High Temperature VARTM of Phenylethynyl Terminated Imides
Depending on the part type and quantity, fabrication of composite structures using vacuum-assisted resin transfer molding (VARTM) can be more affordable than conventional autoclave techniques. Recent efforts have focused on adapting VARTM for the fabrication of high temperature composites. Due to their low melt viscosity and long melt stability, certain phenylethynyl terminated imides (PETI) can be processed into composites using high temperature VARTM (HT-VARTM). However, one of the disadvantages of the current HT-VARTM resin systems has been the high porosity of the resultant composites. For aerospace applications, the desired void fraction of less than 2% has not yet been achieved. In the current study, two PETI resins, LaRC PETI-330 and LaRC PETI-8 have been used to make test specimens using HT-VARTM. The resins were infused into ten layers of IM7-6K carbon fiber 5-harness satin fabric at 260 or 280 °C and cured at temperature up to 371 °C. Initial runs yielded composites with high void content, typically greater than 7% by weight. A thermogravimetric-mass spectroscopic study was conducted to determine the source of volatiles leading to high porosity. It was determined that under the thermal cycle used for laminate fabrication, the phenylethynyl endcap was undergoing degradation leading to volatile evolution. This finding was unexpected as high quality composite laminates have been fabricated under higher pressures using these resin systems. The amount of weight loss experienced during the thermal cycle was only about 1% by weight, but this led to a significant amount of volatiles in a closed system. By modifying the thermal cycle used in laminate fabrication, the void content was significantly reduced (typically ∼ 3% or less). The results of this work are presented herein.
Transfer molding imide resins based on 2,3,3', 4'-biphenyltetracarboxylic dianhydride
Phenylethynyl containing imide oligomers have been under investigation as part of an effort to develop resins for non-autoclave composite fabrication processes such as resin transfer molding (RTM). These high performance/high temperature composites are potentially useful on advanced aerospace vehicles such as reusable launch vehicles (RLVs). New phenylethynyl terminated imide oligomers (PETI) based upon 2,3,3',4'-biphenyltetracarboxylic dianhydride (a-BPDA) were prepared and characterized primarily by rheological behavior and cured glass transition temperature (Tg). In comparison to resins from the symmetrical isomer (3,3',4,4'-biphenyltetracarboxylic dianhydride, s-BPDA), a-BPDA afforded corresponding resins with lower melt viscosities and upon curing, higher Tgs. Several resins exhibited an attractive combination of properties such as low and stable melt viscosities required for RTM composite fabrication, high cured Tgs, and moderate toughness. One resin (P10) was used to fabricate flat, void free laminates by RTM. The laminates exhibited high mechanical properties at temperatures to 288°C. The chemistry and physical properties of these new PETIs and the laminate properties of one composition are discussed.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.