Results 141 to 150 of about 44,636 (300)
Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review
IEEE AccessThe optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction.
Ivan P. Malashin +5 more
doaj +1 more source
Performance Analysis of Abradable Coating Systems for Aircraft Gas Turbines
Advanced Engineering Materials, EarlyView.Three CoNiCrAlY/YSZ/MgAl2O4 abradable liner configurations on a nickel‐superalloy are evaluated by thermal‐gradient cycling and incursion tests. Laser ablation of the bondcoat and/or Y2O3‐stabilized ZrO2 (YSZ) intermediate layer increases mechanical interlocking and bonding for thick topcoats.
Hanna Heyl +4 more
wiley +1 more source
Scientific ReportsThis study develops a novel framework integrating Bayesian inference with deep reinforcement learning for uncertainty quantification and adaptive support optimization in multi-physics coupled deep foundation pit systems.
Weiming Gu
doaj +1 more source
EnergiesLithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life
Luca Martiri, Loredana Cristaldi
doaj +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes.
Kontolati, Katiana +4 more
core +1 more source
Development of Uncertainty Quantification Capability for NESTLE
, 2017 This work aims to develop an uncertainty analysis methodology for the propagation and quantification of the effects of nuclear cross-section uncertainties on important core-wide attributes, such as power distribution and core critical eigenvalue.
Dongli Huang, Hany S. Abdel-Khalik
core +1 more source
Phase Field Failure Modeling: Brittle‐Ductile Dual‐Phase Microstructures under Compressive Loading
Advanced Engineering Materials, EarlyView.The approach by Amor and the approach by Miehe and Zhang for asymmetric damage behavior in the phase field method for fracture are compared regarding their fitness for microcrack‐based failure modeling. The comparison is performed for the case of a dual‐phase microstructure with a brittle and a ductile constituent.
Jakob Huber, Jan Torgersen, Ewald Werner
wiley +1 more source
Firms' investment decisions in response to demand and price uncertainty [PDF]
We estimate the effect of demand and price uncertainty on firms' investment decisions from a panel of manufacturing firms. Uncertainty measures are derived from firms' subjective qualitative expectations. They are close to their theoretical counterparts,
Catherine Fuss, Philip Vermeulen
core
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source