The authors would like to acknowledge the support of the DFG priority program 1599 &#34;Caloric effects in ferroic materials: New concepts for cooling&#34;&#160;(Projects: EG101/23-1, SCHU2217/2-1, SE704/2-1, EG101/29-2, SCH2217/3-2, SE704/2-2).&#160;

1. Sample Preparation

<ol>
	<li>Measure the SMA ribbon with calipers and determine the&#160;cross section of the sample.</li>
	<li>Prepare the sample for IR measurements by coating the ribbon with a thin layer of high emissivity (&#949;=0.96) paint. 
	Caution: The paint is classified as an irritant. Gloves, safety glasses and mouth protection must be worn during the processing of the paint.</li>
</ol>
2. Material Stabilization (Training)
Note: Initial mechanical cycling leads to a mechanical and thermal material stabilization. The investigation of the stabilization effect, and the training procedure itself, requires the use of the actuator and the sensors mounted in the upper level of the test rig as well as the IR camera.
<ol>
	<li>Start motor controller program and check the loaded settings. Change the settings to position mode and command mode. Verify the motor is in active mode.</li>
	<li>Set the target position in the motor controller program to 0 &#181;m and click on the &#34;operation enable&#34; button &#8212; at this position the distance between the clamps is 90 mm.</li>
	<li>Place the sample between clamps of the experimental setup and use a special designed alignment tool to align the sample.</li>
	<li>Tighten clamps using a mounting aid in order to avoid bending load on load cell and sample. Use a torque wrench for tightening the screws to ensure a reproducible clamping force (tightening torque: 20 Nm).</li>
	<li>Check current motor position and make sure that the motor is at start position (0 &#181;m).</li>
	<li>Start the IR camera software and load the calibration for a 50 mm lens combined with a close-up lens. Choose an image size of 1,280 x 100 pixels and a temperature range of -20 &#176;C to 50 &#176;C. Position the camera by using the motor focus unit and confirm the whole sample is in the field of view of the camera. 
	Note: The IR camera, in combination with the chosen lens system, has a focal length (f) of 50 mm, an aperture of f/2 and a minimum pixel size of 60 &#181;m at a working distance of 200 mm.</li>
	<li>Open the control program for training and material characterization and set the control parameters (displacement, velocity, holding time, maximum and minimum force, number of cycles and camera frame rate).
	<ol>
		<li>Set the start position (0 &#181;m) and choose the target position (4,500 &#181;m) so that the material undergoes a complete phase transformation.</li>
		<li>Set the linear direct drive velocity (velocity loading/ unloading) to meet the desired strain rate. Select a strain rate of 5 x 10-4 sec-1 (actuator velocity of 45 &#181;m/sec) for cooling process related training.
		<ol>
			<li>Determine the linear direct drive velocity (v) based on the chosen strain rate (<img alt="epsilon" src="/files/ftp_upload/53626/epsilon.jpg" />) and the initial sample length (l0) of 90 mm (v = <img alt="epsilon" src="/files/ftp_upload/53626/epsilon.jpg" />&#8729;l0)</li>
		</ol>
		</li>
		<li>Set the holding time to 0 sec.</li>
		<li>Set the number of cycles to 1 for the first cycle with a new sample.</li>
		<li>Set the sample-specific minimum and maximum force level to avoid compressive load and tensile overload (minimum load 1 MPa, maximum load 800 MPa).</li>
		<li>Choose an IR camera acquisition rate of 50&#160;msec/frame (20&#160;frames per second).</li>
		<li>Click on the start button to load the settings.</li>
	</ol>
	</li>
	<li>Open the IR camera software, choose a file name and allocate 5,000&#160;frames.
	<ol>
		<li>Switch from internal to external trigger source and start the data acquisition mode.</li>
	</ol>
	</li>
	<li>Open the control program and press the start experiment button.</li>
	<li>Data visualization
	<ol>
		<li>Once the experiment is finished, load the data into the data processing software and visualize it in terms of force/displacement, stress/strain, force/time and position/time diagrams.</li>
		<li>Load the IR data into the IR camera software and evaluate the time resolved temperature profiles. Define a measurement area which covers the surface of the SMA ribbon and plot the mean maximum and minimum temperature of the sample vs. time.</li>
	</ol>
	</li>
	<li>Repeat steps 2.6 to 2.9 until the material shows stable mechanical behavior and adapt the start position to compensate for residual strains.
	<ol>
		<li>After the first 10 cycles, increase the number of cycles per experiment to 10 and proceed with the experiments until stable material behavior is reached.</li>
	</ol>
	</li>
</ol>
3. Material Characterization
Note: The material characterization requires the use of the actuator and the sensors mounted in the upper level of the test rig as well as the IR camera. During the characterization procedure the sample is loaded and unloaded at different rates while performing a holding period after loading and unloading.
<ol>
	<li>If the SMA ribbon has been unclamped and the control system of the test rig has been switched off after the training, repeat steps 2.1 to 2.6 and clamp the sample again. If this was not the case, proceed as follows.</li>
	<li>Open the control program for training and material characterization and set the control parameters (displacement, velocity, holding time, number of cycles and camera frame rate).
	<ol>
		<li>Set the start position so that the sample is under zero load and set the target position equivalent to the target position of the training (4,500 &#181;m).</li>
		<li>Set the linear direct drive velocity (velocity loading/unloading) to meet the desired strain rate. Choose a strain rate of 1 x 10-1 sec-1 (actuator velocity of 9,000 &#181;m/sec) which leads to an adiabatic phase transformation for samples with a cross section of 0.75 mm x 1.4 mm or larger.</li>
		<li>Set the holding time to 180 sec, which is sufficient for the sample to reach the initial temperature level. 
		Note: The holding time has to be verified after the experiment by calculating a thermal equilibration time constant (&#964;) and a holding time smaller than 4 x &#964; has to be increased before the next characterization experiment starts.</li>
		<li>Set the number of cycles to 1.</li>
		<li>Set the sample-specific minimum and maximum force level to avoid compressive load and tensile overload (minimum load 1 MPa, maximum load 800 MPa).</li>
		<li>Choose an IR camera acquisition rate of 5&#160;msec/frame (200 frames per second).</li>
		<li>Click on the start button to load the settings.</li>
	</ol>
	</li>
	<li>Open the IR camera software, choose a file name and allocate 80,000 frames.
	<ol>
		<li>Switch from internal to external trigger source and start the data acquisition mode.</li>
	</ol>
	</li>
	<li>Open the control program and press the start experiment button.</li>
	<li>Load the IR data into the IR camera software. Plot mean maximum and minimum sample temperatures vs. time. Export the data and calculate the thermal equilibration time constant with the data processing software 10,11.</li>
	<li>Adapt the holding time, if necessary, based on the calculated thermal equilibration time constant.</li>
	<li>Repeat steps 3.2 to 3.5 and vary the strain rate from 5 x 10-5 sec-1 to 1 x 10-1 sec-1, as well as the strain from 2% to a maximum strain of 5% (maximum strain is equivalent to the maximum strain during training).</li>
	<li>Investigation of local temperature peaks: 
	Note: The material shows a rate-dependent localization effect of the elastocaloric effect. Careful study of these effects requires a high special resolution of the SMA temperature profile. For this purpose, the lens of the IR Camera has to be replaced by a microscope lens. The microscope lens has an aperture of 3.0, a magnification of 1X and a pixel size of 15 &#181;m at a working distance of 195 mm.
	<ol>
		<li>Switch the light off, remove all heat sources from the field of view of the IR camera and change the lens.</li>
		<li>Change the camera calibration settings and load a microscope lens calibration within the temperature range of 20 &#176;C to 50 &#176;C and an image size of 500 x 250 pixels. Use the motor focus unit to focus the sample.</li>
		<li>Perform a tensile test at a strain rate of 1&#160;x 10-1&#160;sec-1 (9,000&#160;&#956;m/sec), follow the steps described in section 2: Material stabilization.</li>
	</ol>
	</li>
	<li>Data Visualization
	<ol>
		<li>Load the mechanical data into the data processing software and visualize it in terms of force/displacement, stress/strain, force/time and position/time diagrams.</li>
		<li>Load the IR data into the IR camera software and evaluate the time resolved temperature profiles. Define a measurement area which covers the surface of the SMA ribbon and plot the mean maximum and minimum temperature of the sample vs. time.</li>
	</ol>
	</li>
</ol>
4. Elastocaloric Cooling Process
Note: The investigation of elastocaloric cooling processes requires the use of actuators and sensors in the upper and lower level of the setup as well as the IR camera. These experiments include a variation of the control parameters in order to optimize the process performance.
<ol>
	<li>If the SMA ribbon has been unclamped and the test rig has been switched off after the material characterization, repeat steps 2.1 to 2.5 and clamp the sample again. If this was not the case, proceed as follows.</li>
	<li>Start the IR camera software and load the calibration for the 50 mm lens with close-up lens. Choose an image size of 1,280 x 1,024 pixels and a temperature range of -20 &#176;C to 50 &#176;C. Position the camera by using the motor focus unit and make sure that the entire sample is in the field of view of the camera. 
	Note: The IR camera in combination with the chosen lens system has a focal length (f) of 50 mm, an aperture of f/2 and a minimum pixel size of 60 &#181;m at a working distance of 200 mm.</li>
	<li>Open the control program for elastocaloric cooling processes and set the control parameters (displacement of linear direct drive one (upper level), velocity of linear direct drive one and two, contact time, maximum and minimum force, contact phase, number of cycles and camera frame rate).
	<ol>
		<li>Set the start position of the linear direct drive for SMA loading and unloading, so that the sample is under zero load and set the target position equivalent to the target position of the training (4,500 &#181;m).</li>
		<li>Set the velocity (velocity loading/unloading) of the linear direct drive for loading and unloading of the SMA to meet a strain rate of 1 x 10-1 sec-1 (9,000 &#181;m/sec). Set the velocity of the linear direct drive in the lower level of the setup to 100 mm/sec.</li>
		<li>Set the contact time to 6 sec. 
		Note: The contact time determines the duration of the heat transfer and can be set to any value above 10 msec.</li>
		<li>Choose the contact after loading/unloading mode. 
		Note: The contact phase influences whether the loading and unloading is adiabatic (contact after loading/unloading) or combined with a heat transfer to the heat sink/source (contact during loading/unloading).</li>
		<li>Set the number of cycles to 40.</li>
		<li>Set the sample-specific minimum and maximum force level to avoid compressive load and tensile overload (minimum load 1 MPa, maximum load 800 MPa).</li>
		<li>Choose an IR camera acquisition rate of 20 msec/frame (50 frames per second). Click on the start button to load the settings.</li>
	</ol>
	</li>
	<li>Open the IR camera software, choose a file name and allocate 50000 frames. Switch from internal to external trigger source and start the data acquisition mode.</li>
	<li>Open the control program and press the start experiment button.</li>
	<li>Data visualization
	<ol>
		<li>Once the experiment is finished load the data into the data processing software and visualize the following data: force/displacement, stress/strain, temperature/time (temperature of the heat sink/source), force/time, contact force/ time and position of the linear actuators/time.</li>
		<li>Load the IR data into the IR camera software and evaluate the time resolved temperature profiles. Define three measurement areas which cover the surface of the SMA sample as well as the surface of the heat sink and the heat source. Export the time resolved mean, maximum and minimum temperature data of the defined measurement areas and load them into the data processing software.</li>
		<li>Visualize the IR data in a temperature/time diagram.</li>
	</ol>
	</li>
	<li>Repeat the experiment under variation of the parameters: strain, contact time and contact phase.</li>
</ol>
5. Model Validation
Note: The validation of thermo-mechanically coupled material models requires experiments adhering to material stabilization or material characterization. Use a Ni-Ti wire with a cross section of 0.6 mm to perform the experimental investigations.
<ol>
	<li>Perform an isothermal tensile test at a strain rate of 5 x 10-5 sec-1 and a strain of 5%, proceed by performing the steps described in Section 2.</li>
	<li>Once the experiment is finished, load the data into the data processing software and visualize the stress/strain measurement. Calculate the elastic modulus of the austenite and the martensite phase, the transformation strain as well as the width of the hysteresis. The aforementioned data function as mechanical input data for the model 7.</li>
	<li>Perform further tensile tests at strain rates of 1 x 10-4 sec-1, 5 x 10-4 sec-1, 1 x 10-3 sec-1, 5 x 10-3 sec-1, 1 x 10-2 sec-1, 5 x 10-2 sec-1, 1 x 10-1 sec-1 to generate validation data for the model.</li>
	<li>If the experiments are completed take the sample out of the test system and perform a differential scanning calorimetric measurement (DSC) 18 to determine the caloric material properties (latent heats of the phase transformation and specific heat capacity of the material) of the stabilized material. 
	Note: The DSC measurements provide caloric input data for the thermo-mechanically coupled model.</li>
	<li>Start the simulation of the tensile tests described in step 5.3.
	<ol>
		<li>Implement custom model for shape memory alloys into commercially available finite element software:
		<ol>
			<li>Select Geometry node and choose Interval to draw 1D wire geometry.</li>
			<li>Select Parameters node to define model parameters identified from mechanical tests in step 5.2.</li>
			<li>Right-click Definitions node and select Variables to create a Variables node. Select Variables node and define algorithm for determining the transition probabilities derived from statistical thermodynamics 19.</li>
			<li>Select Add Physics and add Coefficient Form PDE or General Form PDE to define set of one-dimensional partial differential equations describing the behavior of superelastic Shape Memory Alloy, consisting of the stationary momentum balance, the balance of internal energy and kinetic equations of phase transformation 20.</li>
		</ol>
		</li>
		<li>Select Initial Values sub-node to set the initial temperature of the wire to the environment temperature.
		<ol>
			<li>Select Dirichlet Boundary Condition to prescribe mechanical boundary conditions for applying a strain following the experimental procedure described in section 2, for the strain-rates in step 5.3, constraining the displacement of one end of the wire and prescribing the displacement of the other end.</li>
			<li>Select Dirichlet Boundary Condition to set thermal boundary conditions to constant temperature because of the massive clamps compared to the thin wire. 
			Note: Standard settings of the finite element software do not lead to a converging solution.</li>
			<li>Select sub-nodes of Solver Configuration to modify standard settings (e.g., absolute and relative tolerances and damping coefficient of the nonlinear, iterative Newton-Raphson solver) and click &#34;Compute&#34; to run solver.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Data Analysis
	<ol>
		<li>Load the experimental and simulation results into the data analysis software and visualize the mechanical and thermal data.</li>
		<li>Compare the experimental and simulation results, respective mechanical (stress/strain response) and thermal (special resolved temperature evolution of the sample) material behavior.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The presented scientific test rig enables comprehensive investigation of elastocaloric materials and cooling processes by performing the experiments described in the protocol section. Precise alignment of the sample before clamping is crucial for all the experiments. Bad alignment can potentially lead to early material failure. Furthermore, the maximum applied strain has significant influence on the material lifetime, whereas the required strain to reach a complete phase transformation depends on the alloy composition. T...

Solid state cooling processes based on ferroic materials have potential to be environmentally friendly alternatives to the conventional vapor compression based process. Ferroic materials may exhibit magnetocaloric, electrocaloric and elastocaloric effects 1,2, as well as combinations of these effects, which are described as multicaloric material behavior 3. The different caloric effects in ferroic materials are currently being investigated as part of the German Science Foundation (DFG) Priority program SPP 1599 &#34;Caloric Effects in Ferroic Materials: New Concepts for Cooling&#34; 4. Shape Memory Alloys (SMA) which are investigated within this program show large elastocaloric effects, in particular Ni-Ti based alloys due to their large latent heats 5. The strain-induced phase transformation at high strain rates leads to significant temperature changes of the SMA, as shown in Figure 1. The adiabatic, exothermic phase transformation from austenite to martensite increases the SMA temperature. The endothermic transformation from martensite to austenite leads to a significant temperature decrease. These elastocaloric material properties can be used for solid-state cooling processes by applying a suitable mechanical loading and unloading cycle. Figure 2 shows a typical elastocaloric cooling cycle, following the Brayton cycle. The heat transfer between the heat source and the cold, unloaded SMA take place at low temperature levels. In the next phase, the SMA is in a contact-free state and the fast, adiabatic loading leads to a significant temperature increase of the SMA. The subsequent heat transfer between the hot SMA and the heat sink take place at constant strain of the SMA. Upon completion of the heat transfer, fast, adiabatic unloading leads to a significant temperature drop of the SMA below the temperature of the heat source, whereupon the next cooling cycle and the heat transfer with the heat source can start. The efficiency of the elastocaloric cooling process depends on the required mechanical work and the absorbed heat.
First, experiments monitoring the temperature field during tensile tests were performed by Shaw et al. 6,7, with the objective to investigate the formation of local temperature peaks during tensile tests of SMA strips and wires at different rates. The applied experimental method combined the measurement of the mechanical parameters (stress, strain and strain rate) with simultaneous acquisition of temperature fields by means of thermographic measurements. During loading and unloading of a SMA specimen with a tensile testing machine, an infrared (IR) camera was used to acquire IR images of the SMA sample. This technique enables the investigation of the strain rate dependent formation of temperature peaks. The measurement of the temperature distribution on the sample is very important for the investigation of the elastocaloric effects and the determination of the cooling properties of the material. A local temperature measurement &#8212; by applying a contacting temperature measurement &#8212; is not sufficient in order to characterize the cooling properties of the material. A measurement of the temperature field was also used by Cui et al. 8 for the study of elastocaloric effects in Ni-Ti wires. Furthermore, Ossmer et al. 9,10 showed that thermographic temperature measurements are also suitable for the investigation of elastocaloric effects in Ni-Ti based thin films, which required high frame rates of the IR camera for the investigation of adiabatic phase transformations at high strain rates. This technique allows for the investigation of elastocaloric quantities and the homogeneity of the temperature profile, which has a significant influence on the solid-state based heat transfer and the efficiency of elastocaloric processes.
The cooling efficiency of the material can be determined by calculating the required work based on the stress/strain measurements as well as the heat (which can be determined taking into account the temperature change and the heat capacity of the material). However, the experimental method does not enable the investigation of the elastocaloric material under process condition. This includes a heat transfer between the SMA and a heat source, which has a significant influence on the efficiency of the cooling effect.
The material characterization of cooling process conditions and the investigation of elastocaloric cooling processes require a test rig enabling solid-state based heat transfer, which cannot be investigated by any existing commercial system. To this end, a novel testing platform has been developed. The test rig is set up in two levels as shown in Figure 3. The upper level allows for basic elastocaloric material characterization and initial training procedures, similar to the previously described method (see Figure 4). The setup is equipped with a linear direct drive capable of loading and unloading the SMA at strain rates up to 1 sec-1 (see Figure 5). The linear direct drive enables the investigation of samples with a cross section of up to 1.8 mm2, while the typical sample length is 90 mm. The advantage of a linear direct drive is the high velocity and the high acceleration &#8212; in contrast to ball screw drives which are typically used for tensile tests. Furthermore, a load cell, as well as the integrated position measurement system of the linear drive, provides mechanical measurement data. A high-resolution IR camera (1,280 x 1,024 pixels) is used to measure the temperature profile of the SMA with up to 400 Hz (in the required temperature range). The use of a microscope lens with a resolution of 15 &#181;m/pixel enables the investigation of local temperature effects. The lower level of the test rig contains a mechanism that allows for alternating conductive heat transfer between the SMA and the heat source/heat sink (see Figures 6 and 7). The linear direct drive in the lower level switches between the heat source to the SMA and from the SMA to the heat sink, whereas a pneumatic cylinder lifts and lowers the heat source/sink (see Figure 8). Each actuator can be controlled independently allowing for investigation of different cooling process variations. The comprehensive measurement system enables measurements of mechanical parameters: actuator position, actuator velocity, SMA loading force, contact force between SMA and heat source/sink during heat transfer as well as thermal parameters (i.e., temperatures inside the heat source/sink, temperature distribution on the surface of the SMA and the heat source/sink). A more detailed description of the scientific testing platform is given in Schmidt et al. 11.
<img alt="Figure 5" src="/files/ftp_upload/53626/53626fig5.jpg" /> 
Figure 5. Scheme of the upper level of the test rig. A linear direct drive for loading and unloading of the SMA sample with integrated position measurement system; a load cell for measuring of tensile forces, as well as a high-resolution IR camera (1,280 x 1,024 pixels) for temperature profile acquisitions.
<img alt="Figure 7" src="/files/ftp_upload/53626/53626fig7.jpg" /> 
Figure 7. Scheme of the lower level of the test rig. A linear direct drive for switching between heat sink and heat source; a pneumatic cylinder to make contact between the SMA sample and the heat source/sink; temperature sensors have been integrated in the heat sink/source to measure the core temperature of the blocks. A compression load cell for measuring the contact force between the SMA and the heat source/sink is integrated in the heat transfer mechanism and not visible in this scheme.
The test rig allows for the investigation of different alloy compositions and sample sizes as well as geometries (ribbons, wires). Furthermore, the setup enables comprehensive investigations of elastocaloric materials and cooling processes. The previously described experiments can be performed and the execution will be described step-by-step in the protocol section of this manuscript.
Material stabilization:
Stable material behavior is important for the use of elastocaloric materials in cooling systems. To this end, a mechanical stabilization procedure is applied. During this procedure the material passes mechanical loading and unloading cycles and performs a phase transformation from austenite to martensite. The material stabilization shows a strong rate dependency. High loading rates lead to a temperature change of the material, which is caused by the latent heat of the phase transformation. This temperature change has a similar influence on the material stabilization, as do mechanical training cycles at various temperatures 12-15. In addition to the well-known mechanical 13 and caloric 16 stabilization, a thermal material stabilization can be observed with the designed setup by applying thermography 17.
Material characterization:
After an initial mechanical training procedure, the material shows stable mechanical, thermal and caloric behavior allowing the elastocaloric material properties to be characterized. Therefore, mechanical cycling at different rates is performed whereas, in contrast to the training procedure, the elastocaloric characterization includes a holding phase after loading and unloading. For the duration of the holding phase the SMA strain is kept constant until an ambient temperature level is reached again. This type of experiment is required in order to determine the lowest achievable temperature after unloading, starting from ambient temperature levels, as well as the material efficiency. Rate dependent formation of local temperature peaks can be observed, with higher rates leading to an increasingly homogeneous temperature distribution. Furthermore, by increasing the strain rate the temperature change equally increases until adiabatic conditions are achieved. The material efficiency can be determined by calculating the required mechanical work, based on a force-displacement diagram of an adiabatic experiment, as well as the absorbable heat, based on the mean temperature change of the material during unloading and the heat capacity of the sample.
Elastocaloric cooling process:
The investigation of the cooling efficiency of SMAs under process conditions requires the heat transfer between the SMA cooling medium and a heat source, as well as a heat sink. For this purpose, the SMA is in contact with a solid-state heat source (following adiabatic unloading) and a heat sink (following adiabatic loading). The efficiency of the process strongly depends on the process control and the thermal boundary conditions. The comprehensive investigation of the cooling process requires a variation of the control parameters in order to determine the most efficient process control. The individual influence of the parameters (contact time, SMA strain, SMA strain rate, contact phase (contact during the loading/unloading phase or following) and contact force) on the process performance has to be investigated. Furthermore, the influence of the changing thermal boundary condition by increasing number of cooling cycles has to be taken into account.
Model validation:
The development of a thermomechanically coupled material model, capable of reproducing the mechanical and thermal material behavior during cooling cycle, is crucial for the development of a novel cooling technology. The model allows for material and process optimization by reduced experimental and material development effort. The validation requires an initial isothermal tensile test of a stabilized material to generate the required mechanical material input data (elastic modulus of the austenite and the martensite phase, the width of the mechanical hysteresis as well as the transformation strain). The validation of the model takes place on the basis of tensile tests at different rates. The required caloric input data for the model can be determined by differential scanning calorimetry (DSC) following the mechanical experiments. The DSC measurements have to be performed after the mechanical test in order to measure the caloric material properties of a stabilized sample.

<table><tbody><tr><td>Linear direct drives</td><td>ESR-Pollmeier</td><td>ML 1418-U5-W1</td><td>SMA loading/unloading; heat transfer</td></tr><tr><td>Pneumatic cylinder&amp;nbsp;</td><td>Festo</td><td>ADNGF-40 574031</td><td>Contact between heat source/sink and SMA</td></tr><tr><td>Inductive position measurement system&amp;nbsp;</td><td>AMO</td><td>LMKA-1101.1NN-1.0-0</td><td></td></tr><tr><td>Tension and compression load cell</td><td>Futek</td><td>LCF451; FSH02241</td><td>SMA force</td></tr><tr><td>Compression load cell</td><td>Futek</td><td>LTH300; FSH00297</td><td>Contact force</td></tr><tr><td>IR camera</td><td>Infra Tec</td><td>Image IR 9360; M91129</td><td>1,280 x 1,024 pixels; Maximum frame rate 3,200 Hz</td></tr><tr><td>Real-Time Controller&amp;nbsp;</td><td>National Instruments</td><td>NI CompactRIO-9074</td><td>Data acquisiton and control system</td></tr><tr><td>Camera varnish</td><td>Tetenal</td><td>105202</td><td></td></tr></tbody></table>

experimental methods for investigation of shape memory based elastocaloric cooling processes and model validation

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor compression based cooling process. Nickel-Titanium (Ni-Ti) based alloy systems, especially, show large elastocaloric effects. Furthermore, exhibit large latent heats which is a necessary material property for the development of an efficient solid-state based cooling process. A scientific test rig has been designed to investigate these processes and the elastocaloric effects in SMAs. The realized test rig enables independent control of an SMA&#39;s mechanical loading and unloading cycles, as well as conductive heat transfer between SMA cooling elements and a heat source/sink. The test rig is equipped with a comprehensive monitoring system capable of synchronized measurements of mechanical and thermal parameters. In addition to determining the process-dependent mechanical work, the system also enables measurement of thermal caloric aspects of the elastocaloric cooling effect through use of a high-performance infrared camera. This combination is of particular interest, because it allows illustrations of localization and rate effects &#8212; both important for efficient heat transfer from the medium to be cooled.
The work presented describes an experimental method to identify elastocaloric material properties in different materials and sample geometries. Furthermore, the test rig is used to investigate different cooling process variations. The introduced analysis methods enable a differentiated consideration of material, process and related boundary condition influences on the process efficiency. The comparison of the experimental data with the simulation results (of a thermomechanically coupled finite element model) allows for better understanding of the underlying physics of the elastocaloric effect. In addition, the experimental results, as well as the findings based on the simulation results, are used to improve the material properties.

Material stabilization (Training):
Figure 9 shows a stress/strain diagram of 50 training cycles. The investigated sample is a Ni-Ti ribbon with a cross section of A = 1.45 mm2. The applied strain rate of 1 x 10-3 sec-1 leads to a mean temperature increase of &#916;T = 12.2 K. The temperature increase has a significant influence on the stabilization effect 12...

Watch this Scientific Journal Video about Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation at JoVE.com

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches.

Shape Memory Alloys (SMA) using elastocaloric cooling processes have the potential to be an environmentally friendly alternative to the conventional vapor ...

experimental-methods-for-investigation-shape-memory-based

Universidad San Sebastian

Research

JoVE Journal

Engineering

11.0K Views.  Saarland University. Experimental methods for investigation of solid state cooling processes and characterization of elastocaloric material properties of Shape Memory Alloys (SMA) are presented. A custom-built test rig has been designed for controlling and comprehensive monitoring of elastocaloric cooling processes. Furthermore, it provides a validation platform for thermomechanically coupled modeling approaches. 

Video: Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

This report aims to fully describe the experimental technique of using ellipsometry for cooling rate dependent Tg (CR-Tg) experiments. These measurements are simple high-throughput characterization experiments, which can determine the glass transition temperature (Tg), average dynamics, fragility and the expansion coefficient of the super-cooled liquid and glassy states for a variety of glassy materials. This technique allows for these parameters to be measured in a single experiment, while other methods must combine a variety of different techniques to investigate all of these properties. Measurements of dynamics close to Tg are particularly challenging. The advantage of cooling rate dependent Tg measurements over other methods which directly probe bulk and surface relaxation dynamics is that they are relatively quick and simple experiments, which do not utilize fluorophores or other complicated experimental techniques. Furthermore, this technique probes the average dynamics of technologically relevant thin films in temperature and relaxation time (&#964;&#945;) regimes relevant to the glass transition (&#964;&#945; &#62; 100 sec). The limitation to using ellipsometry for cooling rate dependent Tg experiments is that it cannot probe relaxation times relevant to measurements of viscosity (&#964;&#945; &#60;&#60; 1 sec). Other cooling rate dependent Tg measurement techniques, however, can extend the CR-Tg method to faster relaxation times. Furthermore, this technique can be used for any glassy system so long as the integrity of the film remains throughout the experiment.

Here, we present a protocol for cooling rate dependent ellipsometry experiments, which can determine the glass transition temperature (Tg), average dynamics, fragility and the expansion coefficient of the super-cooled liquid and glass for a variety of glassy materials.

This report aims to fully describe the experimental technique of using ellipsometry for cooling rate dependent Tg (CR-Tg) experiments. These measurements ...

Chemistry

Synthesis of Programmable Main-chain Liquid-crystalline Elastomers Using a Two-stage Thiol-acrylate Reaction

This study presents a novel two-stage thiol-acrylate Michael addition-photopolymerization (TAMAP) reaction to prepare main-chain liquid-crystalline elastomers (LCEs) with facile control over network structure and programming of an aligned monodomain. Tailored LCE networks were synthesized using routine mixing of commercially available starting materials and pouring monomer solutions into molds to cure. An initial polydomain LCE network is formed via a self-limiting thiol-acrylate Michael-addition reaction. Strain-to-failure and glass transition behavior were investigated as a function of crosslinking monomer, pentaerythritol tetrakis(3-mercaptopropionate) (PETMP). An example non-stoichiometric system of 15 mol% PETMP thiol groups and an excess of 15 mol% acrylate groups was used to demonstrate the robust nature of the material. The LCE formed an aligned and transparent monodomain when stretched, with a maximum failure strain over 600%. Stretched LCE samples were able to demonstrate both stress-driven thermal actuation when held under a constant bias stress or the shape-memory effect when stretched and unloaded. A permanently programmed monodomain was achieved via a second-stage photopolymerization reaction of the excess acrylate groups when the sample was in the stretched state. LCE samples were photo-cured and programmed at 100%, 200%, 300%, and 400% strain, with all samples demonstrating over 90% shape fixity when unloaded. The magnitude of total stress-free actuation increased from 35% to 115% with increased programming strain. Overall, the two-stage TAMAP methodology is presented as a powerful tool to prepare main-chain LCE systems and explore structure-property-performance relationships in these fascinating stimuli-sensitive materials.

A novel methodology is presented to synthesize and program main-chain liquid-crystalline elastomers using commercially available starting monomers. A wide range of thermomechanical properties was tailored by adjusting the amount of crosslinker, while the actuation performance was dependent on the amount of applied strain during programming.

This study presents a novel two-stage thiol-acrylate Michael addition-photopolymerization (TAMAP) reaction to prepare main-chain liquid-crystalline ...

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques.
This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions.

The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the ...

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For example, in the cat, visual information about an impending obstacle can modulate stepping for avoidance. Locomotor adaptation can also occur independent of vision, as sudden tactile inputs to the leg by an expected obstacle can modify the stepping of all four legs for avoidance. Such complex locomotor coordination involves supraspinal structures, such as the parietal cortex. This protocol describes the use of reversible, cooling-induced cortical deactivation to assess parietal cortex contributions to memory-guided obstacle locomotion in the cat. Small cooling loops, known as cryoloops, are specially shaped to deactivate discrete regions of interest to assess their contributions to an overt behavior. Such methods have been used to elucidate the role of parietal area 5 in memory-guided obstacle avoidance in the cat.

Complex locomotion in naturalistic environments requiring careful coordination of the limbs involves regions of the parietal cortex. The following protocol describes the use of reversible cooling-induced deactivation to demonstrate the role of parietal area 5 in memory-guided obstacle avoidance in the walking cat.

On complex, naturalistic terrain, sensory information about an environmental obstacle can be used to rapidly adjust locomotor movements for avoidance. For ...

Behavior

A Novel Biaxial Testing Apparatus for the Determination of Forming Limit under Hot Stamping Conditions

The hot stamping and cold die quenching process is increasingly used to form complex shaped structural components of sheet metals. Conventional experimental approaches, such as out-of-plane and in-plane tests, are not applicable to the determination of forming limits when heating and rapid cooling processes are introduced prior to forming for tests conducted under hot stamping conditions. A novel in-plane biaxial testing system was designed and used for the determination of forming limits of sheet metals at various strain paths, temperatures, and strain rates after heating and cooling processes in a resistance heating uniaxial testing machine. The core part of the biaxial testing system is a biaxial apparatus, which transfers a uniaxial force provided by the uniaxial testing machine to a biaxial force. One type of cruciform specimen was designed and verified for the formability test of aluminum alloy 6082 using the proposed biaxial testing system. The digital image correlation (DIC) system with a high-speed camera was used for taking strain measurements of a specimen during a deformation. The aim of proposing this biaxial testing system is to enable the forming limits of an alloy to be determined at various temperatures and strain rates under hot stamping conditions.

This protocol proposes a novel biaxial testing system used on a resistance heating uniaxial tensile test machine in order to determine the forming limit diagram (FLD) of sheet metals under hot stamping conditions.

The hot stamping and cold die quenching process is increasingly used to form complex shaped structural components of sheet metals. Conventional ...

Characterizing Viscoelastic Properties of 3D-Printed Polymer Metamaterials

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Viscoelastic behavior can be beneficial in enhancing the unprecedented dynamics of polymer metamaterials or, in contrast, negatively impacting their wave control mechanisms. It is, therefore, crucial to properly characterize the viscoelastic properties of a polymer metamaterial at its working frequencies to understand viscoelastic effects. However, the viscoelasticity of polymers is a complex phenomenon, and the data on storage and loss moduli at ultrasonic frequencies are extremely limited, especially for additively manufactured polymers. This work presents a protocol to experimentally characterize the viscoelastic properties of additively manufactured polymers and to use them in the numerical analysis of polymer metamaterials. Specifically, the protocol includes the description of the manufacturing process, experimental procedures to measure the thermal, viscoelastic, and mechanical properties of additively manufactured polymers, and an approach to use these properties in finite-element simulations of the metamaterial dynamics. The numerical results are validated in ultrasonic transmission tests. To exemplify the protocol, the analysis is focused on acrylonitrile butadiene styrene (ABS) and aims at characterizing the dynamic behavior of a simple metamaterial made from it by using fused deposition modeling (FDM) three-dimensional (3D) printing. The proposed protocol will be helpful for many researchers to estimate viscous losses in 3D-printed polymer elastic metamaterials that will improve the understanding of material-property relations for viscoelastic metamaterials and eventually stimulate the use of 3D-printed polymer metamaterial parts in various applications.

Additively manufactured polymers have been widely used for producing elastic metamaterials. The viscoelastic behavior of these polymers at ultrasonic frequencies remains, however, poorly studied. This study reports a protocol to estimate the viscoelastic properties of 3D-printed polymers and show how to use them to analyze the metamaterial dynamics.

Viscoelastic behavior can be beneficial in enhancing the unprecedented dynamics of polymer metamaterials or, in contrast, negatively impacting their wave ...

A Comprehensive Approach to Investigate RMU Temperature Rise Problem

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

The Ring Main Unit (RMU) is a critical device in power distribution systems used for connecting and distributing electricity. However, due to its compact internal structure and high current load, heat dissipation issues are particularly prominent. To address this problem, this study innovatively proposes a simplified RMU model, employing finite element simulation methods to accurately solve for the ohmic losses of conductors under actual operating conditions and obtain ohmic loss data for various components. This is the first in-depth investigation of the RMU's temperature rise problem using such a comprehensive approach. Subsequently, the temperature field was solved using two different temperature field analysis modules, with a detailed comparison and analysis of the simulation results to identify similarities, differences, and trends in temperature distribution. The results indicate that the temperature field solution model, which considers convective heat transfer, is more accurate and aligns with actual operating conditions. This research provides an innovative approach and practical solutions for the design and optimization of RMUs. Future research can further explore multiphysics coupling analysis methods to address structural design and mandatory validation issues for high and ultra-high voltage RMUs and other electrical equipment, thereby providing important insights for engineering design.

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

This paper addresses the temperature rise problem of the ring main unit by establishing a simplified model and conducting a comparative analysis in two temperature field-solving modules.

The Ring Main Unit (RMU) is a critical device in power distribution systems used for connecting and distributing electricity. However, due to its compact ...

Studying Ice Cream Melting Behavior Using a Computer Vision System

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of starting time and rate of melting. However, the aspect of ice cream during melting is also important because shape retention is linked to a good structure of the product. The protocol proposed here illustrates a computer vision system (CVS) that can be used to support the already existing gravitational methodology to calculate two new melting indices related to shape retention and melting rate. Pictures of ice cream during melting are taken every 15 min for a total of 90 min. Afterward, digital images are elaborated using a purposely developed image processing method to calculate ice cream area, height, and width. The ratio between height and width at each melting time, referred to&#160;the ratio at time 0 (Rt/R0), is an index of the shape retention of ice cream, while the area at the different melting times referred to the area at time 0 (At/A0) is related to the melting rate. This computer vision system allows the obtaining of highly sensitive and reliable results, and it can be applied not only to ice cream but also to different food matrices, such as whipped milk cream or egg albumen.

Author Spotlight: Innovative Ice Cream Melting Behavior Analysis Through a Computer Vision System

Here, we present a protocol based on a computer vision system (CVS) to determine the melting behavior of multi-phase food systems.

Melting behavior is one of the most important quality indices of ice cream. It is usually evaluated by gravimetric methods and expressed in terms of ...

Establishing A Mouse Model for Respiratory Tract Extreme Low-temperature Air Inhalation

Currently, constructing a mouse model for cold environmental stimulation employs cold-heat plates and wearable cooling devices. These methods can partially fulfill the requirements for studying the responses and regulatory effects of mouse skin or neural circuits to cold stimulation. Numerous clinical studies have substantiated the correlation between exposure to low-temperature environments and the development of various diseases. Recently, there has been a growing emphasis on the continuous exchange of information between organs and tissues, providing a novel perspective on addressing longstanding issues within the human body. However, existing installations are unable to construct a model for mice inhaling cold air. 
Although placing mice in a cold environment seems attractive, it has considerable limitations. While mice inhale cold air, their skin is also being stimulated by the cold environment, making it unclear whether resulting pathological changes are due to lung stimulation through the interaction of distant organs or due to the skin receptors and neural signal transmission. This creates considerable confusion in related research. This scheme presents a new approach for constructing a mouse model for extreme cold air inhalation stimulation. This device allows mice to inhale extremely low-temperature gases while their bodies remain at a normal temperature. It maximizes the simulation of the stimulating effects of extreme ambient temperatures on mice and meets the research needs for studying the relationship between extreme environmental temperatures and related diseases.

Cold environmental stimulation has been implicated in the development of various chronic diseases. Therefore, establishing animal models for preclinical research is crucial. This system addresses this need by offering a device that creates a stimulus model, meeting the requirements for basic research on pathogenic mechanisms.

Currently, constructing a mouse model for cold environmental stimulation employs cold-heat plates and wearable cooling devices. These methods can ...

Neuroscience

Evaluating the Heat Transfer of a Spin-and-Chill

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
The Spin-and-Chill uses heat transfer and fluid flow fundamentals to chill beverages from room temperature to 38 &#176;F in as little as 2 min. It would take a refrigerator approximately 240 min and an ice chest approximately 40 min to achieve an equivalent temperature change. This is accomplished Spin and Chill by spinning a can or bottle at up to 500 rpm, which creates little or no foaming.
In this experiment, the efficacy of spinning a cylinder (i.e., soda can) at high speeds to cool a soft drink will be evaluated. Operational parameters, such as rpm and spin time, will be varied to assess their effect on heat transfer, and the heat transfer coefficient will be calculated using a lumped parameter model.

Convective Spin-and-Chill Heat Transfer: Lumped Parameter Model

Source: Michael G. Benton and Kerry M. Dooley, Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA
The Spin-and-Chill uses ...