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Symmetric solutions of the n-body problem: A numerical study of Floquet multipliers and Morse indices
No full textthrough invariance under finite group actions. We focus on their stability properties and present algorithms
specifically designed for the computation of Floquet multipliers and Morse indices. Numerical results are
provided to illustrate our methods in both two and three dimensional configuration spaces, and for different
choices on the number of bodies
Production scraps to raw materials: low-cost method for implementing lithium iron phosphate cathode scraps back to production lines
No full textIn recent years, the increased production of lithium-ion batteries (LIBs) has been causing significant amounts of production scraps that require efficient, economical, and environmentally viable recycling methods. This study investigates strategies for integrating low-temperature direct recycling of lithium iron phosphate (LFP) production scraps into battery manufacturing. Scrap LFP cathode active material (CAM) was direct recycled at 200 °C in air and 400 °C in N2. The recycled CAM was blended in different amounts (100, 50, 30%-wt) with commercial CAM. Two slurry compositions were considered based on CAM: polyvinylidene fluoride: carbon black ratios (80:10:10 and 92:5:3), and coin cells were manufactured and tested. Results indicate that recycled CAM can be directly reprocessed in new batteries exhibiting excellent electrochemical performance (154 mAh g−1, equivalent to pristine material) when the slurry included 30%-wt CAM recycled at 200 °C in air and 100%-wt CAM recycled at 400 °C in N2. Compared to virgin slurry material cost (9.06 €/kgslurry) and environmental impact (8.27 kg CO2/kgslurry), incorporating 30%-wt CAM recycled at 200 °C in air reduced costs to 6.59 €/kgSlurry and emissions to 6.21 kgCO2/kgslurry, and 100%-wt CAM recycled at 400 °C in N2 corresponded to 3.77 €/kgSlurry and 2.45 kgCO2/kgslurry. These findings clearly demonstrate that closed-loop integration of low-temperature direct recycling of LFP cathode scraps into cell manufacturing reduces material costs and environmental impact while maintaining high electrochemical performance
An Improved Experimental Validation of Nonlinear Forced Response Simulation of Shrouded Blades
No full textFriction damping devices like tip shrouds are usually employed in low pressure turbine (LPT) blades to reduce their large vibration amplitudes. From an engineering point of view, experimental validation of the numerically predicted dynamic behavior of the blade is essential to demonstrate the damping performance of shrouds in LPTs. In accordance with this purpose, this study presents the comparison of experimental and numerical results for the detailed investigation of the dynamic behavior of shrouded turbine blades. A brief overview of the experimental test rig, which has been previously developed to measure both the nonlinear forced response and contact forces simultaneously, is first presented. The experimental results show the effect of different normal preloads and excitation force levels on the measured parameters. To compute the nonlinear forced response of the blade and the shroud contact forces, the test rig is modeled in a commercial finite element (FE) software, and the system matrices are extracted in a reduced order form. The harmonic balance method (HBM) is applied in a nonlinear solver developed dedicatedly with the implementation of a 3D contact model. The comparison of the experimental and numerical results is presented in particular cases where lower normal preload to excitation force ratio results in alternate stick and slip transitions. The results show that experimental dynamic behavior of shrouded blade is computationally captured in most of the cases. The nonmatching results are also highlighted for some cases in which the nonunique contact forces introduce the response variability. For these cases, response boundaries are numerically estimated by utilizing an optimization algorithm. The outcomes of this paper consequently exhibit a detailed validation procedure for the simulation tools and an understanding of the numerical concerns like convergence
Dynamic Cooperative Energy and Coverage Management for V2G-Enhanced RAN Resilience
No full texthe resilience of cellular communication is
paramount, given how widely its services are used. However,
it also depends on the resilience of the power grid, which
is increasingly threatened by extreme weather events and the
growing reliance on distributed, intermittent energy sources.
Current base station backup systems, typically limited to 2-6
hours of autonomy, operate in isolation and fail to leverage
spatial redundancy. This paper proposes the Multi-Site Resiliency
Cooperative (MSRC) framework, a unified control strategy that
transforms independent sites into a collaborative energy cluster.
We formulate a two-stage mixed-integer optimization problem
that jointly manages radio coverage adaptation switching sites
between helping, assisted, and deep-sleep modes and dynamic
Vehicle-to-Grid (V2G) energy injection. By proactively reshaping
cell boundaries and traffic loads based on real-time battery states,
MSRC maximizes network survival while prioritizing critical
service classes. Extensive simulations on a 19-site urban network
demonstrate that the proposed framework extends survival time
by 96% and maintains 94% service continuity compared to
conventional baselines. Crucially, MSRC requires no physical in-
frastructure upgrades, offering operators a deployable, software-
defined solution for outage-resilient green communications
"Characterization of AlSi10Mg interlocking structures additively manufactured via Laser Powder Bed Fusion"
Get PDFMechanical interlocking is a joining technique capable of mechanically bonding two dissimilar materials by means of protrusions on the component surfaces, as an alternative or complement to mechanical fasteners or adhesives. Proper adhesion between materials, and joint retention mainly depend on the shape and strength of the interlocking structures. In this respect, additive manufacturing is used to optimize the interlocking design and performance. The intent of this research is to evaluate the effectiveness of laser powder bed fusion and post-processing treatments on AlSi10Mg interlocking structures. The geometry, microstructure and density of the structures are investigated utilizing metallographic and tomographic techniques
Estimating Temperature in a Permanent-Magnet Synchronous Motor Using Hammerstein and Nonlinear Autoregressive Models Initialized Via Thermal Networks
No full textMonitoring the temperature of permanent-magnet synchronous motors is crucial to prevent failures in sensitive components such as windings and permanent magnets. In this respect, machine learning techniques have been used to generate models to estimate the temperature of rotor and stator hotspots. However, the effective use of data-driven methods requires large datasets, extensive training time, and substantial computational power. Moreover, machine learning methods mostly operate with a black-box approach; they do not account for the physics of the system to be modeled. This paper proposes and compares Hammerstein and nonlinear autoregressive exogenous models to estimate the temperature of the permanent magnets and windings of an out-runner permanent-magnet synchronous motor. A linear time-invariant component, used for both the Hammerstein and nonlinear autoregressive exogenous models, is initialized via a previously identified fourth-order lumped parameter thermal network. This model accounts for the thermal behavior of the machine. The nonlinear component is modeled via a neuron sigmoid network. Results show that the Hammerstein model achieves a lower mean squared error for the winding temperature estimation than the nonlinear autoregressive exogenous model. The opposite is true for the magnet temperature estimation
Machine learning applied to high-entropy alloy coatings process parameters and composition optimization – A case study
No full textRecent research has indicated that Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi high entropy alloys (HEAs) exhibit superior mechanical and thermal properties under extreme conditions. This chapter provides an account of the wear and surface characteristics of cold-sprayed HEA coatings at various temperatures. The inputs of surface roughness and volume variation are analyzed by analysis of variance (ANOVA), employing formulas optimized by genetic algorithms. Gaussian process regression, support vector regression (SVR), and artificial neural networks are machine learning (ML) methods that predict surface roughness and volume variation with high accuracy. For surface roughness, SVR achieves a coefficient of determination of 0.97, which is lower than that determined from the other models. Furthermore, all three models achieve a coefficient of determination of 0.99 for volume variation. The results indicate the ability of ML to generalize effectively across datasets, capturing nonlinear patterns with precision. These findings emphasize the potential of HEAs for high-wear applications and the reliability of predictive modeling
Relaxation for a degenerate functional with linear growth in the onedimensional case
No full textIn this work, we study the relaxation of a degenerate functional with linear growth, depending on a weight w that does not exhibit doubling or Muckenhoupt-type conditions. In order to obtain an explicit representation of the relaxed functional and its domain, our main tools for are Sobolev inequalities with double weight
A Survey of Memory Models for Virtual Agents and Humans: From Psychological Foundations to Computational Architectures
No full textVirtual Humans are an advanced class of Virtual Agents characterized by human-like embodiment and cognitive capabilities. Central to their adaptivity is the integration of computational cognitive architectures, in which memory models play a key role in learning, continuity, and contextual reasoning. This review bridges psychological theories of memory and their computational implementations by comparing symbolic and connectionist approaches and exploring new paradigms such as Memory-Augmented Neural Networks and Large Language Models. We propose a unified framework for analyzing the components of artificial memory - Working, Semantic, Episodic, Procedural, Spatial, and Autobiographical Memory - and review their applications in domains such as education, games, and social simulation. Finally, we discuss open challenges and future works