Multi-physics Approach Research Articles

Ultra-Low Power All-Optically Tuned Hybrid Graphene Ultra Silicon-Rich Nitride Ring Resonator-Based Add-Drop Filter for DWDM Systems

This research work conducted a design and simulation of an ultra-low power all-optically tuned nonlinear ring resonator-based add-drop filter. The purpose of this study is to investigate a CMOS-compatible nonlinear material system for an optical filter with temperature resilience, polarization insensitivity, and fast and energy-efficient tunability. The all-optical tunability was achieved using an optical pump that photo-excites the high nonlinear Kerr effect in the device material system. A three-dimensional multiphysics approach was used, combining the electromagnetics and thermo-structural effects in the filter. Hybrid graphene on an ultra-rich silicon nitride ring resonator-based filter enabled the realization of an ultra-high tuning efficiency (0.275 nm/mW for TE mode and 0.253 nm/mW for TM mode) on a range of 1.55 nm and thermal stability of 0.11 pm/K. This work contributed to the existing literature by proposing (1) the integration of a high Kerr effect layer on a low loss, high index contrast, and two-photon absorption-free core material with an athermal cladding material system and (2) the use of a cross-section shape insensitive to polarization. Moreover, the tuning mechanism contributed to the realization of an all-optical on-chip integrable filter for Dense Wavelength Division Multiplexing systems in the less occupied L band.

Application of a multicomponent model of convectional reaction-diffusion to description of glucose gradients in a neurovascular unit.

A supply of glucose to a nervous tissue is fulfilled by a cerebrovascular network, and further diffusion is known to occur at both an arteriolar and a microvascular level. Despite a direct relation, a blood flow dynamic and reaction-diffusion of metabolites are usually considered separately in the mathematical models. In the present study they are coupled in a multiphysical approach which allows to evaluate the effects of capillary blood flow changes on near-vessels nutrient concentration gradients evidently. Cerebral blood flow (CBF) was described by the non-steady-state Navier-Stokes equations for a non-Newtonian fluid whose constitutive law is given by the Carreau model. A three-level organization of blood–brain barrier (BBB) is modelled by the flux dysconnectivity functions including densities and kinetic properties of glucose transporters. The velocity of a fluid flow in brain extracellular space (ECS) was estimated using Darcy’s law. The equations of reaction-diffusion with convection based on a generated flow field for continues and porous media were used to describe spatial-time gradients of glucose in the capillary lumen and brain parenchyma of a neurovascular unit (NVU), respectively. Changes in CBF were directly simulated using smoothing step-like functions altering the difference of intracapillary pressure in time. The changes of CBF cover both the decrease (on 70%) and the increase (on 50%) in a capillary flow velocity. Analyzing the dynamics of glucose gradients, it was shown that a rapid decrease of a capillary blood flow yields an enhanced level of glucose in a near-capillary nervous tissue if the contacts between astrocytes end-feet are not tight. Under the increased CBF velocities the amplitude of glucose concentration gradients is always enhanced. The introduced approach can be used for estimation of blood flow changes influence not only on glucose but also on other nutrients concentration gradients and for the modelling of distributions of their concentrations near blood vessels in other tissues as well.

Prediction of moisture diffusion and failure in glass/steel adhesive joints

Glass/steel adhesive joints are being used increasingly in the construction industry as they offer significant structural advantages over conventional mechanical fastener approaches. However, adhesive joints are also known to be sensitive to moisture diffusion into the bondline, which reduces the interfacial bonding strength for hybrid glass/steel substrates. The effect of moisture on the performance degradation of glass/steel adhesive joints has been successfully predicted assuming adhesive property degradation but requires experimental determination of the affected moisture ingress zone. This study utilizes a multi-physics numerical approach implemented via the commercial finite element code Abaqus 2020, which firstly simulates moisture ingress into the adhesive/glass interface and subsequently couples the diffusion effects with a cohesive zone modelling approach for damage initiation and propagation. The numerical predictions are calibrated against experimental data on glass/steel Double Cantilever Beam (DCB) specimens, which are bonded with a ductile methacrylate adhesive (Araldite 2047–1). The modelling approach is then validated against the experimental response of large double lap shear joints of a significantly different bondline geometry. It is demonstrated that the numerical model successfully predicts the critical exposure time for partial to complete joint degradation enabling the development of engineering guidelines for life-time prediction of various joint geometries.

Multiphysical Analytical Modeling and Design of A Magnetically Steerable Robotic Catheter for Treatment of Peripheral Artery Disease.

This article presents a unique multiphysical analytical modeling framework and solution algorithm to provide an effective tool for design of magnetically steerable robotic catheters (MSRCs) experiencing external interaction loads. Particularly, in this study, we are interested in design and fabrication of a MSRC with flexural patterns for treatment of peripheral artery disease (PAD). Aside from the parameters involved in the magnetic actuation system and the external interaction loads acting on the MSRC, the considered flexural patterns have a critical role on the deformation behavior and steerability of the proposed MSRC. Therefore, to optimally design such MSRC, we utilized the proposed multiphysical modeling approach and thoroughly evaluated the influence of involved parameters on the performance of the MSRC via two simulations studies. We also conducted experimental studies in a free bending condition and in the presence of different external interaction loads on two custom-designed MSRCs to thoroughly evaluate the efficacy of the proposed multiphysical model and solution algorithm. Our analysis demonstrates the accuracy of the proposed approach and necessity of utilizing such models to optimally design a MSRC before fabrication procedure.

A Combined Multi-Physics Modelling and Machine Learning to Predict Electro-Thermal Failures of Cylindrical Li-Ion Batteries

Safety aspects of Li-ion batteries (LIBs) operation become increasingly important due to their integration into large-scale systems such as electric vehicles (EVs). Recent events involving many EVs explosions endangering consumers’ lives have shown that electro-thermal aspects of LIBs are far from being adequately managed. Thus, one of the most crucial goals of current research is to steer LIBs’ technology toward long-lasting and safe operation.In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in LIBs. Specifically, we use various machine learning (ML) techniques to learn and predict the likelihood of the TRA, using thermal images acquired from the multi-physics modeling of LIBs. The multi-physics modeling approach is comprised of a coupled thermal, electrochemical (P2D) model1, and degradation due to the solid electrolyte interface (SEI) formation/decomposition sub-models.2 The presented multi-physics model has been formulated as a three-dimensional model to facilitate efficient and accurate numerical simulations in different conditions. Under general working conditions, the cell is exposed to a continuous charge/discharge load, where heat is generated inside the cell due to several electrochemical and degradation processes. Thermal images of the battery surface are collected as sequences of video frames during the battery operation from multiple simulations to be used in ML. LIB’s geometrical and material properties are taken from the 2170 cell as used in the current Tesla vehicles (model S 2021, model 3 2021). Based upon the multi-physics modeling results, two deep learning techniques, i.e., Convolutional Neural Networks (CNN) and object detection using YOLO, are proposed to address the problem of predicting the TRA like-events.3–5 The multi-physics modeling results in the form of the temperature profiles of the battery surface as well as the SEI formation/decomposition rate at various charge/discharge cycles are validated against the prior literature experimental data. Two regimes with single and double hot spots in the cylindrical LIB are investigated for a potential TRA occurrence. The CNN predictions of the TRA-like event on unknown data set show an excellent accuracy reaching up to 90% for all the different CNN architectures used. The object detection technique is found to be very efficient in identifying the location of TRA initiation at various conditions. Thus, the developed combined multi-physics and ML modeling approach establishes a basis for 'on-the-fly' prediction of the TRA as well as a framework for extending machine learning methodologies to broad applications in electrochemistry.References J. Newman and W. Tiedemann, J. Electrochem. Soc., 140, 1–5 (1993).X. Fen, X. He, M. Ouyang, L. Wang, L. Lu, D. Ren and S. Santhanagopalan, J. Electrochem. Soc., 165, A3748–A3765 (2018).J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 779–788 (2016).K. He, X. Zhang, S. Ren, and J. Sun, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 770–778 (2016).K. Simonyan and A. Zisserman, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 1–14 (2015).

Fast Multi-Physics Simulation of Microwave Filters via Deep Hybrid Neural Network

One fundamental difficulty in multiphysics numerical simulation is the complex interactions between different physics domains leading to plenty of computational costs. Although neural networks have recently been introduced in multiphysics simulations, the modeling complexity and the enormous amount of training data required may still pose significant challenges to researchers. In this work, we introduce a low-cost, electromagnetic-centric, multiphysics modeling approach to simulate microwave filters. With ground-truth datasets being generated from the finite element method, a novel deep hybrid neural network (DHNN) model structure is introduced, which uses the sigmoid and the ReLU functions as activators to mimic the diversity of biological neurons. A new, more feasible training algorithm is proposed for the efficient development of the DHNN model. The algorithm adopts the design-of-experiment (DOE) sampling technique and is specifically designed for the simulation of multiphysics responses. The strong approximation ability of the DHNN can lead to high-accuracy modeling with fewer training data and less resource consumption. Another advantage of this approach is that the modeling process is more concise and easier to apply compared with other modeling technologies. Numerical examples show that the DHNN can achieve higher accurate results with much less training data compared to traditional ANNs. The advantages of the proposed method in computational efficiency are more pronounced, especially when the amount of input data increases.

MIRA: a multi-physics approach to designing a fusion power plant

Fusion systems codes (SCs) are deployed to produce the baseline of the European fusion power reactor (DEMO) within its conceptual design. A DEMO baseline is mostly defined by a radial/vertical reactor sketch and major reactor parameters, such as fusion and net electric power, magnetic fields, and plasma burn time. A baseline shall also meet a set of prescribed reactor requirements, constraints, and architectural features. According to the conceptual design workflow implemented within the EU-DEMO programme, the output from the SC is transferred to the detailed physics and engineering design codes. Presently-available fusion SCs rely on rather basic physics and engineering models (mostly at zero or one-dimensional level). The design codes, instead, are very detailed but run on much longer computing times. To fill the gap between systems and design codes, the multi-fidelity systems/design tool modular integrated reactor analysis (MIRA)—has been recently developed. MIRA incorporates the physics and the engineering insights of the utmost domains of tokamak reactors and relies on a higher spatial resolution, spanning from 1D up to 3D modelling frames. The MIRA approach has been applied to the DEMO 2017 baseline, generated by the EU reference SC PROCESS and used as input to MIRA. In the paper, the architectural and mathematical insights of the MIRA package are described, along with an EU-DEMO 2017 baseline analysis.

Influence of thermal deformations on sound absorption of three-dimensional printed metamaterials

Acoustic metamaterials (AMMs) are designed with complex geometrical shapes to obtain unconventional sound-absorbing performances. As additive manufacturing is particularly suited to print complex structures in a more straightforward and controllable way, AMMs often exploit three-dimensional (3-D) printing techniques. However, when exposed to different temperature conditions, such structures can be affected by geometrical deformations, especially when they are polymer-based. This can cause a mismatch between the experimental data and the expected theoretical performance; therefore, it is important to take thermal effects into account. The present paper investigates the influence of thermal deformations on the sound absorption of three geometries: a coplanar spiral tube, a system with double coiled resonators, and a neck-embedded resonator. Measurements were performed on each 3-D printed specimen in the impedance tube after the samples had been placed in a climate chamber to modify the temperature settings (T = 10-50 °C). Numerical models, validated on the measurements, were employed to quantify the geometrical deformation of AMM structures through a multiphysics approach, highlighting the effects of thermal stress on the acoustic behavior. The main outcomes prove that the frequency shifts of sound absorption peaks depend on temperature configurations and follow exponential regressions, in accordance with previous literature on polymeric materials.

Design of actively-controlled oil lubrication to reduce rotor-bearing-foundation coupled vibrations - theory & experiment

This paper gives theoretical and experimental contributions to the field of tribotronics, linking i) tribology of fluid film bearings, ii) flexible rotor and flexible foundation dynamics, iii) sensing systems, iv) fluid power and hydraulic actuator dynamics, and v) control theory in a holistic way. Based on a multi-physical modelling approach, a mathematical model (digital twin) is derived coupling the subsystems from i) till v). The digital twin is employed to design and optimize the feedback control system of an actively-controlled tilting pad journal bearing with the aim of attenuating vibration levels of the rotor-bearing-foundation system. The feedback control system is experimentally implemented and tested on a test rig. Significant reduction of the rotor-bearing-foundation vibrations is achieved in the frequency range of 20 until 250 Hz. Very good agreement between theory and experiment is achieved at the low rotor angular velocity used. The theoretical and experimental outcomes of the paper suggest that vi) the thermal domain needs to be included in the multi-physical modelling approach to accurately represent the bearing properties under thermoelastohydrodynamic conditions and expand the theoretical and experimental approaches to higher angular velocities.

3D design and analysis of an electro-optically tunable athermal and polarization-insensitive ring resonator-based add-drop filter for DWDM systems

Dense Wavelength Division Multiplexing system-based optical networks are currently the most appropriate solutions for all-optical networks that efficiently utilize the large bandwidth offered by optical fiber networks. Tunable ring resonator-based filters are highly attractive owing to their bandwidth and channel tunability, high spectral selectivity, low losses, low power consumption, and compactness; thus they are very good candidates for optical integrated circuits at a very large scale. We used titanium oxide and silicon oxide as the upper-cladding and under-cladding materials, respectively, around a silicon-rich nitride core to design an electro-optically tunable, polarization-insensitive, and thermally resilient sixth-order add-drop optical filter in the L-band (1565 nm-1625 nm). A thin film of lithium niobate added on the top of silicon oxide was used to enhance the tunability of the filter. A 3D multiphysics approach considering thermo-optic, and stress-optical effects while minimizing the polarization rotation has been adopted to solve the electromagnetic problem in a filter that can accommodate arbitrary Transverse Electric and Transverse Magnetic polarized optical signals. The device has a bandwidth of 50 GHz (linewidth of 0.4 nm) at a resonant wavelength of 1575.4 nm, an extended FSR of 2.512 THz, and losses of 0.82 dB in the bandpass. The filter is ultra-compact with a footprint of 15μm×160μm. We achieved a high-quality factor of 3250, a tunability efficiency of 8.95 pm/V, and a finesse of 31. To the best of our knowledge, it is the first time a complementary metal-oxide-semiconductor-compatible, electro-optically tunable, athermal, polarization-insensitive high order add-drop filter in the L-band with a top-flat response in the passband, and with an extended FSR has been designed for Dense Wavelength Division Multiplexing systems.

Morphology predictions in molded parts: a multiphysics approach

Injection molding of polymer parts at a micro-scale is successfully applied in the fabrication of electronics and biomedical devices where high geometrical accuracy is required. Microinjection molding is more challenging than conventional injection molding due to the necessity to account for the presence of air in the cavity, which slows down the process, and a strong flow field that may induce premature solidification of semi-crystalline polymers. The process modeling and simulation are crucial steps toward predicting all the final parts' properties. For this reason, a multiphysics approach was used to model microinjection molding under different mold cycle temperatures. A well-characterized polypropylene was selected for this purpose. A model for tracking the polymer-air interface during the filling was adopted to account for air's effect in the micro-cavity. Additionally, a model accounting for the effect of crystallization on viscosity was implemented. Models describing the evolution of morphology into fibrils were previously proposed in steady-state conditions. In this work, a model for describing the crystallization into fibrils was proposed and adapted for the first time to the transient conditions of microinjection molding. The aim was the prediction of the final morphology developed during the process. The morphology evolution toward fibrillar structure is consistent with those observed experimentally; in particular, the fibrillar layer thickness decreased with the increase of the mold temperature.

Assessment of microwave susceptors for optimum temperature rise using parametric numerical simulation

Microwave hybrid heating or indirect microwave heating has been an important development in the processing of metals using microwave energy. In this method of heating, a “susceptor” is used to absorb and convert microwave energy into thermal energy which is utilized for materials processing. Few of these processing applications are sintering, joining, cladding, casting, and composite development. However, the precise selection of a susceptor for a given process is challenging and requires a thorough understanding of the microwave heating phenomenon. In addition, predicting temperature rise in a microwave cavity is a complex task. Numerical simulation can serve to solve these two significant problems. In this work, parametric simulation of microwave heating of three major susceptor materials; silicon carbide, alumina, and coal has been carried out using a multiphysics approach. Initially, a model was developed to validate the simulation procedure. The results were in good agreement with the available data in published literature. Thereafter, the effect of microwave power, position of susceptor block, and time of irradiation on temperature rise in these materials were investigated. On comparing these susceptor blocks after altering the parametric combinations, coal exhibited the formation of hot spots and uneven temperature field with a maximum temperature of 901°C. On the contrary, the temperature attained in silicon carbide (529°C) and alumina (616°C) were lower but distributed in a relatively uniform field across the block. Regression models have also been built to correlate maximum temperature achieved with the parametric variation of parameters. It has been found that microwave power and the time of microwave irradiation have a significant coactive effect on the maximum temperature reached by the material. While the rise in the position of the susceptor block from the base leads to an adverse effect on the temperature rise. The study demonstrates that indirect microwave heating can be harnessed using the proper selection of susceptors as per the temperature requirement of the process. The regression models can be used for the accurate prediction of temperature attainment for given power input, position, and time.

Assessing flooding impact to riverine bridges: an integrated analysis

Abstract. Flood events are the most frequent cause of damage to infrastructure compared to any other natural hazard, and global changes (climate, socioeconomic, technological) are likely to increase this damage. Transportation infrastructure systems are responsible for moving people, goods and services, and ensuring connection within and among urban areas. A failed link in these systems can impact the community by threatening evacuation capability, recovery operations and the overall economy. Bridges are critical links in the wider urban system since they are associated with little redundancy and a high (re)construction cost. Riverine bridges are particularly prone to failure during flood events; in fact, the risks to bridges from high river flows and erosion have been recognized as crucial at global level. The interaction of flow, structure and network is complex, and not fully understood. This study aims to establish a rigorous, multiphysics modeling approach for the assessment of the hydrodynamic forces impacting inundated bridges, and the subsequent structural response, while understanding the consequences of such impact on the surrounding network. The objectives of this study are to model hydrodynamic forces as demand on the bridge structure, to advance a performance evaluation of the structure under the modeled loading, and to assess the overall impact at systemic level. The flood-prone city of Carlisle (UK) is used as a case study and a proof of concept. Implications of the hydrodynamic impact on the performance and functionality of the surrounding transport network are discussed. This research will help to fill the gap between current guidance for design and assessment of bridges within the overall transport system.

Towards electro-thermo-mechanical lifetime assessment for arbitrary power electronics

Power electronics are key-enablers of several industry trends, such as more efficient renewable energy harvesting, eco-friendly mobility and many more. With their uprising use and versatility the requirement for these packages is steadily increasing; thus leading to an evermore complex electro-thermo-mechanical loading situation. On the one side, external loads such as vibrations or weather extremes leading to challenging thermal loading situations are present. On the other side, internal loads generated by the active semiconductor within a power package result in high temperature loads as well as high temperature gradients; promoting several failure modes caused by arising thermo-mechanical stresses. Consequently, the proper design of the thermal management of these devices plays a key role in their reliability. To this end, a multi-physics multi-domain approach is proposed to improve the operational reliability of power packages, by precisely describing the actual loading situation and assessing the lifetime of the entire system. Thereby, in a global modeling approach entire PCB assemblies (PCB-A) with actively loaded power packages can be examined electro-thermo-mechanically. Based upon the results of the global model areas of interest are identified, e.g. an area with high stress concentrations, and investigated further using sub-modeling approaches and local damage modeling. To verify the developed methodologies an electro-thermal experimental test is used - a so-called Power Thermal Cycle (PTC) test. Using the proposed simulation strategy, critical areas can be determined and virtually investigated; enabling the evaluation of arbitrary power electronic systems, without the need of performing a time-consuming PTC test.

Multiphysics Analysis to Effectively Evaluate Thermal Performance of Liquid-Cooled Electric Machines

The prediction of maximum operating temperature in electric machines is very important to ensure that the machine can produce the required power safely. Accurate thermal modeling is required to predict the Heat Transfer Coefficient (HTC) of walls between coolant and heat sources and estimate the temperature in the machine. Analytical calculation of HTC is difficult for sophisticated geometric bodies since the dimensionless correlations are only available for simple geometries. To reduce the effort required to develop a thermal model of the whole cooling system, a 2- way numerical multi-physics approach using Finite Element Analysis (FEA) and Computational fluid dynamics (CFD) to determine HTC and evaluate the thermal performance of a liquid-cooled electric machine is presented in this paper. This 2- way method called a hybrid approach is used to estimate HTC of liquid-cooled SRM. In this study, a switched reluctance machine (SRM) is used as an example model to evaluate its thermal performance. In the hybrid approach, using the HTC estimated by CFD, and heat generation in the machine as the inputs, the FEA is used to determine the maximum steady-state temperature in the machine. An analytical approach is also implemented to determine the HTC of the example SRM. The calculated HTC is used in FEA to obtain the temperature in the machine. The temperature obtained from hybrid and analytical approaches is compared. The SRM considered in this study is built and tested for different operating points. Results obtained from the hybrid approach are validated with the experimental temperature data

The integrated history of repeated caldera formation and infill at the Okataina Volcanic Centre: Insights from 3D gravity and magnetic models

Multistage collapse caldera create complex geological structures that are often buried by kilometers of pyroclastic infill and late stage lava domes, making study of their origins difficult from outcrop alone. Here we present new gravity and aeromagnetic data compilations derived from terrestrial, lake and airborne surveys to investigate the buried internal structure of the Okataina Volcanic Centre (OVC) and interpret its stages of development. Magnetic highs (1300 nT) are caused by a combination of thick lava flows and domes that infill the collapse structures, and a deeper feeder structure/dyke complex that extends below the basement to at least 6 km depth. Magnetic lows caused by hydrothermal alteration are associated with topographically low areas near the topographic and structural collapse margins suggesting fluid circulation within the caldera exploits both deep and shallow structures. Several large rhyolite lava flows contain low magnetisation rims reflecting the glassy outer carapace and crystalline core.The gravity data show a − 62 mGal residual gravity low associated with the OVC, with the steepest gradients occurring inside the topographic margins. The gradient of the gravity low is stepped towards its lowest point near the outlet of Lake Tarawera. Each step is interpreted as relating to the buried structural collapse margins of the Utu, Matahina, Rotoiti and possibly Kawerau, catastrophic caldera forming eruptions, creating an overlapping and nested caldera structure, however there is no evidence for an OVC encircling ring structure. Buried caldera margins associated with oldest Utu eruption may play a role in the location of the youngest Tarawera eruptions. We propose smaller amplitude gravity lows that extend outside the topographic margins of the caldera are related to lateral magma migration to the south-west, exploiting regional tectonic stress regimes, towards eruption vents within the caldera. 3D gravity inversion, including models numerically constrained by a 3D magnetotelluric model, suggest a caldera depth of 5000 ± 500 m that accumulated over multiple collapse episodes along with rifting induced subsidence in the past 550 ka.Our multiphysics approach to investigating caldera structure has revealed buried structural margins and their relationships to eruptive vents and hydrothermal features at the OVC. Detailed geophysical models of caldera structure are rare globally, and our findings are consistent with analogue and numerical models of caldera formation that accompanies the most violent volcanic eruptions on Earth.

Multiphysics analysis of RANS-based turbulent transport of solid fission products in the Molten Salt Fast Reactor

The analysis of innovative reactor concepts such as the Molten Salt Fast Reactor (MSFR) requires the development of new modeling and simulation tools. In the case of the MSFR, the strong intrinsic coupling between thermal–hydraulics, neutronics and fuel chemistry has lead to the adoption of the multiphysics approach as a state-of-the-art paradigm. One of the peculiar aspects of liquid-fuel reactors such as the MSFR is the mobility of fission products (FPs) in the reactor circuit. Some FP species appear in form of solid precipitates carried by the fuel flow and can deposit on reactor boundaries (e.g., heat exchangers), potentially representing design issues related to the degradation of heat exchange performance or radioactive hotspots. Other precipitates might be present in the primary system as well, e.g. due to oxidation of fissile species which might lead to local criticality issues. The integration of transport models for solid particles in multiphysics codes is therefore relevant for the prediction of deposited fractions. To this aim, a previously developed Eulerian single-phase transport model is employed to analyze the distribution of solid FPs in two simplified MSFR-related geometries. The effect of physical parameters and of distributed particle sources on the numerical requirements needed to resolve particle concentration gradients at reactor boundaries in the considered geometries is investigated with the aid of analytical results. Analytical estimates of concentration gradients, even though not in full agreement with simulations, prove useful to drive the choice of adequate mesh refinements. Furthermore, the influence of different RANS turbulence modeling approaches on the prediction of particle distributions and deposition is tested. Results show a limited influence of the choice of turbulence models and parameters on the deposited fraction and on concentration gradients. As a result, it is found that deposition rates are scarcely affected by the choice of turbulent Schmidt number, with lower diffusivities being compensated by larger gradients. Large deposited fractions are indeed predicted, suggesting the efficiency of FPs transport mechanisms in the reactor and the need for the integration of adequate FPs transport models within state-of-the-art MSFR codes.

An inventive multi-scale, multiphysics modeling approach and comparative analysis of distinctive features of planar ionization waves in air: I. Negative streamers

A multiscale-multiphysics model of two different patterns of negative discharge (droplets and branches) based on the well-known electro-hydrodynamic equations in a two-dimensional non-axisymmetric configuration is developed to investigate the main features of the negative discharge (e.g. bifurcation, stochastic branching, and evolution of all charged particles including positive and negative ions and electrons). We discuss the challenges behind the finite-element-based streamer models and illustrate the robustness and efficiency of introduced three different meshing techniques via coupled fixed and adaptively refined meshes in terms of the accuracy of results and computational burden. The capability of the model in representing an outstanding spatial resolution of the streamer propagation and its stages is demonstrated under both uniform and non-uniform electric fields. A further required extension of the presented model for the representation of the positive streamer is presented in part II of this series paper. Also, in this part, the distinctive characteristics of both positive and negative streamers are presented, and the different features achieved from the model are thoroughly discussed and compared with the prior experimental results.

An inventive multi-scale, multiphysics modelling approach and comparative analysis of distinctive features of planar ionization waves in air: II. Positive streamers

A robust numerical framework for positive streamer modelling based on electro-hydrodynamic equations coupled with Poisson and Helmholtz differential equations for the photoionization process is presented. The proposed multi-layer meshing scheme in a 2D non-axisymmetric finite-element model along with a hybrid meshing technique presented in part I of this series paper for negative streamers provide high accuracy, spatial resolution, and capability to present the major features of both positive and negative streamers. In addition, the presented model is utilized to simulate multi positive and negative streamers propagation in a non-uniform electric field in the air. The main characteristics of the positive and negative streamers including the morphology, distribution pattern of space charges, local electric field, diameter, length, and velocity are presented, discussed, and compared with the experiment. Moreover, the impacts of initial seed density and voltage on the propagation of streamers are presented and explained. The branching mechanism arising from Laplacian instability and its impact on the streamer parameters such as tip electric field and dominant charge density is explained.

Toward a multi-physical approach to connection ageing in power modules

Generally, studies on ageing of power module have very often sought to write phenomenological laws able to give an estimate of their lifespan. These laws, identified experimentally, are empirical functions of the characteristic electrical loads that the component must withstand. As an alternative, this paper presents a first attempt of numerical approach for wire bond ageing in semiconductor power modules that takes account for the different multi-physical coupling mechanisms that occur during the component life. The electro-thermal and thermo-mechanical interactions are physically described and numerically modeled. The calculations show important viscoplastic strains in the connection zones and bonding wires from the first loading cycles. A quasi-cyclic stabilization of the mechanical and thermal states is observed at more than a hundred cycles (viscoplastic shakedown). The creep-fatigue damage mechanisms become then preponderant inducing mechanical-electrical couplings.