Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

Using a simultaneous fitting technique to extract nonlinear absorption coefficients from data at two pulse widths, we measure two-photon and free-carrier absorption coefficients for Ge and GaSb at 2.05 and 2.5 m for the first time, to our knowledge. Results agreed well with published theory. Single-shot damage thresholds were also measured at 2.5 m and agreed well with modeled thresholds using experimentally determined parameters including nonlinear absorption coefficients and temperature dependent linear absorption. The damage threshold for a single-layer Al 2 O 3 anti-reflective coating on Ge was 55% or 35% lower than the uncoated threshold for picosecond or nanosecond pulses, respectively.

No abstract

No abstract

Biodiesel offers several environmental benefits and improvements to some fuel performance properties, but its poor oxidative stability has been a major concern. Currently, the accepted practice to improve biodiesel oxidative stability is the addition of antioxidants; numerous antioxidants have been studied but their effectiveness in inhibiting biodiesel oxidation is difficult to predict due to variation with resonance stability, solubility, reactivity, and volatility. To improve prediction efforts, this study explored the Rapid Small‐Scale Oxidation Test (RSSOT) as a means to investigate how biodiesel oxidation is affected by antioxidant concentration and temperature, and compared its results with the oxidative stability index test. A weak correlation was identified due to antioxidant variation. A kinetic model expressed in temperature and induction period was developed for biodiesel before high‐vacuum distillation (HVD), after HVD and also after HVD with three concentrations of propyl gallate (PG) and tert‐butylhydroquinone (TBHQ) antioxidants. The approach was validated by comparing collected data on the oxidation of methyl oleate with kinetic parameters found in the literature. Antioxidant concentrations from 130–930 ppm were tested, and the results revealed that the apparent activation energy of biodiesel oxidation increases with increasing concentration of primary antioxidants and decreases during vacuum distillation. When treated with an increasing concentration (130–930 ppm) of PG and TBHQ, the apparent activation energies of a vacuum distilled biodiesel changed from 108.46 ± 4.45 to 112.72 ± 1.46 kJ·mol−1 and from 77.14 ± 2.25 to 89.91 ± 2.29 kJ·mol−1, respectively. These observed trends agree with both the accepted mechanism of primary oxidation of fuels and mode of action of primary antioxidants.

In this paper, we report record nanosecond output energies of gain-switched Cr:ZnSe lasers pumped by Q-switched Cr:Tm:Ho:YAG (100 ns @ 2.096 μm) and Raman shifted Nd:YAG lasers (7 ns @ 1.906 μm). In these experiments we used Brewster cut Cr:ZnSe gain elements with a chromium concentration of 8x10 18 cm -3 . Under Cr:Tm:Ho:YAG pumping, the first Cr:ZnSe laser demonstrated 3.1 mJ of output energy, 52% slope efficiency and 110 nm linewidth centered at a wavelength of 2.47 µm. Maximum output energy of the second Cr:ZnSe laser reached 10.1 mJ under H 2 Raman shifted Nd:YAG laser pumping. The slope efficiency estimated from the input-output data was 47%.

No abstract

Remote locations such as disaster relief camps, isolated arctic communities, and military forward operating bases are disconnected from traditional power grids forcing them to rely on diesel generators with a total installed capacity of 10,000 MW worldwide. The generators require a constant resupply of fuel, resulting in increased operating costs, negative environmental impacts, and challenging fuel logistics. To enhance remote site sustainability, planners can develop stand-alone photovoltaic-battery systems to replace existing prime power generators. This paper presents the development of a novel cost-performance model capable of optimizing solar array and Li-ion battery storage size by generating tradeoffs between minimizing initial system cost and maximizing power reliability. A case study for the replacement of an 800 kW generator, the US Air Force's standard for prime power at deployed locations, was analyzed to demonstrate the model and its capabilities. A MATLAB model, simulating one year of solar data, was used to generate an optimized solution to minimize initial cost while providing over 99% reliability. Replacing a single diesel generator would result in a savings of 1.9 million liters of fuel, eliminating 100 fuel tanker truck deliveries annually. The distinctive capabilities of this model enable designers to enhance environmental, economic, and operational sustainability of remote locations by creating energy self-sufficient sites, which can operate indefinitely without the need for resupply.