Results 21 to 30 of about 760 (262)

Numerical Simulations of the 1 May 2012 Deep Convection Event over Cuba: Sensitivity to Cumulus and Microphysical Schemes in a High-Resolution Model

Advances in Meteorology, 2015
This paper evaluates the sensitivity to cumulus and microphysics schemes, as represented in numerical simulations of the Weather Research and Forecasting model, in characterizing a deep convection event over the Cuban island on 1 May 2012.
Yandy G. Mayor, Michel D. S. Mesquita
doaj   +1 more source

A Shallow‐Deep Unified Stochastic Mass Flux Cumulus Parameterization in the Single Column Community Climate Model

Journal of Advances in Modeling Earth Systems, 2023
Cumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying ...
B. Khouider, B. B. Goswami, R. Phani, A. J. Majda   +3 more
doaj   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Impact of Cumulus Options from Weather Research and Forecasting with Chemistry in Atmospheric Modeling in the Andean Region of Southern Ecuador

Atmosphere
Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognosis of cloud formation, rainfall, energy levels reaching the surface, and air quality.
Rene Parra
doaj   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Cumulus Ensembles in Shear: Implications for Parameterization

Journal of the Atmospheric Sciences, 2001
Abstract A systematic numerical investigation is conducted into the role of ambient shear on the macrophysical properties of tropical cumulus ensembles maintained by convective available potential energy generated by constant surface fluxes of temperature and moisture and large-scale advective cooling and moistening.
Changhai Liu, Mitchell W. Moncrieff
openaire   +1 more source

A cumulus parameterization with multiple cloud base levels [PDF]

Journal of Geophysical Research: Atmospheres, 1998
We have generalized the Arakawa‐Schubert cumulus parameterization to allow multiple cloud‐base levels. A spectrum of cloud‐top levels is allowed for each cloud‐base level. We use a linear (rather than exponential) mass flux profile, because this greatly simplifies the determination of the entrainment rate and reduces the computational requirements of ...
Ping Ding, David A. Randall
openaire   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

Advanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma, Sarath Menon, Fritz Körmann, Thomas Hammerschmidt, Ralf Drautz   +4 more
wiley   +1 more source

Parameterizing convective organization

Journal of Advances in Modeling Earth Systems, 2011
Lateral mixing parameters in buoyancy-driven deep convection schemes are among the most sensitive and important unknowns in atmosphere models. Unfortunately, there is not a true optimum value for plume mixing rate, but rather a dilemma or tradeoff ...
Brian Earle Mapes, Richard Brian Neale
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source