arXiv subjects

Simone Casolo

Publications and source records attributed to Simone Casolo.

10 recordsLinked to original sources

arXiv2026

Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health

Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertainty. While traditional Bayesian methods like Markov Chain Monte Carlo (MCMC) provide rigorous uncertainty quantification, their heavy computational bottlenecks render them impractical for real-time process control. To overcome this limitation, we propose an AI-driven framework utilizing Simulation-Based Inference (SBI) powered by amortized neural posterior estimation to diagnose complex failure modes in heat exchangers. By training neural density estimators on a simulated dataset, our approach learns a direct, likelihood-free mapping from thermal-fluid observations to the full posterior distribution of degradation parameters. We benchmark this framework against an MCMC baseline across various synthetic fouling and leakage scenarios, including challenging low-probability, sparse-event failures. The results show that SBI achieves comparable diagnostic accuracy and reliable uncertainty quantification, while accelerating inference time by a factor of82$\times$ compared to traditional sampling. The amortized nature of the neural network enables near-instantaneous inference, establishing SBI as a highly scalable, real-time alternative for probabilistic fault diagnosis and digital twin realization in complex engineering systems.

cs.LG↗

arXiv2024

Testing Topological Data Analysis for Condition Monitoring of Wind Turbines

We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation. TDA is a branch of data analysis focusing on extracting meaningful information from complex datasets by analyzing their structure in state space and computing their underlying topological features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anomalies, and trends in the data that may not be apparent through traditional signal processing methods. For this study, wind turbine data was acquired from a wind park in Norway via standard vibration sensors at different locations of the turbine's gearbox. Both the vibration acceleration data and its frequency spectra were recorded at infrequent intervals for a few seconds at high frequency and failure events were labelled as either gear-tooth or ball-bearing failures. The data processing and analysis are based on a pipeline where the time series data is first split into intervals and then transformed into multi-dimensional point clouds via a time-delay embedding. The shape of the point cloud is analyzed with topological methods such as persistent homology to generate topology-based key health indicators based on Betti numbers, information entropy and signal persistence. Such indicators are tested for CBM and diagnosis (fault detection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an interesting alternative for failure identification and diagnosis of operational failures in wind turbines.

eess.SY↗

arXiv2012

Theoretical analysis of oxygen vacancies in layered sodium cobaltate Na_xCoO_{2-δ}

Sodium cobaltate with high Na content is a promising thermoelectric material. It has recently been reported that oxygen vacancies can alter the material properties, reducing its figure of merit. However, experimental data concerning the oxygen stoichiometry are contradictory. We therefore studied the formation of oxygen vacancies in Na_xCoO_2 with first principles calculations, focusing on x = 0.75. We show that a very low oxygen vacancy concentration is expected at the temperatures and partial pressures relevant for applications.

cond-mat.mtrl-sci↗

arXiv2012

Atomic-scale characterization of nitrogen-doped graphite: Effects of dopant nitrogen on the local electronic structure of the surrounding carbon atoms

We report the local atomic and electronic structure of a nitrogen-doped graphite surface by scanning tunnelling microscopy, scanning tunnelling spectroscopy, X-ray photoelectron spectroscopy, and first-principles calculations. The nitrogen-doped graphite was prepared by nitrogen ion bombardment followed by thermal annealing. Two types of nitrogen species were identified at the atomic level: pyridinic-N (N bonded to two C nearest neighbours) and graphitic-N (N bonded to three C nearest neighbours). Distinct electronic states of localized π states were found to appear in the occupied and unoccupied regions near the Fermi level at the carbon atoms around pyridinic-N and graphitic-N species, respectively. The origin of these states is discussed based on the experimental results and theoretical simulations.

cond-mat.mtrl-sci↗

arXiv2011

Few simple rules governing hydrogenation of graphene dots

We investigated binding of hydrogen atoms to small Polycyclic Aromatic Hydrocarbons (PAHs) - i.e. graphene dots with hydrogen-terminated edges - using density functional theory and correlated wavefunction techniques. We considered a number of PAHs with 3 to 7 hexagonal rings and computed binding energies for most of the symmetry unique sites, along with the minimum energy paths for significant cases. The chosen PAHs are small enough to not present radical character at their edges, yet show a clear preference for adsorption at the edge sites which can be attributed to electronic effects. We show how the results, as obtained at different level of theory, can be rationalized in detail with the help of few simple concepts derivable from a tight-binding model of the $π$ electrons.

cond-mat.mes-hall↗

arXiv2011

The effect of atomic-scale defects and dopants on graphene electronic structure

Graphene, being one-atom thick, is extremely sensitive to the presence of adsorbed atoms and molecules and, more generally, to defects such as vacancies, holes and/or substitutional dopants. This property, apart from being directly usable in molecular sensor devices, can also be employed to tune graphene electronic properties. Here we briefly review the basic features of atomic-scale defects that can be useful for material design. After a brief introduction on isolated $p_z$ defects, we analyse the electronic structure of multiple defective graphene substrates, and show how to predict the presence of microscopically ordered magnetic structures. Subsequently, we analyse the more complicated situation where the electronic structure, as modified by the presence of some defects, affects chemical reactivity of the substrate towards adsorption (chemisorption) of atomic/molecular species, leading to preferential sticking on specific lattice positions. Then, we consider the reverse problem, that is how to use defects to engineer graphene electronic properties. In this context, we show that arranging defects to form honeycomb-shaped superlattices (what we may call "supergraphenes") a sizeable gap opens in the band structure and new Dirac cones are created right close to the gapped region. Similarly, we show that substitutional dopants such as group IIIA/VA elements may have gapped quasi-conical structures corresponding to massive Dirac carriers. All these possible structures might find important technological applications in the development of graphene-based logic transistors.

cond-mat.mtrl-sci↗

arXiv2011

Band engineering in graphene with superlattices of substitutional defects

We investigate graphene superlattices of nitrogen and boron substitutional defects and by using symmetry arguments and electronic structure calculations we show how such superlattices can be used to modify graphene band structure. Specifically, depending on the superlattice symmetry, the structures considered here can either preserve the Dirac cones (D_{6h} superlattices) or open a band gap (D_{3h}). Relevant band parameters (carriers effective masses, group velocities and gaps, when present) are found to depend on the superlattice constant n as 1/n^{p} where p is in the range 1-2, depending on the case considered. Overall, the results presented here show how one can tune the graphene band structure to a great extent by modifying few superlattice parameters.

cond-mat.mtrl-sci↗

arXiv2010

Role of the self-interaction error in studying chemisorption on graphene from first-principles

Adsorption of gaseous species, and in particular of hydrogen atoms, on graphene is an important process for the chemistry of this material. At the equilibrium geometry, the H atom is covalently bonded to a carbon that puckers out from the surface plane. Nevertheless the \emph{flat} graphene geometry becomes important when considering the full sticking dynamics. Here we show how GGA-DFT predicts a wrong spin state for this geometry, namely $S_z$=0 for a single H atom on graphene. We show how this is caused by the self-interaction error since the system shows fractional electron occupations in the two bands closest to the Fermi energy. It is demonstrated how the use of hybrid functionals or the GGA+$U$ method an be used to retrieve the correct spin solution although the latter gives an incorrect potential energy curve.

cond-mat.mtrl-sci↗

arXiv2009

Symmetry-induced gap opening in graphene superlattices

We study nxn honeycomb superlattices of defects in graphene. The considered defects are missing p_z orbitals and can be realized by either introducing C atom vacancies or chemically binding simple atomic species at the given sites. Using symmetry arguments we show how it is possible to open a gap when n=3m+1,3m+2 (m integer), and estimate its value to have an approximate square-root dependence on the defect concentration x=1/n^2. Tight-binding calculations confirm these findings and show that the induced-gaps can be quite large, e.g. ~100 meV for x~10^{-3}. Gradient-corrected density functional theory calculations on a number of superlattices made by H atoms adsorbed on graphene are in good agreement with tight-binding results, thereby suggesting that the proposed structures may be used in practice to open a gap in graphene.

cond-mat.mtrl-sci↗

arXiv2008

Understanding adsorption of hydrogen atoms on graphene

Adsorption of hydrogen atoms on a single graphite sheet (graphene) has been investigated by first-principles electronic structure means, employing plane-wave based, periodic density functional theory. A reasonably large 5x5 surface unit cell has been employed to study single and multiple adsorption of H atoms. Binding and barrier energies for sequential sticking have been computed for a number of configurations involving adsorption on top of carbon atoms. We find that binding energies per atom range from ~0.8 eV to ~1.9 eV, with barriers to sticking in the range 0.0-0.2 eV. In addition, depending on the number and location of adsorbed hydrogen atoms, we find that magnetic structures may form in which spin density localizes on a $\sqrt{3}{x}\sqrt{3}{R}30^{\circ}$ sublattice, and that binding (barrier) energies for sequential adsorption increase (decrease) linearly with the site-integrated magnetization. These results can be rationalized with the help of the valence-bond resonance theory of planar $π$ conjugated systems, and suggest that preferential sticking due to barrierless adsorption is limited to formation of hydrogen pairs.

cond-mat.mtrl-sci↗