An empirical approach to the re-creation of vehicle drive cycles

Vehicles such as buses, delivery trucks, mining equipment, and motorsport vehicles often repeat a highly defined pattern, route, or track during normal use. For these vehicles, standard dynamometer drive cycles are of little use. It was proposed that deriving a vehicle drive cycle from empirical data collected from on-board vehicle sensors would produce more accurate vehicle characteristic predictions for special purpose vehicles. This study answers the question Is it possible to use recorded vehicle data to replicate a real world driving scenario for the purpose of vehicle diagnostics? To reduce the complexity of the project, an electric go-kart was used as test vehicle. The go-kart was driven around the Purdue Gand Prix kart track. Data was collected from on-board sensors built into the vehicle motor controller. A turn by turn analysis of the recorded data is provided. A chassis dynamometer was redesigned to replicate the recorded drive cycle. The recorded drive cycle was replicated using the same test vehicle and the on-track data is compared to the in-lab data. During drive cycle re-creation, the system was found to have an average RPM error of 3.23% and an average current error of 7.89%. The comparison of the energy used on the track and in the lab test demonstrated that the cumulative energy used varied by only 0.49%

Dynamical model for the formation of patterned deposits at receding contact lines

We describe the formation of deposition patterns that are observed in many different experiments where a three-phase contact line of a volatile nanoparticle suspension or polymer solution recedes. A dynamical model based on a long-wave approximation predicts the deposition of irregular and regular line patterns due to self-organised pinning-depinning cycles corresponding to a stick-slip motion of the contact line. We analyze how the line pattern properties depend on the evaporation rate and solute concentration

SMALL SCALE VARIABILITY IN SNOW ACCUMULATION AND ABLATION UNDER A HETEROGENEOUS MIXED-CONIFER CANOPY

The spatial patterns of snow accumulation and melt in forested watersheds directly control runoff generation processes and the annual quantity and quality of available water to downstream receiving waters. In the western U.S. nearly three quarters of the annual water input into the hydrologic cycle comes from snow accumulation and melt in forested watersheds. This provision of water is one of the most important forest ecosystem services and is necessary for ecological, economic and social health. Despite our understanding of the coupling of forests and watersheds, the relationship between forest spatial patterns and snow hydrology is poorly understood. Forest canopies exhibit heterogeneity manifested as a mosaic of differing species, spatial arrangements, and canopy densities that differentially intercept incoming precipitation, alter wind patterns, and absorb, trap or reflect radiation; controlling the processes of snow accumulation and ablation. Vegetation patterns have been used as surrogates for processes where we expect that spatially recognizable structures give rise to specific ecological processes and vice versa. We investigated how spatial patterns of snow depth, density, snow water equivalent (SWE), and snow disappearance date (SDD) varied within stands of heterogeneous canopy structure. We collected 780 empirical measurements of snow depth, density, and SWE at peak accumulation on two fully georeferenced, mixed-conifer plots at Lubrecht Experimental Forest in western Montana. Throughout the 49 day melt season, we monitored SDD, snow depth, and SWE every third day with 4900 samples per campaign. In 2014, snow depth, density and SWE ranged from 0.0-67.31 cm, 5.43-49.76%, and 0.75-17.90 cm respectively. A canopy competition index ranged from 0.0-86.8 with non-forested areas averaging 11.5 cm SWE, melting around day 41 compared to mature dense canopy with average SWE of 5.1 cm and a SDD around day 9. This preliminary work suggests a strong linkage between canopy structure and accumulation and snowmelt processes. In the future we seek to link canopy patterns and the specific physical mechanisms that lead to differential snow dynamics in forested landscapes. This understanding is essential for improving process-based models and tools for forest managers to optimize forest water resources in a changing climate

Understanding extreme quasar optical variability with CRTS: I. Major AGN flares

There is a large degree of variety in the optical variability of quasars and it is unclear whether this is all attributable to a single (set of) physical mechanism(s). We present the results of a systematic search for major flares in AGN in the Catalina Real-time Transient Survey as part of a broader study into extreme quasar variability. Such flares are defined in a quantitative manner as being atop of the normal, stochastic variability of quasars. We have identified 51 events from over 900,000 known quasars and high probability quasar candidates, typically lasting 900 days and with a median peak amplitude of $\Delta m = 1.25$ mag. Characterizing the flare profile with a Weibull distribution, we find that nine of the sources are well described by a single-point single-lens model. This supports the proposal by Lawrence et al. (2016) that microlensing is a plausible physical mechanism for extreme variability. However, we attribute the majority of our events to explosive stellar-related activity in the accretion disk: superluminous supernovae, tidal disruption events, and mergers of stellar mass black holes.Comment: 25 pages, 18 figures, accepted for publication by MNRA

A possible close supermassive black-hole binary in a quasar with optical periodicity

Quasars have long been known to be variable sources at all wavelengths. Their optical variability is stochastic, can be due to a variety of physical mechanisms, and is well-described statistically in terms of a damped random walk model. The recent availability of large collections of astronomical time series of flux measurements (light curves) offers new data sets for a systematic exploration of quasar variability. Here we report on the detection of a strong, smooth periodic signal in the optical variability of the quasar PG 1302-102 with a mean observed period of 1,884 $\pm$ 88 days. It was identified in a search for periodic variability in a data set of light curves for 247,000 known, spectroscopically confirmed quasars with a temporal baseline of $\sim9$ years. While the interpretation of this phenomenon is still uncertain, the most plausible mechanisms involve a binary system of two supermassive black holes with a subparsec separation. Such systems are an expected consequence of galaxy mergers and can provide important constraints on models of galaxy formation and evolution.Comment: 19 pages, 6 figures. Published online by Nature on 7 January 201

Detecting Tree Mortality with Landsat-Derived Spectral Indices: Improving Ecological Accuracy by Examining Uncertainty

Satellite-derived fire severity metrics are a foundational tool used to estimate fire effects at the landscape scale. Changes in surface characteristics permit reasonably accurate delineation between burned and unburned areas, but variability in severity within burned areas is much more challenging to detect. Previous studies have relied primarily on categorical data to calibrate severity indices in terms of classification accuracy, but this approach does not readily translate into an expected amount of error in terms of actual tree mortality. We addressed this issue by examining a dataset of 40,370 geolocated trees that burned in the 2013 California Rim Fire using 36 Landsat-derived burn severity indices. The differenced Normalized Burn Ratio (dNBR) performed reliably well, but the differenced SWIR:NIR ratio most accurately predicted percent basal area mortality and the differenced normalized vegetation index (dNDVI) most accurately predicted percent mortality of stems ≥10 cm diameter at breast height. Relativized versions of dNBR did not consistently improve accuracy; the relativized burn ratio (RBR) was generally equivalent to dNBR while RdNBR had consistently lower accuracy. There was a high degree of variability in observed tree mortality, especially at intermediate spectral index values. This translated into a considerable amount of uncertainty at the landscape scale, with an expected range in estimated percent basal area mortality greater than 37% for half of the area burned (\u3e50,000 ha). In other words, a 37% range in predicted mortality rate was insufficient to capture the observed mortality rate for half of the area burned. Uncertainty was even greater for percent stem mortality, with half of the area burned exceeding a 46% range in predicted mortality rate. The high degree of uncertainty in tree mortality that we observed challenges the confidence with which Landsat-derived spectral indices have been used to measure fire effects, and this has broad implications for research and management related to post-fire landscape complexity, distribution of seed sources, or persistence of fire refugia. We suggest ways to account for uncertainty that will facilitate a more nuanced and ecologically-accurate interpretation of fire effects. This study makes three key contributions to the field of remote sensing of fire effects: 1) we conducted the most comprehensive comparison to date of all previously published severity indices using the largest contiguous set of georeferenced tree mortality field data and revealed that the accuracy of both absolute and relative spectral indices depends on the tree mortality metric of interest; 2) we conducted this study in a single, large fire that enabled us to isolate variability due to intrinsic, within-landscape factors without the additional variance due to extrinsic factors associated with different biogeographies or climatic conditions; and 3) we identified the range in tree mortality that may be indistinguishable based on spectral indices derived from Landsat satellites, and we demonstrated how this variability translates into a considerable amount of uncertainty in fire effects at the landscape scale

Fuel Dynamics After Reintroduced Fire in an Old-Growth Sierra Nevada Mixed-Conifer Forest

Background: Surface fuel loadings are some of the most important factors contributing to fire intensity and fire spread. In old-growth forests where fire has been long excluded, surface fuel loadings can be high and can include woody debris ≥100 cm in diameter. We assessed surface fuel loadings in a long-unburned old-growth mixed-conifer forest in Yosemite National Park, California, USA, and assessed fuel consumption from a management-ignited fire set to control the progression of the 2013 Rim Fire. Specifically, we characterized the distribution and heterogeneity of pre-fire fuel loadings, both along transects and contained in duff mounds around large trees. We compared surface fuel consumption to that predicted by the standard First Order Fire Effects Model (FOFEM) based on pre-fire fuel loadings and fuel moistures. We also assessed the relationship between tree basal area—calculated for two different spatial neighborhood scales—and pre-fire fuel loadings. Results: Pre-fire total surface fuel loading averaged 192 Mg ha−1 and was reduced by 79% by the fire to 41 Mg ha−1 immediately after fire. Most fuel components were reduced by 87% to 90% by the fire, with the exception of coarse woody debris (CWD), which was reduced by 60%. Litter depth in duff mounds were within 1 SD of plot means, but duff biomass for the largest trees (\u3e150 cm diameter at breast height [DBH]) exceeded plot background levels. Overstory basal area generally had significant positive relationships with pre-fire fuel loadings of litter, duff, 1-hour, and 10-hour fuels, but the strength of the relationships differed between overstory components (live, dead, all [live and dead], species), and negative relationships were observed between live Pinus lambertiana Douglas basal area and CWD. FOFEM over-predicted rotten CWD consumption and under-predicted duff consumption. Conclusions: Surface fuel loadings were characterized by heterogeneity and the presence of large pieces. This heterogeneity likely contributed to differential fire behavior at small scales and heterogeneity in the post-fire environment. The reductions in fuel loadings at our research site were in line with ecological restoration objectives; thus, ecologically restorative burning during fire suppression is possible