WaveletAE detects wind turbine blade icing using SCADA data.
problem Detecting blade icing in wind turbines for maintenance.
method Wavelet-enhanced autoencoder with CNN-LSTM architecture.
result WaveletAE outperforms other anomaly detection methods.
Deep learning predicts turbine blade pressures with high accuracy.
problem Predicting turbomachinery performance using deep learning.
method Three deep neural networks were built and trained to predict pressure distributions of turbine airfoils.
result A four-layer convolutional neural network with two layers of fully connected neural network provided the best predictions.
Calibrating materials to engineering targets improves wind turbine design.
problem Separate material and mechanical design processes lead to uncertainty.
method Repurpose calibration techniques to integrate material and mechanical design.
result Materials can be designed with specific engineering targets in mind.
New scalarizing functions improve multi-objective Bayesian optimisation.
problem Improving multi-objective Bayesian optimisation efficiency.
method Comparing two infill criteria based on hypervolume improvement.
result Effective scalarizing functions enhance hypervolume maximisation.
Bayesian optimization identifies optimal alloy formulations.
problem Accelerated discovery in materials science with autonomous systems.
method Bayesian optimization over problem formulation space.
result Framework converges on optimal alloy formulations.
BLADE uses Bayesian methods to discover complex systems from scarce data.
problem Efficiently discovering governing equations of complex dynamical systems from limited data.
method Combines replica-exchange stochastic gradient Langevin Monte Carlo with active learning.
result Reduces measurement requirements by 60% for Lotka-Volterra and 40% for Burgers' equation.
Blade uses diffusion priors to accurately and calibratedly infer complex systems.
problem Derivative-free Bayesian inversion for high-dimensional, nonlinear problems with costly forward models.
method Blade employs an ensemble of interacting particles and diffusion models as priors, querying forward models only through evaluations.
result Blade produces well-calibrated posterior samples that existing methods cannot, improving with more iterations and particles.
This work models variability in composite blades' vibrations.
problem Variability in structural dynamics due to manufacturing differences.
method Defining a general model for frequency response functions using mixtures of Gaussian processes.
result A general model for frequency response functions of composite blades.
A method to improve surrogate model accuracy using multiple fidelity models.
problem Efficiently combining models of varying accuracy and computational cost.
method Multifidelity Gaussian process models and leave-one-out cross-validation.
result Reduced LOO-CV error at the highest fidelity through adaptive learning.
Researchers developed a generic model to account for structural variability in SHM.
problem Variability in natural frequency due to operational and environmental conditions limits SHM technologies.
method An overlapping mixture of Gaussian processes (OMGP) was used to generate a generic representation of normal condition.
result The OMGP model provided a generic representation (form) to characterise the normal condition of structures.
Wind turbine status classification models are improved for cross-site applicability.
problem Standard models don't generalize across different wind turbines.
method Data normalization and convolutional neural networks with feature-space extension.
result Trained models successfully classified wind turbines from different sites.
ROM-net framework applies to industrial design uncertainty quantification.
problem Uncertainty quantification in industrial design models.
method Dictionary-based ROM-net framework for reduced order modeling.
result ROM-net computes predictions in 2 hours with high accuracy.
Machine learning analyzes AIS data to track offshore wind turbine installations.
problem Tracking and optimizing the installation of offshore wind turbines using jack-up vessels.
method Applied machine learning to AIS data from jack-up vessels to identify turbine locations and installation times.
result Automated identification of turbine locations and installation times using only AIS data.
Study assesses risk of upward lightning at wind turbines using direct measurements and machine learning.
problem Risk underestimation of upward lightning at wind turbines due to limited detection by current standards.
method Direct UL measurements linked to meteorological reanalysis data using random forests.
result Risk maps based on case study events show high probabilities coincide with actual UL events.
Bayesian method maps high-dimensional inputs to lower dimensions for efficient multi-fidelity Gaussian Process modeling.
problem Efficiently modeling high-dimensional inputs with low-dimensional latent variables for multi-fidelity Gaussian Processes.
method Bayesian approach with orthonormal projection matrix inference using Markov Chain Monte Carlo (MCMC) and Geodesic Monte Carlo sampling.
result Optimal transformations identified that improve computational efficiency in multi-fidelity Gaussian Process modeling.
Paper uses neural networks to predict NOx emissions from gas turbines.
problem Predicting NOx emissions from degrading gas turbines.
method Applied neural network algorithm to model NOx emissions from nine process variables.
result Neural network model optimizes process variables for minimal NOx emissions.
A novel ensemble classifier improves vibration-based quality monitoring accuracy.
problem Developing high accuracy classification methods for general datasets.
method Dempster-Shafer theory of evidence with three remedies for conflicting evidences.
result The proposed ensemble classifier outperforms state-of-the-art fusion techniques.
New taxonomy for SCADA-based wind turbine fault detection improves model performance.
problem Lack of consensus on feature causality in normal behavior models.
method Presented a new taxonomy based on causal relations between input features and target.
result Evaluation of different feature configurations on fault detection performance.
Deep learning improves combustor anomaly detection in gas turbines.
problem Improving anomaly detection performance in gas turbine combustors.
method Hierarchically learned features from exhaust gas temperature sensor measurements using deep learning.
result Deep learning-based anomaly detection significantly improved combustor anomaly detection performance.
Bayesian model aligns wind turbine sensor data, revealing shared structure.
problem Finding common structure in wind turbine sensor data.
method Latent shared information, non-parametric output warpings, nested variational compression.
result Extracts shared information between wind turbine sensor data.
We prove a modified version of Turbiner's conjecture in three dimensions and we give a counter-example to the original conjecture. The Lie algebraic Schrödinger operators corresponding to flat metrics of a certain restricted type are shown to separate partially in either Cartesian, cylindrical or spherical coordinates.
We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…
Paper reduces turbomachinery CFD simulations by identifying key dimensions.
problem Reducing computational cost in turbomachinery 3D CFD simulations.
method Statistical sufficient dimension reduction methods and polynomial variable projection.
result Polynomial variable projection accurately identifies dimension reducing subspaces at lower cost.
Study monitors wind turbine drivetrain bearings using dictionary learning from vibration data.
problem Early detection of faults in wind turbine drivetrain bearings with minimal false positives.
method Unsupervised dictionary learning from 46 months of vibration data.
result Abnormal dictionary adaptation signals faults 6-12 months before bearing or gearbox replacement.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
problem Designing optimal hydrokinetic turbine shapes due to high cost and geometric constraints.
method Design-by-Morphing (DbM) and Mixed variable, Multi-Objective Bayesian Optimization (MixMOBO).
result Optimized shapes lead to maximum power output with minimal evaluations.
LOL-GP model improves surrogate modeling of expensive simulators.
problem Costly computer simulations for complex systems.
method Local transfer learning Gaussian process.
result Improved surrogate performance over existing methods.
Deep CNN predicts wind power with 49.83% less error.
problem Wind power prediction lacks temporal and spatial representation.
method Mapped turbine data to state maps, applied deep CNN.
result Deep CNN reduces MSE by 49.83%.
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
New algorithm learns coordinated decisions in loosely-coupled multi-agent systems.
problem Learning coordinated decisions in multi-agent systems with sparse interactions.
method Multi-Agent Thompson Sampling (MATS) for multi-agent multi-armed bandits.
result MATS achieves sublinear regret and outperforms MAUCE on synthetic and real benchmarks.
Study predicts wind energy potential in Gulf of Oman using climate models.
problem Predicting future wind energy potential in the Gulf of Oman.
method Used ERA5 and MENA simulations to project historical and future wind energy variability.
result Selected locations have suitable potential for wind power turbine construction.
Turbiner's conjecture posits that a Lie-algebraic Hamiltonian operator whose domain is a subset of the Euclidean plane admits a separation of variables. A proof of this conjecture is given in those cases where the generating Lie-algebra acts imprimitively. The general form of the conjecture is false. A counter-example …
Approximate 3D elastic curves with exact constraints
problem Designing and approximating 3D elastic curves
method Numerically stable method for recovering 11 parameters
result Fast and stable approximation of arbitrary curves
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
This paper improves surrogate modeling for noisy data.
problem Uncertainty in high-fidelity models due to noise.
method Comprehensive framework for multi-fidelity surrogate modeling.
result Estimates uncertainty in high-fidelity model predictions.
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
problem Multidisciplinary design analysis of coupled engineering systems requires solving equilibrium states where all disciplinary coupling variables are consistent.
method Residual manifold surrogate modeling framework for coupled systems.
result REMAL learns a surrogate model of the joint residual manifold via multitask Gaussian process models.
MLM models match or exceed RN in generating wind power time series without location info.
problem Generating accurate long-term wind power time series without location information.
method Applied neural networks to MERRA2 wind speed data with and without location info.
result MLM models produce time series of equal or better quality than RN.
We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. The BNNs are trained by minimizing α-divergences, allowing us to capture complicated statistical patterns in the transition dynamics, e.g.…
An empirical study of joint bivariate probability distribution of two consecutive price increments for a set of stocks at time scales ranging from one minute to thirty minutes reveals asymmetric structures with respect to the axes y=0, y=x, x=0 and y=-x. All four asymmetry patterns remarkably resemble a four-blade mill…
Study uses ANFIS to assess wind power under climate change.
problem Tackles climate change impact on wind power potential.
method Employed ANFIS to match climate model data with reference data.
result Real wind power potential lower than projected.
New shape representation for airfoils improves design and manufacturing.
problem Designing and manufacturing airfoils efficiently and accurately.
method Combining physics-based and data-driven techniques on a Grassmannian manifold.
result Rich set of novel 2D airfoil deformations not previously captured.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
problem Optimizing a power-to-heat system with fluctuating renewable energy sources.
method Stochastic optimal control, reinforcement learning (Q-learning).
result Reinforcement learning provides an efficient solution to the optimization problem.
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
problem Creating accurate, computationally efficient models for methane combustion.
method Data-oriented three-step methodology: 1) Remove non-essential species, 2) Numerically optimize to key species profiles, 3) Machine learning to refine parameters.
result Produced 19 and 15 species compact models that outperform current state-of-the-art models in accuracy and range of conditions.
We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it. We propose and characterize a very broad class of preference matrices giving rise to the Feature Low Rank (FLR) model, which subsumes several models ranging from the classic Bradl…
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.
problem Improving yaw control inputs for maximum power production in wind farms.
method Gaussian process regression and modifier adaptation scheme based on high-fidelity simulation data.
result Both modifier adaptation and Bayesian optimization improve power production with smaller yaw misalignments.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
problem Missing data in wind farm records due to sensor failures.
method Combines spectral graph theory and k-Nearest Neighbors to estimate missing data.
result Significant improvement in data reconstruction over existing methods.
In this paper, a new algorithm based on differential geometry viewpoint to solve the 3D rotating Navier-Stokes equations with complex Boundary is proposed, which is called Bi-parallel algorithm. For xample, it can be applied to passage flow between two blades in impeller and circulation flow through aircrafts with comp…