Temperature distribution mapping

The generation of heat always accompanies the operation of a fuel cell. The heat is due to inefficiencies in the basic fuel-cell electrochemical reaction, crossover (residual diffusion through the fuel-cell solid-electrolyte membrane) of fuel, and electrical heating of interconnection resistances. Spatial temperature variation can occur if any of these heat-generating processes occur preferentially in different parts of the fuel cell stack. For example, non-uniform distribution of fuel across the surfaces of electrodes, different resistances between the interconnections in a stack, and variations among 

The most commonly utilized embedded sensor for temperature distribution mapping is the thermocouple. Wilkinson et al.130 developed a simple, in-situ, and noninvasive method of measuring the temperature distribution of a fuel cell with micro-thermocouples. In this study, thermocouples were located in the landing area of the flow field plates (in contact with the GDL of the MEA) of a fuel cell. The temperature data taken at different locations along the flow channel was then used to find each temperature slope, which in turn were related through mathematical equations to the local current density of each location. Thus, the current density distribution in the fuel cell was determined by simple temperature measurements. The results of this approach are discussed in more detail in Section 

Mench et al.131 also presented a method of measuring temperature distribution in a PEM fuel cell using micro-thermocouples. The thermocouples were placed inside the MEA between two sheets of Nation and a different approach was utilized for the analysis. In an extension of the work, Burford and Mench132 showed that temperature variation within the 50 cm2 could be greater than 10°C at current densities above 1 A/cm2. 

Two combinatorial studies also utilized thermocouple arrays to measure temperature distribution. Yan et al.133 used an electrically segmented plate with three thermocouples to investigate the effects of a dynamic load on current and temperature distribution. The results are described further in Section 3.2. Maranzana et al.134 developed a transparent cell with 20 thermocouples and 20 electrically isolated segments of gold wire to monitor performance 

Fiber optic sensors are an alternative to thermocouples as embedded temperature distribution mapping sensors. As described in Section 2.2.7, McIntyre et al.104 developed two distinct fiber optic temperature probe technologies for fuel cell applications (free space probes and optical fiber probes). Both sensor technologies showed similar trends in fuel cell temperature and were also used to study transient conditions. 

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Temperature distribution mapping has not yet seen significant implementation toward improved cell designs. However, as previously mentioned in Section 2.3, Wen and Huang122 used an array of 11 thermocouples in combination with a visualization cell to evaluate the effectiveness of a PGS sheet to improve performance and to reduce and homogenize temperature distribution in a PEM fuel cell. Also, in the study by Yan et al.133 they investigated dynamic load conditions by studying the effects of air stoichiometry, rate... 

Figure 6.14 CFD predictions for three-dimensional temperature distribution mapping the combustion and flame shape.

As I see it, ground resolution is generally sufficient. Maps on the scale of 1 100000 can be obtained, allowing us to tackle our problems with unprecedented ease. However, the most important innovation is not that of resolution, but involves the placement of the two twin channels on either side of the dip (Reststrahlen) which has been discussed previously. In practice, the two detectors supply two radiance temperatures at the same instant for the same object. The thermal difference between these two channels, resulting from a difference in emissivity, enables alkaline rocks to be distinguished from acid rocks. If no difference is recorded (plant cover, snow, oceans) we get, as in the past, a radiance temperature distribution map. 

We are here touching on thQ fourth basic principle bare rocks are distinguished from one another by texture, physical, and chemical properties, color, etc. In combination with the second principle, this explains why tectonic areas stand out on radiance temperature distribution maps. In theory, we should be able to trace the lines separating geological outcroppings, but the facts are otherwise. 

Here, we are arriving at the real key to the interpretation of nocturnal radiance temperature distribution maps when he finds heat anomalies, the interpreter seeks by a process of elimination to determine the cause. 

An entirely different type of transport is formed by thermal convection and conduction. Flow induced by thermal convection can be examined by the phaseencoding techniques described above [8, 44, 45] or by time-of-flight methods [28, 45]. The latter provide less quantitative but more illustrative representations of thermal convection rolls. The origin of any heat transport, namely temperature gradients and spatial temperature distributions, can also be mapped with the aid of NMR techniques. Of course, there is no direct encoding method such as those for flow parameters. However, there are a number of other parameters, for example, relaxation times, which strongly depend on the temperature so that these parameters can be calibrated correspondingly. Examples are described in Refs. [8, 46, 47], for instance. 

The spatial temperature distribution established under steady-state conditions is the result both of thermal conduction in the fluid and in the matrix material and of convective flow. Figure 2. 9.10, top row, shows temperature maps representing this combined effect in a random-site percolation cluster. The convection rolls distorted by the flow obstacles in the model object are represented by the velocity maps in Figure 2.9.10. All experimental data (left column) were recorded with the NMR methods described above, and compare well with the simulated data obtained with the aid of the FLUENT 5.5.1 [40] software package (right-hand column). Details both of the experimental set-up and the numerical simulations can be found in Ref. [8], The spatial resolution is limited by the same restrictions associated with spin... 

Fig. 27a shows the temperature field in the fluid adjacent to the tube wall, by means of a temperature contour map. The axes of the map are the axial coordinate Z and the arc length along the curved tube wall, S. The contour map shows one hot region and several colder regions in an overall temperature distribution that was quite moderate. The hotter region in the center of the map is associated with the strong axial flow component found there. The cold region to the left of the center of the map ( -coordinate 0.035-0.045, Z-coordinate 0.02-0.04) corresponds to the position of the curved section of the center particle in Fig. 25. In this area the flow is of average velocity, but has a uniform direction and a reasonable radial component, creating the cooler spot.

The initial testing is performed on an empty chamber to measure temperature distribution. The thermodynamic characteristics of the empty sterilizer are depicted in a temperature distribution profile, which will serve to locate hot or cold areas in the sterilizer by mapping the temperatures at various points in the chamber. 

Species, Temperature, and Current Distribution Mapping in Polymer Electrolyte Membrane Fuel Cells... 

Figuie6.15 shows a comparison of the temperature distributions over the first, the second and the tenth tube in the cases with and without radiation. The main effect that is possible to observe in the maps in Figure 6.15 is the stronger similarity among... 

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