No abstract

We report a 2.5-year study of the photolytic degradation of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) with variations in temperature, humidity, and illumination by fluorescent and UV light (254, 312, and 365 nm). The free-radical decomposition product was monitored with electron paramagnetic resonance (EPR). The EPR spectrum of the green powder allowed reliable quantitation with a single peak (fwhm = 29.1 G). The variations in humidity showed little effect in accelerating the degradation of TATB. The only significant temperature effect was noticed at −10 °C, where fewer radicals formed. The radical production rates at −10 °C were some of the highest measured, however, suggesting that the rates under other temperature conditions had slowed, perhaps as a result of extensive conversion of surface molecules to radical species. We show that a substantial amount of radicals can be generated with UV light, and work is ongoing to modify our EPR spectrometer so that TATB can be irradiated in the EPR cavity to measure the initial rates of radical formation.

Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method.

The temperature‐dependent solubility of hexanitrostilbene (HNS) [CAS# 20062‐22‐0] was determined in ten solvents and solvent blends using the Tyndall effect. Thermodynamic modeling of the data yielded Flory interaction parameters, the molar enthalpy of mixing, the molar entropy of mixing, and the molar Gibbs energy of mixing. All solutions exhibited endothermic enthalpies and positive entropies of mixing. The presence of water in some of the solvent blends made dissolution increasingly endothermic and disfavored solubility. The solubilities of HNS at 25 °C were used to determine the three‐component Hansen solubility parameters (HSP) (δD=18.6, δP=13.5, δH=6.1 MPa1/2) and the radius of the solubility sphere (R0=5.8 MPa1/2). The HSP determined for HNS using group‐additivity (δD=21.0, δP=13.3, and δH=8.6 MPa1/2) also correctly predicted the optimum solvents for this explosive.