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Metallic foams are a new class of ultra-lightweight materials with potential applications in such industries as automobile, aerospace, and energy industries. These materials when realized in product form can serve as efficient heat exchanges, energy absorbers, and thermal protective and hydrogen storage devices. Accurate determination of thermal conductivity and understanding of heat transfer characteristics is important in designing such products incorporating metal foams. The present research characterizes the effective thermal conductivity and heat transfer characteristics of DUOCEL AL 6106-T6 and Stainless Steel 314 open cell foams by experiments at near room temperature conditions. The effective thermal conductivity of these materials has been determined experimentally. Thermal conductivity of metal foams increased with increasing mechanical stress. The effect of porosity on the thermal conductivity of ERG supplied aluminum and NASA-GRC supplied SS 314 are also studied and compared with the published data in literature, however, in our studies systematic dependency of porosity is not observed. Experiments also conducted to quantify forced convective heat transfer characteristics under laminar flow conditions. Heat transfer coefficient increases with increased Reynolds number but results are not conclusive in case of natural convection.

Failure-free operation of rolling element bearings is essential for the safe and reliable operation of rotating industrial equipment, especially of the type high-energy aircraft engines. However, improper installation and maintenance, large variations in applied loads, and cycle time initiate functional defects to reduce the service life of bearing elements severely. In-service condition monitoring of bearings by permanently mounted sensors offers excellent potential to reduce the risk of failures and avoid premature failure of bearings and the rotor system. This paper presents the results from a study on experimental investigation of bearing defect identification by sensor-based condition monitoring methods. Vibration spectra from a direct mount accelerometer are analyzed to interpret and classify bearing defects. Sensor locations on the bearing are selected for enhanced sensitivity and minimum loss in the signal transmission path. Signal processing methods such as Fast Fourier Transform and statistical Kurtosis and skewness parameters are applied to characterize the dynamic spectra due to bearing defects in outer race, inner race, and cage elements of a test bearing. Unlike the previous research, this study analyzes the dynamic spectra of a test bearing to identify the spectral differences due to varying loads. The results are presented in simple and easy to understand format for the maintenance and engineering staff to affect repairs to bearing assemblies.