Aerospace Testing Alliance (United States)

The transition to sustainable energy is paramount for addressing climate change, and low-carbon technologies play a pivotal role in this shift in the USA. The prime objective of this research paper was to apply the capabilities of machine learning in an examination of America's low-carbon technology trading. With powerful analysis tools, we attempted to detect trends in exporting and importing, estimate the contribution of such technology to the economy, and estimate the effectiveness of supporting policies. The scope of our activity was U.S. low-carbon technology trade, both its imports and its exports. Examining a rich dataset including volumes of trade, technological categories, and economic factors, we try to unveil deeper trends driving this new sector. The dataset for analysis in such a case involved in-depth information drawn from a range of reliable sources, including U.S. trade reports, economic statistics, and global databases for sustainability. Trade volumes, in terms of value and quantity of low-carbon technology exported and imported, form one of the key variables in such a dataset. There was extensive information about carbon emissions, providing an analysis of the impact on terms of the environment through such technology, and policy incentives, in terms of government actions for encouragement of low-carbon alternatives. In selecting machine learning models for examining low-carbon technology trade, three candidates—Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbor (KNN)—stood out for their particular strengths. In terms of accuracy, the SVM model is the top scorer, closely followed by KNN, while Logistic Regression takes a considerable drop, indicating its relatively lower predictive capability. Precision measurements also rank similarly, with SVM and KNN recording high precision values, suggesting that they are reliable in predicting true positives. Recall scores also indicate the strength of SVM and KNN in recalling all instances, while the Logistic Regression model records lower recall, particularly in predicting the class. Finally, the F1 score, being the trade-off between precision and recall, further reinforces the superior performance of SVM and KNN, as both models record high scores, with Logistic Regression lagging. To enhance the U.S. position in the global low-carbon technology market, a multi-faceted approach must be taken. Firstly, efforts must be made to drive innovation through increased investment in research and development (R&D). For business firms and investors, the transition to a low-carbon economy presents a plethora of market opportunities in the low-carbon technology sector. U.S. firms can leverage growing consumer demand for green products by developing product lines to cater to renewable energy systems, energy-efficient appliances, and electric vehicles. For investors, an understanding of the dynamics of the low-carbon technology market is essential for risk management through predictive economic modeling. It is necessary to synchronize trade policy with U.S. and global carbon reduction objectives to foster a sustainable economy.

Viewed from a distance, the issue of the United Kingdom with Europe is old. Its relationship with the continent depends on the distrust and the preservation of its security, identity and its interests, whatever the origin of the threat, of a State or an association of States that compromises the balance of powers. It is this major purpose that has determined the strategy that traditionally it adopted in relation to the Continent and that motivated its late accession to the EEC / EU, in which it maintained a recalcitrant positioning.

ABSTRACTOrganosolv switchgrass lignin (SGL) and hardwood lignin (HWL) polymers are used as precursors to prepare low cost carbon fibers (CFs). Lignin powder and fiber samples are stabilized and carbonized at different conditions to investigate the effect of HCl on thermal‐oxidative stabilization time, mass yield, fiber diameter, and mechanical properties. The results show that HCl can accelerate stabilization and reduce the stabilization time from many hours to 75 min for the SGL fibers, and to 35 min for the HWL fiber. The rate of rapid stabilization in HCl/air is at least four times faster than conventional stabilization in air. The CFs prepared with two different stabilization methods have almost the same strength and modulus, but higher carbon yield is obtained with the rapid stabilization due to a short time of oxidation. Pores and defects observed on the surface and the cross‐section of the CFs across all stabilization conditions contribute to low fiber strength. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45507.

We review recent applications of the Two Body Dirac equations of constraint dynamics to meson spectroscopy and describe new extensions to three-body problems in their use in the study of baryon spectroscopy. We outline unique aspects of these equations for QED bound states that distinguish them among the various other approaches to the relativistic two body problem. Finally we discuss recent theorectial solutions of new peculiar bound states for positronium arising from the Two Body Dirac equations of constraint dynamics, assuming point particles for the electron and the positron.

Design, fabrication, and testing of a new Mach 3.0 high heat flux nozzle was performed at the Arnold Engineering Development Complex’s (AEDC) High-Temperature Lab (HTL) for the H3 arc jet facility at Arnold Air Force Base, TN. Per customer request, the HTL investigated the possibility of higher heat flux nozzles with larger flow field areas for the H3 facility. After design and fabrication, testing was performed to characterize the flow field of the 7.1-cm-diam contoured nozzle.

A statistical engineering approach featuring a formally designed experiment has recently been implemented for calibration of an internal, strain-gauged, wind-tunnel balance at Arnold Engineering Development Complex. It was necessary to consider a substantially wide variation in balance temperature for this particular application. The cost to perform a balance calibration with the balance equilibrium temperature as a completely randomized factor was therefore prohibitive. A design-of-experiments approach using a run schedule with restricted randomization, known commonly as a split-plot design, is being evaluated. For more than a decade, design of experiments has been implemented in wind-tunnel strain-gauge balance calibration processes. Calibration with design of experiments is a characterization process where data are collected and analyzed using statistical methods, allowing conclusions to be drawn with chosen levels of confidence and power. A sequential approach to new balance calibration using existing hardware was demonstrated using a crossed design. A modified, two-active-factor, Box–Behnken design was executed at three temperature levels, and a second-order regression model was constructed in all factors. When compared to the traditional approach, a minimum 50% reduction in calibration duration and a reduction in overall residual error were demonstrated.

A numerical model of a rapid response phase change heat exchange module has been developed and challenged with experimental data taken on a flow bench with multiple temperatures and flow rates for two different phase change thermal storage devices (PTSDs). The model requires an a priori knowledge of an effective overall heat transfer coefficient. A single test was used to establish a value for an effective overall heat transfer coefficient. With this information the model will predict the power removed from a fluid being cooled to closer than 15% of the peak power and the temperature of the fluid exiting the device to within 2 °C over the entire fluid discharge temperature range. This model, developed for potential use in feedback control algorithms, requires a real-time execution speed, and this goal has been achieved with a desktop quad-core computer (four times faster than real time). While 3D models with millions of cells can provide greater resolution, the large computational resources and run times required for these simulations precludes their use as a part of feedback control algorithms.