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1. Understanding Atypical Midlevel Wind Speed Maxima in Hurricane Eyewalls

   https://doi.org/10.1175/JAS-D-19-0191.1  

   Stern, Daniel P. ; Kepert, Jeffrey D. ; Bryan, George H. ; Doyle, James D. ( May 2020 , Journal of the Atmospheric Sciences)

   In tropical cyclones (TCs), the peak wind speed is typically found near the top of the boundary layer (approximately 0.5–1 km). Recently, it was shown that in a few observed TCs, the wind speed within the eyewall can increase with height within the midtroposphere, resulting in a secondary local maximum at 4–5 km. This study presents additional evidence of such an atypical structure, using dropsonde and Doppler radar observations from Hurricane Patricia (2015). Near peak intensity, Patricia exhibited an absolute wind speed maximum at 5–6-km height, along with a weaker boundary layer maximum. Idealized simulations and a diagnostic boundary layer model are used to investigate the dynamics that result in these atypical wind profiles, which only occur in TCs that are very intense (surface wind speed > 50 m s⁻¹) and/or very small (radius of maximum winds < 20 km). The existence of multiple maxima in wind speed is a consequence of an inertial oscillation that is driven ultimately by surface friction. The vertical oscillation in the radial velocity results in a series of unbalanced tangential wind jets, whose magnitude and structure can manifest as a midlevel wind speed maximum. The wavelength of the inertial oscillation increases with vertical mixingmore »length l_∞in a turbulence parameterization, and no midlevel wind speed maximum occurs when l_∞is large. Consistent with theory, the wavelength in the simulations scales with (2 K/ I)^1/2, where K is the (vertical) turbulent diffusivity, and I²is the inertial stability. This scaling is used to explain why only small and/or strong TCs exhibit midlevel wind speed maxima.

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2. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model

   https://doi.org/10.5194/acp-21-10413-2021  

   Im, Ulas ; Tsigaridis, Kostas ; Faluvegi, Gregory ; Langen, Peter L. ; French, Joshua P. ; Mahmood, Rashed ; Thomas, Manu A. ; von Salzen, Knut ; Thomas, Daniel C. ; Whaley, Cynthia H. ; et al ( January 2021 , Atmospheric Chemistry and Physics)

   Abstract. The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10 %–60 % following the reductionsmore »of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of -0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which -0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of −0.35 W m−2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W m−2 of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 ∘C in the Eclipse ensemble and 1.9 to 2.6 ∘C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.« less
3. Role of cosmic-ray streaming and turbulent damping in driving galactic winds

   https://doi.org/10.1093/mnras/stz2568  

   Holguin, F. ; Ruszkowski, M. ; Lazarian, A. ; Farber, R. ; Yang, H-Y K. ( September 2019 , Monthly Notices of the Royal Astronomical Society)

   Large-scale galactic winds driven by stellar feedback are one phenomenon that influences the dynamical and chemical evolution of a galaxy, redistributing material throughout the circumgalatic medium. Non-thermal feedback from galactic cosmic rays (CRs) – high-energy charged particles accelerated in supernovae and young stars – can impact the efficiency of wind driving. The streaming instability limits the speed at which they can escape. However, in the presence of turbulence, the streaming instability is subject to suppression that depends on the magnetization of turbulence given by its Alfvén Mach number. While previous simulations that relied on a simplified model of CR transport have shown that super-Alfvénic streaming of CRs enhances galactic winds, in this paper we take into account a realistic model of streaming suppression. We perform three-dimensional magnetohydrodynamic simulations of a section of a galactic disc and find that turbulent damping dependent on local magnetization of turbulent interstellar medium (ISM) leads to more spatially extended gas and CR distributions compared to the earlier streaming calculations, and that scale heights of these distributions increase for stronger turbulence. Our results indicate that the star formation rate increases with the level of turbulence in the ISM. We also find that the instantaneous windmore »mass loading is sensitive to local streaming physics with the mass loading dropping significantly as the strength of turbulence increases.

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4. Simulation Data for Design of Peptides that Fold and Self-Assemble on Graphite

   https://doi.org/10.5281/zenodo.6426152  

   Comer, Jeffrey ; Thakkar, Ravindra ; Velásquez-Silva, Astrid ; Miranda-Carvajal, Ingrid ( January 2022 )

   Abstract

   <p>This data set for the manuscript entitled &#34;Design of Peptides that Fold and Self-Assemble on Graphite&#34; includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>Version: 2.0</p> <p>Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.</p> <p><br /> Conventions Used in These Files<br /> &#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;</p> <p>Structure Files<br /> ----------------<br /> - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), as well as definitions of bonds, angles, dihedrals, and impropers for each dipeptide.)</p> <p>- graph_*.pdb or sol_*.pdb (initial coordinates before equilibration)<br /> - repart_*.psf (same as the above psf files, but the masses of non-water hydrogen atoms have been repartitioned by VMD script repartitionMass.tcl)<br /> - freeTop_*.pdb (same as the above pdb files, but the carbons of the lower graphene layer have been placed at a single z value and marked for restraints in NAMD)<br /> - amber_*.prmtop (combined topology and parameter files for Amber force field simulations)<br /> - repart_amber_*.prmtop (same as the above prmtop files, but the masses of non-water hydrogen atoms have been repartitioned by ParmEd)</p> <p>Force Field Parameters<br /> ----------------------<br /> CHARMM format parameter files:<br /> - par_all36m_prot.prm (CHARMM36m FF for proteins)<br /> - par_all36_cgenff_no_nbfix.prm (CGenFF v4.4 for graphene) The NBFIX parameters are commented out since they are only needed for aromatic halogens and we use only the CG2R61 type for graphene.<br /> - toppar_water_ions_prot_cgenff.str (CHARMM water and ions with NBFIX parameters needed for protein and CGenFF included and others commented out)</p> <p>Template NAMD Configuration Files<br /> ---------------------------------<br /> These contain the most commonly used simulation parameters. They are called by the other NAMD configuration files (which are in the namd/ subdirectory):<br /> - template_min.namd (minimization)<br /> - template_eq.namd (NPT equilibration with lower graphene fixed)<br /> - template_abf.namd (for adaptive biasing force)</p> <p>Minimization<br /> -------------<br /> - namd/min_*.0.namd</p> <p>Equilibration<br /> -------------<br /> - namd/eq_*.0.namd</p> <p>Adaptive biasing force calculations<br /> -----------------------------------<br /> - namd/eabfZRest7_graph_chp1404.0.namd<br /> - namd/eabfZRest7_graph_chp1404.1.namd (continuation of eabfZRest7_graph_chp1404.0.namd)</p> <p>Log Files<br /> ---------<br /> For each NAMD configuration file given in the last two sections, there is a log file with the same prefix, which gives the text output of NAMD. For instance, the output of namd/eabfZRest7_graph_chp1404.0.namd is eabfZRest7_graph_chp1404.0.log.</p> <p>Simulation Output<br /> -----------------<br /> The simulation output files (which match the names of the NAMD configuration files) are in the output/ directory. Files with the extensions .coor, .vel, and .xsc are coordinates in NAMD binary format, velocities in NAMD binary format, and extended system information (including cell size) in text format. Files with the extension .dcd give the trajectory of the atomic coorinates over time (and also include system cell information). Due to storage limitations, large DCD files have been omitted or replaced with new DCD files having the prefix stride50_ including only every 50 frames. The time between frames in these files is 50 * 50000 steps/frame * 4 fs/step &#61; 10 ns. The system cell trajectory is also included for the NPT runs are output/eq_*.xst.</p> <p>Scripts<br /> -------<br /> Files with the .sh extension can be found throughout. These usually provide the highest level control for submission of simulations and analysis. Look to these as a guide to what is happening. If there are scripts with step1_*.sh and step2_*.sh, they are intended to be run in order, with step1_*.sh first.</p> <p><br /> CONTENTS<br /> &#61;&#61;&#61;&#61;&#61;&#61;&#61;&#61;</p> <p>The directory contents are as follows. The directories Sim_Figure-1 and Sim_Figure-8 include README.txt files that describe the files and naming conventions used throughout this data set.</p> <p>Sim_Figure-1: Simulations of N-acetylated C-amidated amino acids (Ac-X-NHMe) at the graphite–water interface.</p> <p>Sim_Figure-2: Simulations of different peptide designs (including acyclic, disulfide cyclized, and N-to-C cyclized) at the graphite–water interface.</p> <p>Sim_Figure-3: MM-GBSA calculations of different peptide sequences for a folded conformation and 5 misfolded/unfolded conformations.</p> <p>Sim_Figure-4: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-5: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 295 K.</p> <p>Sim_Figure-5_replica: Temperature replica exchange molecular dynamics simulations for the peptide cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) with 20 replicas for temperatures from 295 to 454 K.</p> <p>Sim_Figure-6: Simulation of the peptide molecule cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) in free solution (no graphite).</p> <p>Sim_Figure-7: Free energy calculations for folding, adsorption, and pairing for the peptide CHP1404 (sequence: cyc(GTGSGTG-GPGG-GCGTGTG-SGPG)). For folding, we calculate the PMF as function of RMSD by replica-exchange umbrella sampling (in the subdirectory Folding_CHP1404_Graphene/). We make the same calculation in solution, which required 3 seperate replica-exchange umbrella sampling calculations (in the subdirectory Folding_CHP1404_Solution/). Both PMF of RMSD calculations for the scrambled peptide are in Folding_scram1404/. For adsorption, calculation of the PMF for the orientational restraints and the calculation of the PMF along z (the distance between the graphene sheet and the center of mass of the peptide) are in Adsorption_CHP1404/ and Adsorption_scram1404/. The actual calculation of the free energy is done by a shell script (&#34;doRestraintEnergyError.sh&#34;) in the 1_free_energy/ subsubdirectory. Processing of the PMFs must be done first in the 0_pmf/ subsubdirectory. Finally, files for free energy calculations of pair formation for CHP1404 are found in the Pair/ subdirectory.</p> <p>Sim_Figure-8: Simulation of four peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) where the peptides are far above the graphene–water interface in the initial configuration.</p> <p>Sim_Figure-9: Two replicates of a simulation of nine peptide molecules with the sequence cyc(GTGSGTG-GPGG-GCGTGTG-SGPG) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-9_scrambled: Two replicates of a simulation of nine peptide molecules with the control sequence cyc(GGTPTTGGGGGGSGGPSGTGGC) at the graphite–water interface at 370 K.</p> <p>Sim_Figure-10: Adaptive biasing for calculation of the free energy of the folded peptide as a function of the angle between its long axis and the zigzag directions of the underlying graphene sheet.</p> <p> </p>
   

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   This material is based upon work supported by the US National Science Foundation under grant no. DMR-1945589. A majority of the computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CHE-1726332, CNS-1006860, EPS-1006860, and EPS-0919443. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562, through allocation BIO200030.
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5. Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver

   https://doi.org/10.2514/6.2018-2018  

   Santhanagopalan, Vignesh ; Letizia, Stefano ; Zhan, Lu ; Al-Hamidi, Louay Yahia ; Iungo, Giacomo Valerio ( January 2018 , 2018 Wind Energy Symposium)

   This work focuses on the optimization of performance and profitability of a wind farm carried out by means of an economic model and Reynolds-Averaged Navier-Stokes (RANS) simulations of wind turbine wakes. Axisymmetric RANS simulations of isolated wind turbine wakes are leveraged with a quadratic super-positioning model to estimate wake interactions within wind farms. The resulting velocity field is used with an actuator disk model to predict power production from each turbine in the wind farm. Design optimization is performed by considering a site in North Texas, whose wind resource statistics are obtained from a meteorological tower. The RANS solver provides capabilities to simulate different incoming wind turbulence intensities and, hence, the wind farm optimization is performed by taking the daily cycle of the atmospheric stability into account. The objective functional of the optimization problem is the levelized cost of energy (LCoE) encompassing capital cost, operation and maintenance costs, land cost and annual power production. At the first level of the optimization problem, the wind farm gross capacity is determined by considering three potential turbine types with different rated power. Subsequently, the optimal wind farm layout is estimated by varying the uniform spacing between consecutive turbine rows. It is found thatmore »increasing turbine rated power, the wind farm profitability is enhanced. Substituting a wind farm of 24 turbines of 2.3-MW rated power with 18, 3-MW turbines could reduce the LCoE of about 1.56 $/MWh, while maintaining a similar gross capacity factor. The optimization of the spacing between turbine rows was found to be sensitive to the land cost. For a land cost of 0.05 $/m2, the layout could be designed with a spacing between 6 to 15 rotor diameters without any significant effect on the LCoE, while an increased land cost of 0.1 $/m2 leads to an optimal spacing of about 6 rotor diameters.« less