Abstract
Chun Luo, Xinmei Wang, Weizhu Yang, Shouyi Sun
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
A compact LWIR borescope sensor for 2D engine component surface temperature measurement
10.1088/1361-6501/acc048 · 2023
Combustion performance analysis and engineering practice of a F-class gas turbine with hydrogen addition
2022
Analysis on peak load regulation of E-stage combined cycle heating unit
2018
Unresolved referenced work
Kept as external metadata until matched
A Bayesian generalized Eyring‐Weibull accelerated life testing model
10.1002/qre.3458 · 2023
Application of manson-haferd and larson-miller methods in creep rupture property evaluation of heat-resistant steels
10.1115/1.4001916 · 2010
Simple data analytics approach coupled with larson–miller parameter analysis for improved prediction of creep rupture life
10.1007/s12540-023-01445-3 · 2023
Physical significance and reliability of Larson–Miller and Manson–Haferd parameters
10.1179/mst.1994.10.4.340 · 2013
Modified Norris–Landzberg model and optimum design of temperature cycling alt
10.1007/s11223-016-9748-1 · 2016
A bibliography of accelerated test plans
10.1109/tr.2005.847247 · 2005
A load sequence design method for hydraulic piston pump based on time-related Markov Matrix
10.1109/tr.2018.2830330 · 2018
Accelerated degradation tests: modeling and analysis
10.1080/00401706.1998.10485191 · 1998
Accelerated reliability demonstration under competing failure modes
10.1016/j.ress.2014.11.014 · 2015
Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes
10.1016/j.ress.2011.03.014 · 2011
Comparisons of exponential life test plans with intermittent inspections
10.1080/00224065.2000.11979998 · 2018
Multi-stress accelerated life test prediction method based on general logarithmic linear model
2020
Mechanistic implications of plastic degradation
10.1016/j.polymdegradstab.2007.11.008 · 2008
Polymer life-time prediction: The role of temperature in UV accelerated ageing of polypropylene and its copolymers
10.1016/j.polymertesting.2014.03.019 · 2014
Weatherability of polypropylene by accelerated weathering tests and outdoor exposure tests in japan
10.1155/2016/6539567 · 2016
Accelerated weathering device for service life prediction for organic coatings
10.1016/j.polymertesting.2004.08.001 · 2005
Experimental and Numerical Study of Strength Prediction of Cold forged Parts Based on the Chaboche Combined Hardening Model
2021
10.1063/1.4822235
10.1063/1.4822235
Unresolved referenced work
Kept as external metadata until matched
Creep effects on design below the temperature limits of ASME section III Subsection NB
10.1115/1.3126263 · 2010
Compilation method of a load-enhanced accelerated mission test spectrum for turbine blades based on creep-fatigue damage equivalent
10.1016/j.engfracmech.2026.111903 · 2026
PINN-based reconstruction of deformation fields in hybrid additive-subtractive manufacturing of metal cavity parts
10.1016/j.jmsy.2026.05.001 · 2026
Fractional MHD second-grade fluid flow in porous ducts: Fast spectral solver and PINN-based parameter estimation
10.1016/j.icheatmasstransfer.2026.111449 · 2026
On the convergence of Markov chain distribution within quantum walk circuit subspace
10.1007/s11128-026-05158-5 · 2026
Comparative study of meta-learning and transfer learning for the prediction of supercritical airfoils under small-scale dataset
10.3390/aerospace13040333 · 2026
Informer-based cross-site transfer learning for water demand forecasting via domain adaptation and meta-learning
2026
Opening the black box: How reasoning-enabled AI agents influence user perceptions and behavior in sustainable consumption
2026
Deep-learning-enabled development of a high-dynamic-performance CFRP damping boring bar for chatter suppression
10.1016/j.precisioneng.2026.04.022 · 2026
A multi-mechanism damage coupling model
10.1016/j.ijfatigue.2004.02.004 · 2004
Deposition model of corrosion particles with multi-mechanism coupling in flowing LBE
10.1016/j.anucene.2025.111439 · 2025
Modeling dry deposition of aerosol particles onto rough surfaces
10.1080/02786826.2011.605814 · 2012
A multi-physics computational tool based on CFD and GEM chemical equilibrium solver for modeling coolant chemistry in nuclear reactors
10.1016/j.pnucene.2019.103190 · 2020
Fluctuation mechanism for biquadratic exchange coupling in magnetic multilayers
10.1103/physrevlett.67.3172 · 1991
The flow stress prediction of TiB2/2024 aluminum matrix composites based on modified Arrhenius model and gene expression programming model
2024
aluminum alloy
2024
Cyclic deformation response of UFG 2024 Al alloy
10.1016/j.ijfatigue.2010.11.025 · 2011
A deep learning framework for supersonic turbulent combustion
10.1016/j.actaastro.2024.09.027 · doi-reference
Neural network-augmented eddy viscosity closures for turbulent premixed jet flames
10.1016/j.combustflame.2025.114241 · doi-reference
Uncertainty quantification of departure delay considering network properties and conformal prediction framework
10.1016/j.jairtraman.2026.103037 · doi-reference
Uncertainty quantification in resolvent analysis of experimental wall-bounded turbulent flows
10.1007/s00348-026-04213-2 · doi-reference
RANS structural uncertainty quantification of transonic centrifugal compressors aerodynamics using eigenvalue perturbation
10.1016/j.ast.2026.112629 · doi-reference
Experimental evaluation of a small-capacity, waste-heat driven ammonia-water absorption chiller
10.1016/j.ijrefrig.2017.04.006 · doi-reference
Learning thermoacoustic interactions in combustors using a physics-informed neural network
10.1016/j.engappai.2024.109388 · doi-reference
Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts
10.1016/j.ijfatigue.2023.107500 · doi-reference
Physics-informed neural network for creep-fatigue life prediction of Inconel 617 and interpretation of influencing factors
10.1016/j.matdes.2024.113267 · doi-reference
Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis
10.1186/s12911-024-02734-6 · doi-reference
Determining pressure from velocity via physics-informed neural network
10.1016/j.euromechflu.2024.08.007 · doi-reference
Accelerated multiscale space–time finite element simulation and application to high cycle fatigue life prediction
10.1007/s00466-016-1296-9 · doi-reference
10.1016/j.compositesb.2025.112527
10.1016/j.compositesb.2025.112527 · doi-reference
Storage life prediction under pre-strained thermally-accelerated aging of HTPB coating using the change of crosslinking density
10.1016/j.dt.2020.07.008 · doi-reference
Enhancing structural analysis efficiency: a comprehensive review and experimental validation of advanced submodeling techniques, introducing the submodeling-density-shape-element removal (S-D-S-ER) method
10.1108/ec-03-2024-0188 · doi-reference
Experimental study of impulse-current-induced electrical, arc, thermal, and mechanical multiphysics coupling on carbon fiber-reinforced polymer composites
10.1016/j.tws.2025.113972 · doi-reference
Multi-physics coupled prediction method for the interlaminar performance of thermoplastic composites considering manufacturing-induced residual stresses
10.1016/j.compscitech.2026.111615 · doi-reference
Advances in additively manufactured multi-principal element alloys for turbine blades in next generation jet engines
10.3390/aerospace13050395 · doi-reference
Lifetime prediction using accelerated test data and neural networks
10.1016/j.compstruc.2008.12.007 · doi-reference
The detrimental effect of quenching-induced plastic strain on the creep property of nickel-based superalloy turbine discs
10.1016/j.jallcom.2026.187963 · doi-reference
MFLPPINN: A physics-informed neural network for multiaxial fatigue life prediction
10.1016/j.euromechsol.2022.104889 · doi-reference
Prediction of fatigue–crack growth with neural network-based increment learning scheme
10.1016/j.engfracmech.2020.107402 · doi-reference
A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures
10.1016/j.engfracmech.2021.108130 · doi-reference
A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints
10.1016/j.ijfatigue.2024.108566 · doi-reference
Prediction of remaining useful life for electronic equipment based on online PINN
10.1038/s41598-025-32497-7 · doi-reference
A synergistic multi-scale physics-informed neural network for wake flow reconstruction from sparse data
10.1016/j.oceaneng.2026.125072 · doi-reference
A data-assisted physics-informed neural network for predicting fatigue life of electronic components under complex shock loads
10.1016/j.ijfatigue.2025.108933 · doi-reference
PINN-parafoil: A physics-informed neural network method for complex parafoil dynamics simulating
10.1016/j.jocs.2025.102639 · doi-reference
A PINN methodology for temperature field reconstruction in the PIV measurement plane: Case of Rayleigh–Bénard convection
10.1016/j.icheatmasstransfer.2025.109284 · doi-reference
Fouling modeling and prediction approach for heat exchangers using deep learning
10.1016/j.ijheatmasstransfer.2020.120112 · doi-reference
Data-driven enhanced rough contact mechanics: PINN estimation of gap distribution across length scales for partial contacts
10.1016/j.triboint.2025.111100 · doi-reference
A novel singularity- and discontinuity-capturing PINN for time-fractional diffusion equations involving initial singularities and interfaces on complex curved surfaces
10.1016/j.amc.2025.129917 · doi-reference
Gradient-enhanced PINN-based hybrid attitude control for reentry vehicle under uncertainties
10.1016/j.ast.2025.111600 · doi-reference
Comparative analysis of standard PINN and gradient-enhanced PINN approaches for thin plate bending problems
10.1007/s10409-025-25762-x · doi-reference
PINN-based joint identification and low-dimensional dynamical modeling of joint-assembled structures
10.1016/j.ijmecsci.2025.111109 · doi-reference
Using Physics-Informed neural networks for solving Navier-Stokes equations in fluid dynamic complex scenarios
10.1016/j.engappai.2025.110347 · doi-reference
A comprehensive PINN method with hybrid Fourier feature for high-precision natural convection solution
10.1016/j.energy.2026.140302 · doi-reference
Physics informed neural network for hidden thermal field discovery in porous media flows
10.1016/j.ijheatmasstransfer.2026.128806 · doi-reference
PINN with dynamic constraint optimization for complex air-based TABS thermal dynamics prediction
10.1016/j.energy.2026.140989 · doi-reference
High-cycle and very-high-cycle fatigue life prediction in additive manufacturing using hybrid physics-informed neural networks
10.1016/j.engfracmech.2025.111026 · doi-reference