Unstable Behavior Research Articles

Micro-damage instability mechanisms in composite materials: Cracking coalescence versus fibre ductility and slippage

The load-displacement softening response of quasi-brittle solids exhibits an unstable structural behavior, which is characterised by a negative slope in the post-peak regime. In severely brittle situations, the post-peak behaviour can show a virtual positive slope, the fracture propagation occurring unexpectedly with a catastrophic loss in the load-carrying capacity. In this case, if the displacement controls the loading process, the curve exhibits a discontinuity and the representative point drops to the lower branch with a negative slope. On the other hand, in order to obtain a stable crack growth, a decrease both in load and in displacement is required. In the last forty years, in-depth study of the so-called snap-back instability was conducted in relation to crack propagation phenomena in quasi-brittle materials. In the present work, the structural response of two brittle-matrix specimens is analysed: the first contains a distribution of collinear micro-cracks, whereas the second presents multiple parallel reinforcing fibres embedded in the matrix. In both cases, it is shown that the structural response presents a discrete number of snap-back instabilities with related peaks and valleys, the crack propagation occurring alternately within the matrix and through the heterogeneities. Thus, the strong analogy between weakened and strengthened zones consists in a multiple snap-back mechanical response, where descending branches of propagating cracks alternate with ascending (linear) branches of arrested cracks.

Experimental Characterization of a Bladeless Air Compressor

Abstract The Tesla compressor is an innovative technology that offers a unique approach to fluid compression. Unlike traditional compressors that use rotating blades, bladeless compressors utilize closely spaced disks to create compression. The purpose of this article is to design a prototype Tesla air compressor with optimal design parameters and investigate the performance and loss characteristics based on numerical analysis and experimental demonstration. The prototype model has been numerically investigated at different rotational speeds, and the results have been compared with those obtained in experiments. Computational fluid dynamics (CFD) simulations indicate that the rotor-only efficiency is greater than 90% at very low mass flowrates, while the coupling of the rotor and volute leads to a total-to-static efficiency of approximately 58% (without losses) at 14 g/s. At a nominal mass flow of 4 g/s, the highest total-to-static pressure ratio would be around 1.27. Experimental results indicate leakage losses greatly reduce net mass flow, while pressure ratio values are in good agreement with CFD predictions. During this experiment, a maximum isentropic efficiency of 32.4% is measured. Indeed, the prototype included ventilation and leakage losses, which were not modeled in the CFD analysis. It is remarkable that the compressor does not show any unstable behavior down to zero mass flow (closed valve test), where the CFD and the experiment show consistent pressure ratios. An estimation of the losses from end-wall friction and leakage flow is carried out using numerical simulations at different exit radial clearances. Increasing radial clearance results in an increase in leakage and end-wall power loss, the latter being driven mainly by the axial clearance with the casing, which remained unchanged. To minimize leakage, a Teflon ring has been used as a first measure. Numerical calculations have indicated that the leakage rate is approximately 6 g/s at design speed. A brush seal-type solution can improve the sealing system to reduce leakage.

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

Investigating the role of PmrB mutation on Colistin antibiotics drug resistance in Klebsiella Pneumoniae

Klebsiella pneumoniae, a member of the Enterobacteriaceae family, naturally resides in the digestive tracts of both healthy animals and humans. Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant health risk for hospitalized patients worldwide, greatly reducing the effectiveness of commonly used antibiotics. This leaves healthcare providers with limited treatment options, often relying on colistin. PmrB is important for the survival of Klebsiella pneumonia and, a mutation in the PmrB protein is accountable for the development of colistin antibiotic resistance in Klebsiella pneumonia. This is especially important because colistin is a fundamental component in the treatment of pneumonia. Three mutated residues—T157P, G207D, and T246A—are responsible for colistin resistance. The structural alterations and underlying mechanisms in the PmrB protein that cause resistance owing to mutation remain unclear. As a result, this study is focused to the exploration of the putative mechanism of resistance resulting from these mutations, as well as the structure modification of normal and mutant PmrB proteins, using molecular docking and molecular dynamics simulations analysis. Our results demonstrated that the interaction paradigm for the mutants has been altered and thus showing a significant effect upon the hydrogen bonding network. Interestingly, the binding of Colistin with the three mutant demonstrate unstable behavior as compared with WT+Colistin. The proposed drug-resistance mechanism will help to guide the development of PmrB drugs. These finding may give a new framework for developing novel drugs against the mutant version of PmrB.

Predicting Phylogenetic Bootstrap Values via Machine Learning.

Estimating the statistical robustness of the inferred tree(s) constitutes an integral part of most phylogenetic analyses. Commonly, one computes and assigns a branch support value to each inner branch of the inferred phylogeny. The still most widely used method for calculating branch support on trees inferred under Maximum Likelihood (ML) is the Standard, non-parametric Felsenstein Bootstrap Support (SBS). Due to the high computational cost of the SBS, a plethora of methods has been developed to approximate it, for instance, via the Rapid Bootstrap (RB) algorithm. There have also been attempts to devise faster, alternative support measures, such as the SH-aLRT (Shimodaira-Hasegawa-like approximate Likelihood Ratio Test) or the UltraFast Bootstrap 2 (UFBoot2) method. Those faster alternatives exhibit some limitations, such as the need to assess model violations (UFBoot2) or unstable behavior in the low support interval range (SH-aLRT). Here, we present the Educated Bootstrap Guesser (EBG), a machine learning-based tool that predicts SBS branch support values for a given input phylogeny. EBG is on average 9.4 (σ = 5.5) times faster than UFBoot2. EBG-based SBS estimates exhibit a median absolute error of 5 when predicting SBS values between 0 and 100. Furthermore, EBG also provides uncertainty measures for all per-branch SBS predictions and thereby allows for a more rigorous and careful interpretation. EBG can, for instance, predict SBS support values on a phylogeny comprising $1654$ SARS-CoV2 genome sequences within 3 hours on a mid-class laptop. EBG is available under GNU GPL3.

On the Criterion for Selecting the Models of Viscoelasticity for Description of the Unstable behavior of Geosystems

On the Criterion for Selecting the Models of Viscoelasticity for Description of the Unstable behavior of Geosystems

Enhancing disturbance rejection in boost converter for CPL: A controller design approach with partial pole placement and approximate model matching

In DC microgrids, the cascade operation of the DC-DC converters leads to a constant power load fed by the boost converter, resulting in unstable behavior in addition to the non-minimum phase dynamics. An enhanced load disturbance rejection control scheme based on partial pole placement and an approximate model matching technique is proposed. This approach is inspired by the well-known direct synthesis method to achieve optimal load disturbance rejection performance. The effectiveness of the proposed control scheme has been validated through hardware implementation, demonstrating its capability in setpoint tracking and load disturbance rejection under variations in load power and input voltage. The robust stability of the suggested scheme has been investigated through the Kharitonov theorem and Monte-Carlo simulation, considering different performance matrices. The suitability of the presented scheme has been established by analyzing the comparative performance of recently reported works. The proposed controller reduces peak overshoot and settling time by ∼50%, handles system uncertainties up to 75%, and outperforms FOPID controllers tuned with particle swarm optimization, queen bee genetic algorithm, and chaos game optimization methods.

Experimental validation of the amplitude ratio as a metric for milling stability identification

This paper presents the experimental validation of the amplitude ratio, a metric for milling stability identification. The amplitude ratio quantifies the severity of chatter by comparing the amplitude of the expected frequency content of a milling signal (i.e., tooth passing frequency, runout frequency, and harmonics) to the amplitude of the chatter frequency, if present. Through multiple iterations of a milling time domain simulation, the amplitude ratio diagram, which characterizes stable and unstable milling behavior over a range of spindle speeds and axial depths of cut, may be generated. In this paper, a comparison of the simulated and measured amplitude ratios for a series of milling test cuts is presented. It is shown that the amplitude ratio is suitable for identifying milling stability in both simulations and experiments. Additionally, it is shown that through judicious selection of low-cost sensors, implementation of the amplitude ratio is cost efficient. Direct comparison of the simulated and measured amplitude ratios demonstrates the effectiveness of the approach.

Scrutinizing black hole stability in cubic vector Galileon theories

In a subclass of generalized Proca theories where a cubic vector Galileon term breaks the U(1) gauge invariance, it is known that there are static and spherically symmetric black hole (BH) solutions endowed with nonvanishing temporal and longitudinal vector components. Such hairy BHs are present for a vanishing vector-field mass (m=0) with a non-zero cubic Galileon coupling β 3. We study the linear stability of those hairy BHs by considering even-parity perturbations in the eikonal limit. In the angular direction, we show that one of the three dynamical perturbations has a nontrivial squared propagation speed c Ω,1 2, while the other two dynamical modes are luminal.We could detect two different unstable behaviors of perturbations in all the parameter spaces of hairy asymptotically flat BH solutions we searched for. In the first case, an angular Laplacian instability on the horizon is induced by negative c Ω,1 2. For the second case, it is possible to avoid this horizon instability, but in such cases, the positivity of c Ω,1 2 is violated at large distances. Hence these hairy BHs are generally prone to Laplacian instabilities along the angular direction in some regions outside the horizon. Moreover, we also encounter a pathological behavior of the radial propagation speeds c r possessing two different values of c r 2 for one of the dynamical perturbations. Introducing the vector-field mass m to cubic vector Galileons, however, we show that the resulting no-hair Schwarzschild BH solution satisfies all the linear stability conditions in the small-scale limit, with luminal propagation speeds of three dynamical even-parity perturbations.

Microscopic and elemental analysis of temperature-induced changes in sulfur/silicon nitride stack-passivated Si surface

Sulfur passivation of different Czochralski Si surfaces along with hydrogenated amorphous silicon nitride capping layer has been reported. The effect of capping layer thickness and deposition temperature on sulfur reacted surface has a strong effect on initial passivation quality and its stability. Sulfur passivation on phosphorus diffused n-type textured Czochralski Si wafers capped with a 100 nm stacked hydrogenated amorphous silicon nitride layer demonstrates improved stability under heat and light with saturation current density <80 fA/cm2. A stack layer of a low temperature (≤300OC) hydrogenated amorphous silicon nitride followed by a high temperature (≈ 450OC) hydrogenated amorphous silicon nitride is adopted to achieve good thermally stable surface passivation. Sulfur-passivation with only a low temperature hydrogenated amorphous silicon nitride capping layer exhibits unstable behavior after thermal treatment due to blister/pinhole formation at ≥300OC and possible oxidation of the S-passivated surface. Photoluminescence imaging shows increased defect recombination loss due to disrupted surface passivation after thermal treatment. Fourier transform infrared spectroscopic studies demonstrate that these blister formations were caused by hydrogen effusion from Si–H and N–H bonds dissociation upon thermal processing. Time of flight secondary ion mass spectroscopy exhibits oxidation of S-reacted surface after thermal treatment that degrades the passivation quality.

Sustainability of lending diversification of sub-Saharan African low-income countries

The momentum in sub-Saharan low-income countries is leading to diversifying their funding sources to cover large infrastructure expenditures. This situation meets the expectations of international investors who are still willing to explore new opportunities. Recent access to the Eurobond market by these new players has resulted in the growing substitution of traditional concessional loans by market ones in countries with low-debt portfolios. Thus, a new sustainability approach is required to include risk premiums and potentially versatile investor behaviors. First, we use a theoretical model to show that in a context of low international interest rates, debtor countries can continue to stabilize their level of debt either through budget deficit, if it remains below a given sustainability threshold, or through budget surplus in a downturn context (high-interest rates and spreads). Second, the versatile behavior of investors could be challenging. Third, global liquidity greatly influences the debt dynamics of SSA countries so it matters more for bond spreads than domestic factors. As our empirical study shows that the current debt situation of sub-Saharan African low-income countries increasingly involved in the Eurobond market remains under control. To avoid the worst-case scenario, strong institutions should be a priority, along with appropriate management tools and methods of debt monitoring to cope with all potential vulnerability caused by undesirable side effects of such unstable behaviors.

Estimating Lab-Quake Source Parameters: Spectral Inversion from a Calibrated Acoustic System.

Laboratory acoustic emissions (AEs) serve as small-scale analogues to earthquakes, offering fundamental insights into seismic processes. To ensure accurate physical interpretations of AEs, rigorous calibration of the acoustic system is essential. In this paper, we present an empirical calibration technique that quantifies sensor response, instrumentation effects, and path characteristics into a single entity termed instrument apparatus response. Using a controlled seismic source with different steel balls, we retrieve the instrument apparatus response in the frequency domain under typical experimental conditions for various piezoelectric sensors (PZTs) arranged to simulate a three-component seismic station. Removing these responses from the raw AE spectra allows us to obtain calibrated AE source spectra, which are then effectively used to constrain the seismic AE source parameters. We apply this calibration method to acoustic emissions (AEs) generated during unstable stick-slip behavior of a quartz gouge in double direct shear experiments. The calibrated AEs range in magnitude from -7.1 to -6.4 and exhibit stress drops between 0.075 MPa and 4.29 MPa, consistent with earthquake scaling relation. This result highlights the strong similarities between AEs generated from frictional gouge experiments and natural earthquakes. Through this acoustic emission calibration, we gain physical insights into the seismic sources of laboratory AEs, enhancing our understanding of seismic rupture processes in fault gouge experiments.

Centrifugal Compressor Surge in Closed Loop Systems: Initial Modelling and Comparison with Experiments

Abstract Heat pumps are expected to play a primary role in electrification of thermal users in the residential and industrial sectors. Dynamic compressors are widely used in large size heat pumps, thanks to their industrial replicability, compact size, affordable costs, and good performance in terms of efficiency and low acoustic emissions. The instability, which may occur in a compressor installed in closed loop cycle such as in a heat pump, is quite different from the classic open loop configuration involving dynamic compressors. This is mainly due to the complexity of the compressor instability mechanism coupled to a vapor compression system connected with two- phase heat exchangers, having different thermal and fluid dynamic capacitance properties, under a physical feedback loop. The aim of this paper is to investigate the behaviour of a dynamic compressor installed in an innovative heat pump prototype, of laboratory scale, under stable and unstable conditions. A preliminary simple dynamic model of the compression system consisting of a radial compressor, condenser and evaporator, is developed to represent the heat pump compression system, which can capture the unstable behaviour. The evaporator and condenser are modelled using empirical correlations. The preliminary validation of the dynamic model results is done through a dedicated experimental campaign, under different operating conditions. Results show the complexity of the interaction between the centrifugal compressor and the heat pump loop, discerning the different contributions to the time-dependent response of the system, prompting the necessity of a detailed model in the next step of the work.

Modulating conductive filaments via thermally stable bilayer organic memristor

The basic unit of information in conductive bridge random access memory based on the redox mechanism of metal ions is physically stored in a conductive filament (CF). Therefore, the overall performance of the device is indissolubly related to the properties of such CFs, as unreliable performance often originates from unstable CFs behavior. However, accurately controlling the dissolution of CFs during device operation can be challenging due to their non-uniformity. This paper proposes a type of memristor based on a solid polymer electrolyte with a polyvinylpyrrolidone/polyvinyl alcohol composite layer structure. The properties of the composite layer are employed to regulate the number of CFs and the growth/fracture location, while the damage to the device by Joule heat is prevented. The device exhibits low operating voltage (0.5 V), stable switching conditions (2000 cycles), and a long holdup time (&amp;gt;3 × 104 s).

Inertial flow-induced fluid pressurization enhances the reactivation of rate-and-state faults

We reveal that inertial flow can introduce additional pressure perturbations which accelerate the instability of critically stressed faults. We integrate a poroelastic spring-slider model with rate-and-state friction (RSF), a nonlinear flow model characterized by the improved Izbash equation, and a universal visco-inertial permeability model. The effect of inertial flow depends on the fracture network properties, proximity to faults, and injection parameters. Significant inertial flow results in higher pressure magnitudes and pressurization rates, causing notable differences in predictions between linear and nonlinear flow models, as well as aging and slip laws. The impact of inertial flow is particularly pronounced within a specific permeability range and is enhanced by high-rate injections. The fault reactivation is delayed due to fault strengthening with RSF in response to increased pressurization rates. Conversely, the presence of inertial flow enhances fault reactivation and promotes unstable slip behaviors. Furthermore, the results demonstrate that cyclic injection increases the critical stiffness of faults without an adequate release of pore pressure in each cycle. The time interval of the cyclic injection is prolonged as inertial flow generates higher pressure levels. This study highlights the importance of inertial flow in the stability analysis of critically stressed faults.

Elucidation of the “jumping and dropping” phenomena of piezoelectric vibrators in high-amplitude operations in the vicinity of their mechanical resonance frequencies

The peculiar events called the “jumping and dropping” phenomena of piezoelectric ceramic vibrators in high amplitude operation have been widely known. The phenomena, that occur solely in the vicinity of the mechanical resonance frequencies of the vibrators, are due to the emergence of hysteresis in the frequency domain with an abrupt increase and decrease of the vibratory amplitude. It has long been believed that the nonlinearity of the piezoelectric materials is the dominant cause of the unstable vibratory behaviors. Nevertheless, the authors have found that they can be attributed to the local piezoelectric polarization reversals due to the electric field concentration caused by its conspicuous distortion inside the vibrators in mechanical resonance. This hypothesis has been examined by numerical and experimental investigations for hard-type piezoelectric ceramic disks vibrating in axisymmetric radially pulsating mode.

CFD modeling of a mini-pilot scale CO2 hydrogenation to hydrocarbons reactor using both direct and indirect pathway-based kinetic model

Both indirect CO2 hydrogenation (reverse water gas shift (RWGS) followed by CO-based Fischer-Tropsch synthesis (FTS)) and direct CO2-based FTS were considered for CO2 hydrogenation, and a kinetic model for the chain-length distribution of hydrocarbon products was developed. For independent estimation, the kinetic parameters were estimated by fitting the experimental data using powder catalysts under various conditions, mainly including CO/CO2 ratios. The contribution of indirect CO2 hydrogenation (RWGS followed by CO-FTS) was more favorable than that of direct CO2-FTS, and CO2 conversion and product selectivity were significantly dependent on the temperature and hydrogen fraction. The effectiveness factor was estimated for the pellet-type catalysts, and values less than one validated the existence of mass-transfer resistance. Computational fluid dynamics (CFD) modeling was used to simulate the three-dimensional thermal behaviors of a mini-pilot-scale reactor with a substantially large diameter loaded with a pellet-type catalyst and inert materials. Both a low catalyst loading in the early stage of the reactor and the use of an additional inner cooling tube showed a stable temperature profile, with the peak temperature maintained below 350 °C (the critical temperature to prevent the thermal decomposition of chemicals) and fast heating of cold feed in the early stage. The CFD results with no inner tube showed thermal runaway in the second reactor, and the simulation with arbitrarily reduced heat of the reaction (70 % of the actual value) resulted in a peak temperature higher than 410 °C. Further quantitative analysis indicated that the no-inner-tube case's reduced heat transfer area per unit volume was responsible for its thermally unstable behavior.

The change of the absorptance at the transition from partial- to full-penetration laser welding

Full-penetration laser welding processes are necessarily associated with significant changes of the geometrical properties of the keyhole at the beginning of the process when the keyhole expands all the way through the workpiece and finally pierces the bottom of the sheet. The impact that this transition has on the absorptance was investigated by means of X-ray imaging to determine the geometry of the keyhole and subsequent raytracing to calculate the distribution of the absorbed irradiance. The results show a significant drop of the overall absorptance when the bottom of the capillary opens through the rear side of the workpiece which in practice is noticed by an unstable behavior of the keyhole. Since the drop of the absorptance is less pronounced for smaller diameters of the keyhole, one may recommend the application of laser beams with small diameters at least during the initial phase until the keyhole is fully developed and reliably reaches through the bottom surface of the welded sheet.

Rank is all you need: development and analysis of robust causal networks

Financial networks can be constructed to model the intertemporal price dependencies within an asset market, giving rise to a causal network. These networks are traditionally inferred through multivariate predictive modelling. However, the application of these techniques to financial data is highly challenged. The interplay of social and economic factors produces unstable price behaviour that violates most conventional modelling assumptions, limiting the informational content of networks derived from standard inference tools. Despite these limitations, it remains unclear whether the improved accuracy of robustly estimated networks translates into qualitatively unique insight. This study provides an extended analysis of our recently introduced Rank-Vector-Autoregression model, demonstrating its capacity to identify properties that are undetected with standard methodology. We initially validate the superior accuracy of Rank-VAR through a simulation study on processes that contain adversarial abnormalities. When applied to a dataset of 261 cryptocurrencies, our rank network uniquely displays capitalisation-dependent hierarchical ordering, with outgoing influence being positively, and incoming influence negatively correlated to total coin valuation. Applying our method to the squared deviations verifies that even under robust estimation, volatility networks display fundamentally differing dynamics to raw returns, with more connections, clustering, and causal cycles. Our results demonstrate the use of Rank-VAR to identify and verify unique properties in the causal structures of cryptocurrency markets.

Resilient adaptive event-triggered containment control of nonlinear multi-agent system under concurrent DoS attacks and disturbances

This paper presents a secure containment control problem of nonlinear Multi-Agent Systems (MASs) under aperiodic Denial of Service (DoS) attacks and external disturbances simultaneously. A novel adaptive neural network (NN)-based event-triggered control is considered that uses the nonlinear estimator to predict the state of other agents. Since data access is denied during DoS attacks, the overall system switches between two modes of stable and unstable containment behaviours. Therefore, the maximum of attack duration and frequency is determined such that the overall system evolution leads to containment convergence in the presence of DoS attacks. We proposed an adaptive NN-based distributed disturbance observer to estimate external disturbances in a nonlinear system's dynamics. The state estimator predicts neighbouring agents' states, and each agent's input and event times are determined without monitoring other agents. The directed graph topology is used to determine data exchange among agents instead of an undirected graph that reduces implementation conditions. Zeno-free behaviour is also proved by analysis of the system. Eventually, the numerical simulation of the proposed approach is shown. Abbreviations: DoS attacks, Containment control of multi-agent system