Phenomenological Model Research Articles

Numerical Study of Neutral and Charged Microgel Suspensions: From Single-Particle to Collective Behavior

We perform extensive molecular dynamics simulations of an ensemble of realistic microgel particles in swollen conditions in a wide range of packing fractions ζ. We compare neutral and charged microgels, where we consider charge distribution adherent to experimental conditions. Through a detailed analysis of single-particle behavior, we are able to identify the different regimes occurring upon increasing concentration: from shrinking to deformation and interpenetration, always connecting our findings with available experimental observations. We then link these single-particle features with the collective behavior of the suspension, finding evidence of a structural reentrance that has no counterpart in the dynamics. Hence, while the maximum of the radial distribution function displays a nonmonotonic behavior with increasing ζ, the dynamics, quantified by the microgels’ mean-squared displacement, always slows down. This behavior, at odds with the simple Hertzian model, can be described by a phenomenological multi-Hertzian model, which takes into account the enhanced internal stiffness of the core. However, also this model fails when deformation enters into play, whereby more realistic many-body models are required. Thanks to our analysis, we are able to unveil the key physical mechanisms, shrinking and deformation, giving rise to the structural reentrance that holds up to very large packing fractions. We further identify key similarities and differences between neutral and charged microgels, for which we detect at high enough ζ the fusion of charged shells, previously invoked to explain key experimental findings, and responsible for the structural reentrance. Overall, our study establishes a powerful framework to uncover the physics of microgel suspensions, paving the way to tackle different regimes, e.g., high temperature, and internal architectures, such as for hollow and ultralow-cross-linked microgels, where experimental evidence is still limited. Published by the American Physical Society 2024

Machine learning-based constitutive modelling for material non-linearity: A review

Machine learning (ML) models are widely used across numerous scientific and engineering disciplines due to their exceptional performance, flexibility, prediction quality, and ability to handle highly complex problems if appropriate data are available. One example of such areas which has attracted a lot of attentions in the last couple of years is the integration of data-driven approaches in material modeling. There has been several successful researches in implementing ML-based constitutive models instead of classical phenomenological models for various materials, particularly those with non-linear mechanical behaviors. This review paper aims to systematically investigate the literature on ML-based constitutive models for materials and classify these models based on their suitability for material non-linearity including Non-linear elasticity (hyperelasticity), plasticity, visco-elasticity, and visco-plasticity. Furthermore, we also reviewed and compared the ML-based approaches that have been applied for architectured materials as these groups of materials are designed to represent specific material behaviors that might not exist in classical and conventional material categories. The other goal of this review paper is to provide initial steps in understanding of various ML-based approaches for material modeling, including artificial neural networks (ANN), Gaussian processes, random forests (RF), generated adversarial networks (GANs), support vector machines (SVM), different regression models and physics-informed neural networks (PINN). This paper also outlines different data collection methods, types of data, data processing approaches, the theoretical background of the ML models, advantage and limitations of various models, and potential future research directions. This comprehensive review will provide researchers with the knowledge necessary to develop high-fidelity, robust, adaptable, flexible, and accurate data-driven constitutive models for advanced materials.

Simple approximate model for pion masses

Closed expressions for the masses of the charged and neutral pions are derived based on a simple approximate model for electrostatic screening when a proton is located adjacent to a neutron. The estimated mass values agree to a few percent of experimentally measured values. The electric dipole moment of the deuteron is also estimated based on the phenomenological toy model used here.

Time-reversal invariance violation effect in $dd$ scattering

Abstract 

A formalism has been developed for calculating the signal of violation of
time-reversal invariance, provided that
space-reflection ({\color{blue} parity}) invariance
is conserved during the scattering of tensor-polarized deuterons on vector-polarized
 ones. The formalism is based on the Glauber theory with the full consideration of spin dependence of $NN$ elastic scattering amplitudes and spin structure
of colliding deuterons.
The numerical calculations have been carried out in the range of laboratory proton energies of $T_p=0.1$--$1.2$ GeV using the SAID database
for spin amplitudes and in the energy region of the SPD NICA experiment corresponding
to the invariant mass of the interacting nucleon pairs $\sqrt{s_{NN}}= 2.5$--$25$ GeV, using two phenomenological models of $pN$ elastic scattering.
It is found that only one type of the time-reversal non-invariant
{\color{blue} parity} conserving
 $NN$ interaction gives a non-zero contribution to the signal
 in question, that is important for isolating an unknown constant of this interaction from the corresponding data.

Study on non-radiative recombination centers in GaInP sub-cell of electron-irradiated triple junction solar cells based on photoluminescence measurements

The radiation damage induced by energetic particles on GaInP sub-cell in GaInP/GaAs/Ge triple junction solar cell (TJSC) has a significant impact on its performance. Thus, in this paper, we measured the photoluminescence (PL) spectra for GaInP sub-cell in TJSC irradiated by 11.5 MeV electron at temperature range 10 –300 K. By analyzing the variation of PL intensity with temperature, the thermal activation energy for nonradiative recombination center was determined as 8.43 meV, and its minority carrier capture cross-section is about 3.63 × 10−13 cm2. Further confirmed that the nonradiative recombination center in GaInP is H2 (EV + 0.55 eV) defect. Furthermore, by using multi-peaks curve-fittings to analyze PL spectra, the carrier recombination processes in electron irradiated GaInP sub-cell are determined, and a phenomenological model was given for explaining carrier recombination processes.

Weak rates in strongly coupled cold quark matter

The rates of flavor-changing weak processes are crucial in determining the conditions of beta equilibrium in neutron stars and mergers, influencing the damping of oscillations, the stability of rotating pulsars, and the emission of gravitational waves. We derive a formula for these rates at nonzero temperature, to leading order in the Fermi coupling and exact in the QCD coupling. Utilizing a simple phenomenological holographic model dual to QCD, we study massless unpaired quark matter at high densities. We numerically compute the rate for small deviations from beta equilibrium and derive an analytic approximation for small temperatures. Our findings reveal that, compared to the perturbative result, the rate is suppressed by logarithmic factors of the temperature.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

On the Use of Medium-Term Forecast Data for the Baikal Rift Zone in Seismic-Hazard Assessments

The article presents the general results of medium- and long-term forecasting of earthquakes with K ≥ 13 (M ≥ 5.0) in the Baikal rift zone. These results have recently been obtained through the joint use of the geoinformation system for predicting the preshock stage of earthquake preparation and its associated two-stage phenomenological model. This model was created based on the analysis of seismological data on the preparation of the most dangerous earthquakes that occurred in the Baikal rift zone. It is consistent with the results obtained in studies of seismic regimes of ice shock preparation on the Baikal ice cover and in conducting full-scale experiments on fault sections with the aim of clarifying physical and mechanical conditions for the emergence of the sources of seismic-induced oscillations.The paper provides an example of practical use of results obtained for earthquake forecasting, as well as the ways of refining seismic hazard assessments relative to the infrastructure in the city of Angarsk 100 km away from the seismically hazardous Main Sayan Fault (MSF), whose zone reveals a “locked” segment with a seismic gap during the seismic regime analysis. In accordance with its linear dimensioning which represents a length of 60 km, two L/M equations served as a basis for potential energy calculations whose maximum values correspond to Mmax 7.1 and 7.8. It is shown that the use of the obtained earthquake-forecast results assists in refining the level of seismic hazard for the nearest time intervals of expected earthquakes with different Mmax values. Consideration is being given to the example of assessing the current seismic hazard using a medium-term forecast for the infrastructure of the city of Angarsk for the probability of seismic shaking from the southeastern section of the MSF zone for the next 10 and 50 years. The comparison with the OSR-16 map showed that the calculations carried out indicate a relatively lower level of seismic hazard for the city of Angarsk for 10- and 50-year expectation periods.

Evaluating the Effects of Globalization on Work: Building upon Awareness, Learning, and Knowledge

Phenomenological processes influence how one evaluates globalization, and consequently one’s attitudes toward it. The awareness of, learning about, and knowledge of complex problems have direct effects on how problems are evaluated and managed, even in contexts predominantly based on material interests. We analyze the perceived effects globalization has on work by looking at the attitudes and evaluations of labor union representatives in Finland (N = 334). We propose a tripartite phenomenological model of awareness, learning, and knowledge, and apply it in a crosssectional OLS regression model using 20 explanatory variables. Findings show significant associations for two variables, including employers and supervisors as information sources and effects of foreign branches. The model moderately explains the evaluations of the effects of globalization, despite the sample consisting of social classes with differing life-worlds. This suggests that union representatives’ attitudes toward globalization seem partly contingent on their individual, phenomenological attachment to global circumstances.

Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations—II: Illustrative Application to Heat and Energy Transfer in the Nordheim–Fuchs Phenomenological Model for Reactor Safety

This work presents an illustrative application of the newly developed “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)” methodology to determine most efficiently the exact expressions of the first- and second-order sensitivities of NODE decoder responses to the neural net’s underlying parameters (weights and initial conditions). The application of the 2nd-FASAM-NODE methodology will be illustrated using the Nordheim–Fuchs phenomenological model for reactor safety, which describes a short-time self-limiting power transient in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted. The representative model responses that will be analyzed in this work include the model’s time-dependent total energy released, neutron flux, temperature and thermal conductivity. The 2nd-FASAM-NODE methodology yields the exact expressions of the first-order sensitivities of these decoder responses with respect to the underlying uncertain model parameters and initial conditions, requiring just a single large-scale computation per response. Furthermore, the 2nd-FASAM-NODE methodology yields the exact expressions of the second-order sensitivities of a model response requiring as few large-scale computations as there are features/functions of model parameters, thereby demonstrating its unsurpassed efficiency for performing sensitivity analysis of NODE nets.

UTILIZATION OF ALUMINA AND STEEL INDUSTRIAL WASTE FOR THE DEVELOPMENT OF CONTROLLED LOW STRENGTH MATERIALS

The study aimed at incorporating Red Mud, a byproduct of bauxite ore extraction, and Ground Granulated Blast Furnace Slag (GGBS), a byproduct of the steel industry, into Controlled Low Strength Materials (CLSM). These materials were combined with M Sand and cement to develop a sustainable and cost-effective construction material. Utilizing Red Mud and GGBS addresses waste disposal challenges while enhancing CLSM properties, making it a more environmentally friendly option. By converting industrial byproducts into valuable construction components, the research contributes to sustainable development and the circular economy. The research methodology involves creating five different dry mix combinations, varying the proportions of Red Mud, GGBS, M Sand, and cement. Each combination was tested with four different water contents to evaluate their engineering and dynamic properties. This methodical approach was focused to identify the optimal mix that balances strength and workability. By analyzing these properties, the study provided a valuable insight into the behavior of CLSM under various conditions, contributing to the advancement of more efficient construction materials. The systematic testing ensured that the developed CLSM mixes met the necessary performance criteria for practical applications. The primary objective of the study was to develop a phenomenological model based on the obtained strength and flow values. This model will serve as a predictive tool for future research, aiding in the design of CLSM mixtures with desired characteristics. By demonstrating the feasibility of using Red Mud in CLSM, the research underscores its potential to offer cost-effective and sustainable solutions for various engineering applications. This innovative approach not only promotes waste utilization but also opens new possibilities for improving construction practices. The developed model will help engineers and researchers predict the performance of new CLSM formulations, streamlining the development process for future projects.

Analyzing the Brain’s Dynamic Response to Targeted Stimulation using Generative Modeling

Abstract Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain-stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss: (i) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics; and (ii) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

On the hysteretic bending behavior of slack metallic cables

Short and slack stranded metallic cables are encountered in several technical applications to connect different structures or different parts of the same structural system. A couple of relevant examples are provided by flexible-bus conductors used in high-voltage electrical substations or passive damping devices installed on overhead electrical lines, such as Bretelle dampers. Bending behavior of metallic cables is nonlinear and non-holonomic due to the onset and propagation of relative sliding phenomena between the wires. While taut suspended cables have been widely studied in the literature under the classic assumptions of small sag and perfect flexibility, whenever dealing with short and slack cables, the aforementioned hypotheses typically cease to be valid, and the bending stiffness contribution plays a significative role in the determination of the overall structural response. In the present paper, two different modeling strategies for the description of the bending behavior of short and slack stranded cables are presented. The first (discrete bending model) describes the cable as a composite structural element made of wires, which are individually modeled as curved elastic thin rods interacting through normal and tangential contact forces. The second (phenomenological bending model), instead, relies on a description of the cable bending process based on an application of the well-known Bouc-Wen hysteresis model. Applications of the proposed models are presented both at the cross-sectional and at the structural levels. The systematic comparison between theoretical and experimental results allows to assess the performance of the two proposed bending models when used with “nominal” values of the model parameters, which are estimated only on the base of the knowledge of the material and geometric properties of the strand. Moreover, a back-analysis of the model parameters is carried out to get an insight on internal mechanical parameters of the cable and on the overall cross-sectional bending response (e.g. bending stiffness values).

A phenomenological discrete model for cardiac tissue mechanics

A phenomenological discrete model for cardiac tissue mechanics

Thermodynamics-based data-driven combustion modelling for modern spark-ignition engines

Combustion modelling is complicated, computationally expensive, and crucial for the development of modern spark-ignition (SI) engines. This study introduces a novel data-driven approach to improve the predictability of phenomenological SI engine models. First, a physics-based model is used to generate Mass Fraction Burned (MFB) profiles for 1,258 precisely controlled knock-limited combustion experiments. To predict these MFB profiles based on the operating conditions, Artificial Neural Networks (ANN), Multiple Output Support Vector Regression (MOSVR), and Multivariate Gaussian Process (MGP) are then applied. Among these, MGP demonstrates superior performance due to the Gaussian-like distribution of the outputs. Further sensitivity analysis using MGP identifies critical inputs that are not engine specific to develop a thermodynamics-based data-driven model. The model demonstrates high accuracy, uses normalised inputs that are independent of engine geometry, and consistently performs well with small datasets. When applied to a different but similarly sized engine, the model accurately predicts the knock-limited spark timing and captures the MFB profile relatively well, showing strong generalisability. This study not only improves the predictability of engine combustion simulations but also establishes a valuable dataset for further development of data-driven models in different engines.

A self-consistent design method of the autothermal dual circulating fluidized bed reactor for in-situ gasification chemical looping combustion of coal

This paper establishes a design method for the autothermal dual circulating fluidized bed reactor (DCFBR) of the coal-fueled in-situ gasification chemical looping combustion (iG-CLC) process. This method, grounded in a self-consistent phenomenological model, comprehensively elucidates the factors of mass and energy conservation, fuel and oxygen carrier (OC) reaction processes, and fluidization characteristics within the reactor. It is particularly suitable for the rapid screening of numerous potential designs, obtaining detailed design parameters, and studying their interrelationships. Utilizing this method, the regulation patterns of dual-bed interactive transport-reaction phenomena within a 5 MWthiG-CLC DCFBR are studied. Firstly, the effects of key design parameters on the OC circulation rate and bed inventory are analyzed. Following this, the impacts of circulation rate on the interactive heat and mass transfer between two beds are examined, delineating a reasonable range for the control of autothermal CLC process (circulation rate of 50–90 kg/m2s, temperature difference of 30–90 °C, OC conversion of 0.2–0.35, and oxygen-fuel ratio of 3–6). Subsequently, this paper investigates the influences of main design parameters on the performance metrics, such as gas/solid fuel conversion, operational costs, and operational benefits. It is found that the carbon stripper significantly enhances the carbon capture efficiency of the device, provided that its separation efficiency is maintained above 70–95 %. Sensitivity analysis is employed to study the response patterns of performance metrics to changes in input parameters. Ultimately, based on these analyses, a design scheme for the 5 MWth reactor is determined, with a detailed examination of the pressure balance state of the reactor under the design conditions, and the balanced material and energy flows at the reactor inlets and outlets.

Advanced phenomenological models guided heat treating processes for LPBF Ti-6Al-4V alloy

Ti-6Al-4V (Ti64) alloy produced by laser powder bed fusion (LPBF) technique consists of an α′-martensite phase, which is usually brittle. Hence, heat treatments are required to promote the α՛→α+β and/or α→β transformations for microstructural optimization to achieve balances between strength and ductility. To guide the selection of heat treatment parameters for the design of LPBF Ti64 microstructures exhibiting optimized strength and ductility, this work explores the suitability of a Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Lifshitz-Slyozov-Wagner (LSW) models. The work showed that the JMAK model can effectively model the heat treatment processes to predict volume fraction of constituent phases in LPBF Ti64. The transformation kinetics was predicted by an extended non-isothermal JMAK model based on differential scanning calorimetry (DSC) experimental data recorded during continuous heating. Moreover, hardness, as a mechanical property index, was measured for a set of specimens. The hardness initially decreased, then remained constant, and finally slightly increased with the increase in β phase fraction and underlying evolution of microstructure. When the β phase fraction was small < 3%, hardness decreased with the continuous coarsening of α lath during heat treatment process. The measured hardness and α lath width for the specimens followed the Hall-Petch relationship. Moreover, the α lath width and heat treatment time at a given temperature were found to undergo the LSW model. Hardness became independent on the β phase fraction of 3-8%, owing to the competition between α lath coarsening and solution strengthening. The hardness slightly increased when the β phase fraction was greater than 8%, because solid solution strengthening of the α phase played a dominant role over the coarsening of α lath width. The experimentally verified JMAK and LSW models are therefore discussed as effective tools to guide the selection of heat treatment parameters for designing the microstructure and hardness/strength of LPBF Ti64 alloy.

Compartmentalized pooling generates orientation selectivity in wide-field amacrine cells

Orientation is one of the most salient features in visual scenes. Neurons at multiple levels of the visual system detect orientation, but in many cases, the underlying biophysical mechanisms remain unresolved. Here, we studied mechanisms for orientation detection at the earliest stage in the visual system, in B/K wide-field amacrine cells (B/K WACs), a group of giant, nonspiking interneurons in the mouse retina that coexpress Bhlhe22 (B) and Kappa Opioid Receptor (K). B/K WACs exhibit orientation-tuned calcium signals along their long, straight, unbranching dendrites, which contain both synaptic inputs and outputs. Simultaneous dendritic calcium imaging and somatic voltage recordings reveal that individual B/K dendrites are electrotonically isolated, exhibiting a spatially confined yet extended receptive field along the dendrite, which we term "compartmentalized pooling." Further, the receptive field of a B/K WAC dendrite exhibits center-surround antagonism. Phenomenological receptive field models demonstrate that compartmentalized pooling generates orientation selectivity, and center-surround antagonism shapes band-pass spatial frequency tuning. At the microcircuit level, B/K WACs receive excitation driven by one contrast polarity (e.g., "ON") and glycinergic inhibition driven by the opposite polarity (e.g., "OFF"). However, this "crossover" inhibition is not essential for generating orientation selectivity. A minimal biophysical model reproduced compartmentalized pooling from feedforward excitatory inputs combined with a substantial increase in the specific membrane resistance between somatic and dendritic compartments. Collectively, our results reveal the biophysical mechanism for generating orientation selectivity in dendrites of B/K WACs, enriching our understanding of the diverse strategies employed throughout the visual system to detect orientation.

Strong-field magnetohydrodynamics for neutron stars

We present a formulation of magnetohydrodynamics which can be used to describe the evolution of strong magnetic fields in neutron star interiors. Our approach is based on viewing magnetohydrodynamics as a theory with a one-form global symmetry and developing an effective field theory for the hydrodynamic modes associated with this symmetry. In the regime where the local velocity and temperature variations can be neglected, we derive the most general constitutive relation consistent with symmetry constraints for the electric field in the presence of a strong magnetic field. This constitutive relation not only reproduces the phenomena of Ohmic decay, ambipolar diffusion, and Hall drift derived in a phenomenological model by Goldreich and Reisenegger, but also reveals terms in the evolution of the magnetic field which cannot easily be seen from such microscopic models. This formulation gives predictions for diffusion behaviors of small perturbations around a constant background magnetic field, and for the two-point correlation functions among various components of the electric and magnetic fields. Published by the American Physical Society 2024

Multiscale modelling of bioprocess dynamics and cellular growth

BackgroundFermentation processes are essential for the production of small molecules, heterologous proteins and other commercially important products. Traditional bioprocess optimisation relies on phenomenological models that focus on macroscale variables like biomass growth and protein yield. However, these models often fail to consider the crucial link between macroscale dynamics and the intracellular activities that drive metabolism and proteins synthesis.ResultsWe introduce a multiscale model that not only captures batch bioreactor dynamics but also incorporates a coarse-grained approach to key intracellular processes such as gene expression, ribosome allocation and growth. Our model accurately fits biomass and substrate data across various Escherichia coli strains, effectively predicts acetate dynamics and evaluates the expression of heterologous proteins. By integrating construct-specific parameters like promoter strength and ribosomal binding sites, our model reveals several key interdependencies between gene expression parameters and outputs such as protein yield and acetate secretion.ConclusionsThis study presents a computational model that, with proper parameterisation, allows for the in silico analysis of genetic constructs. The focus is on combinations of ribosomal binding site (RBS) strength and promoters, assessing their impact on production. In this way, the ability to predict bioreactor dynamics from these genetic constructs can pave the way for more efficient design and optimisation of microbial fermentation processes, enhancing the production of valuable bioproducts.