Results 181 to 190 of about 9,504 (261)
Integrated ATC enhancement and load growth forecasting via WOA-based optimal DSTATCOM placement. [PDF]
M AB, S A, D SS, R S.
europepmc +1 more source
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley +1 more source
Temporal forecasting of electric vehicle charging load using an IVY-VMD-TCN-BiLSTM model with cross-zone evaluation. [PDF]
Zhang W, Ma B, Wang D.
europepmc +1 more source
This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca +9 more
wiley +1 more source
The future of power forecasting: neuromorphic-axolotl hybrid intelligence revolutionizing grid operations through bio-inspired missing data mastery. [PDF]
Alhag SK +6 more
europepmc +1 more source
Power system short-term load forecasting [PDF]
openaire +1 more source
Composite Ti–6Al–4V–epoxy lattice structures are additively manufactured and epoxy infiltrated for cyclic loading. At low lattice volume fractions, hybridization produces synergistic gains in stiffness and energy dissipation. At higher volume fractions, synergy diminishes, although composites still exceed metallic lattices in specific energy ...
Joey Tallon +3 more
wiley +1 more source
Genetic algorithm-based daily power output forecasting for energy storage power stations. [PDF]
Xi L, Zhong J, Yu S, Liao C, You J.
europepmc +1 more source
From Shear to Sound: Mechanics–Acoustics Mapping of TPMS Lattices
Triply periodic minimal surface (TPMS) lattices are mapped across mechanical and acoustic performance, revealing that descriptors validated in compression fail under shear. First‐time comparison with trusses included. A transition from porous to resonance‐driven absorption emerges at 25% density.
Lucía Doyle +3 more
wiley +1 more source
Electricity consumption prediction using an advanced spatial-temporal deep learning framework. [PDF]
A Palan V, N S.
europepmc +1 more source

