ventilated room. A second-order model proved to be a sufficiently good description of the temperature dynamics (R-2 = 0.929) of the system for control purposes. Furthermore, it was possible to fully understand the physical meaning of the second order model structure. Using this model, a model-based predictive controller ( MBPC) was developed for a single input single output (SISO) system. The controller was able to accurately control the mean temperature level of four spatial points in the room, and to robustly react to a random local disturbance signal. The results presented in this paper show that MBPC using data-based mechanistic modelling can be of significant importance in the development of a new generation of climate controllers.

A data-based mechanistic (DBM) approach was used to model the spatio-temporal temperature distribution in the imperfectly mixed fluid in a car. The first phase of DBM involves the identification of a mathematical model from experimental data. A second order model proves to deliver a sufficiently good description of the temperature dynamics of the system (R-2 = 0.985). Furthermore, the physical interpretation of this second order model provides a useful variable. The physical meaning of one of the model parameters is what is called the local volumetric concentration of fresh air flow inside the car. It thus becomes possible to quantify the local air freshness in a complex geometric space as the interior of a car, only using simple temperature measurements. This technique could become a valuable tool in evaluating the performance of for instance climate controllers in interior spaces.

An on-line mathematical approach was used to model the spatio-temporal temperature distribution in the imperfectly mixed air inside a car. A second-order model proved to be a sufficiently good description of the temperature dynamics (R-2 = 0.985) of the system. Furthermore, it was possible to fully understand the physical meaning of the second-order model structure. Using this model, a proportional integral plus (PIP) climate controller was developed for a single input single output (SISO) system. The controller was able to follow a temperature level of 18-23-21degreesC at any desirable point, and to robustly react to a random local disturbance.