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  1. Free, publicly-accessible full text available May 1, 2023
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  4. Abstract Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in themore »next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.« less
    Free, publicly-accessible full text available August 4, 2023
  5. A computational approach based on a k-ω delayed detached eddy simulation model for predicting aerodynamic loads on a smooth circular cylinder is verified against experiments. Comparisons with experiments are performed for flow over a rigidly mounted (static) cylinder and for an elastically-mounted rigid cylinder oscillating in the transverse direction due to vortex-induced vibration (VIV). For the static cases, measurement data from the literature is used to validate the predictions for normally incident flow. New experiments are conducted as a part of this study for yawed flow, where the cylinder axis is inclined with respect to the inflow velocity at themore »desired yaw angle, β = 30◦. Good agreement is observed between the predictions and measurements for mean and rms surface pressure. Three yawed flow cases (β = 15◦, 30◦, & 45◦) are simulated and the results are found to be independent of β (independence principle) when the flow speed normal to the cylinder axis is selected as the reference velocity scale. Dynamic (VIV) simulations for an elastically-mounted rigid cylinder are performed by coupling the flow solver with a solid dynamics solver where the cylinder motion is modeled as a mass–spring–damper system. The simulations accurately predict the displacement amplitude and unsteady loading over a wide range of reduced velocity, including the region where ‘‘lock-in’’ (synchronization) occurs. VIV simulations are performed at two yaw angles, β = 0◦ and 45◦ and the independence principle is found to be valid over the range of reduced velocities tested with a slightly higher discrepancy when the vortex shedding frequency is close to the natural frequency of the system.« less
  6. Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.« less
    Free, publicly-accessible full text available December 1, 2023