Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption possible. Composite materials replaced aluminum alloys even primary to achieve higher performances lighter components. However, random events such low-velocity impacts may induce damages that typically more dangerous and mostly not visible than metals. The damage tolerance (DT) approach is adopted for the fatigue design of aircraft, but fracture mechanisms propagation failure...

Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high-performance physics-based simulators for predicting behavior in charged beam are computationally expensive, limiting their utility solving inverse problems online. The problem estimating upstream six-dimensional (6D) phase space given downstream measurements particles an accelerator growing importance. This paper introduces reverse latent evolution model designed...

Identification of elastic properties is crucial for nondestructive material characterization as well in-situ condition monitoring. In this paper, we have used ultrasonic guided waves the identification a unidirectional laminate with stacked transversely isotropic lamina. The forward problem formulated and solved using Spectral Finite Element Method. data collected from model utilized to solve inverse property identification. A supervised regression-based 1D-Convolutional Neural Network...

In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type identifying material properties. the forward problem, polar group velocity representations obtained for fundamental Lamb wave modes using stiffness matrix method. For supervised classification-based network is implemented classify into types (inverse problem - 1) regression-based utilized identify properties 2)

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due limited non-destructive measurements, computationally demanding simulations, inherent uncertainties in the system. We propose two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for spatiotemporal dynamics of particles accelerators. CLARM consists Variational...

The effect of temperature on guided waves is considered one the crucial aspects a structural health monitoring procedure. influence can cause abrupt variations in actual signatures and interfere with existing damage identification strategies. In this paper, we have addressed issue self-supervised deep learning-based compensation methodology. We used affected time-traces from dataset converted them into 2D-representation using continuous wavelet transformation. proposed new philosophy which...

Modern aerospace structures demand lightweight design procedures and require scheduled maintenance intervals. Supervised deep learning strategies can allow reliable damage detection provided a large amount of data is available to train. These algorithms may face problems in the absence possible scenarios training dataset. This class imbalance problem supervised curtail process possess issues related generalization on unseen examples. On other hand, unsupervised like autoencoders handle such...

Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role understanding the short-duration transient response structures. The forward physics-based models utilized to map from elastic properties space wave behavior a laminated material. Due high-frequency, multimodal, and dispersive nature guided waves, simulations computationally demanding. It makes property prediction, generation, material design problems more...

With the rising demands for robust structural health monitoring procedures aerospace structures, scope of intelligent algorithms and learning techniques is expanding. Supervised have shown promising results in field provided a large, balanced, labeled amount data training. For some applications like aerospace, collection process cumbersome, time-taking, costly. Also, generating possible damage scenarios laboratory setup challenging because complexity initiation failure mechanism. Besides...

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due limited non-destructive measurements, computationally demanding simulations, inherent uncertainties in the system. We propose two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for spatiotemporal dynamics of particles accelerators. CLARM consists Variational...

<title>Abstract</title> Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due limited non-destructive measurements, computationally demanding simulations, inherent uncertainties in the system. We propose two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for spatiotemporal dynamics of particles accelerators....

Particle accelerators are time-varying systems whose components perturbed by external disturbances. Tuning can be a time-consuming process involving manual adjustment of multiple components, such as RF cavities, to minimize beam loss due drifts. The high dimensionality the system ($\sim$100 amplitude and phase settings in LANSCE accelerator) makes it difficult achieve optimal operation. drifts make parameter estimation challenging optimization problem. In this work, we propose Variational...

Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations limited computational time. Machine learning (ML) based surrogate models have emerged as promising tool for non-invasive diagnostics. Trained ML can make predictions much faster than computationally expensive physics simulations. In this work, we proposed temporally structured variational autoencoder model to autoregressively forecast spatiotemporal dynamics of...

Complex dynamical systems, such as particle accelerators, often require complicated and time-consuming tuning procedures for optimal performance. It may also be required that these estimate the system parameters, which govern dynamics of a spatiotemporal beam -- this can high-dimensional optimization problem. To address this, we propose Classifier-pruned Bayesian Optimization-based Latent space Tuner (CBOL-Tuner), framework efficient exploration within temporally-structured latent space. The...

Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behavior in charged beam are computationally expensive, limiting their utility solving inverse problems online. The problem estimating upstream six-dimensional phase space given downstream measurements particles an accelerator growing importance. This paper introduces reverse Latent Evolution Model (rLEM) designed...

Structural Health Monitoring (SHM) deals mainly with structures instrumented by secondary bonded or embedded sensors that, acting as both signal generators and receivers, are able to "interrogate" the structure about its "health status". Sensorised appear promising for reducing maintenance costs weight of aerospace composite structures, without any reduction safety level required. Much effort has been spent during last years on analysis techniques in order extract from signals provided...

Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role understanding the short-duration transient response structures. The forward physics-based models utilized to map from elastic properties space wave behavior a laminated material. Due high-frequency, multi-modal, and dispersive nature guided waves, simulations computationally demanding. It makes property prediction, generation, material design problems more...

Guided wave propagation is a valuable and reliable technique for structural health monitoring (SHM) of aerospace structures. Along with its higher sensitivity towards small damages, it offers advantages in traveling long distances minimum attenuation. Simulation guided essential to understand behavior, calculating the dispersion relations forms an integral part procedure. Application current numerical techniques complex media highly involved faces issues related accuracy, stability,...