Order Model Research Articles

Model Order Reduction of an Ultraweak and Optimally Stable Variational Formulation for Parametrized Reactive Transport Problems

Model Order Reduction of an Ultraweak and Optimally Stable Variational Formulation for Parametrized Reactive Transport Problems

Harnessing Moringa oleifera root powder (MORP) for the sustainable remediation of heavy metal contaminated water.

Heavy metal environmental pollution is rapidly increasing due to the increase in industrialization and urbanization. Industrial processes, such as paint production, mining, and raw materials producing industries release effluents rich in heavy metals, like Pb2+, Cd2+, Cu2+, and Cr3+. These heavy metals are dangerous because they persist in nature, are non-biodegradable and they have high tendency to accumulate in the environment and in living organisms who are exposed to them. This work studied the removal of heavy metals (Cu, Pb, Cr, and Cd) from aqueous solution using Moringa oleifera root powder (MORP) as the adsorbent. The MORP was characterized by SEM, FTIR, BET, and XRD. Batch adsorption experiments carried out investigated the effects of adsorbate concentration, adsorbent dosage, agitation time, pH and temperature on adsorption. The optimum parameters are: contact time (90 min); pH (9); adsorbent dose (0.6); metal ion concentration (30 mg L-1) for Cr and 40 mg L-1 for the rest; and temperature (50 °C) for Cu and Pb, and 70 °C for Cr and Cd. These experimental data were analyzed with 5 isotherm models (Temkin, Flory-Huggins, Langmuir, D-R and Freundlich). The result obtained fitted best to Temkin isotherm in comparison to others. Kinetic studies revealed that the pseudo-second order kinetic model best described the adsorption (with high R2 values ranging from 0.9810-0.9976) compared to pseudo-first order and intra-particle diffusion kinetics model. Results of the thermodynamic study showed that the sorption process was endothermic for Cu and Pb, but exothermic for Cd and Cr. The adsorbent showed good adsorptive tendencies toward the ions studied, and could be applied on an industrial scale for the remediation of metal contaminated water.

Control of continuous digester kappa number using generalized model predictive control

Kappa number variability at the digester impacts pulp yield, physical strength properties, and lignin content for downstream delignification processing. Regulation of the digester kappa number is therefore of great importance to the pulp and paper industry. In this work, an industrial application of model-based predictive control (MPC), based on generalized prediction control, was developed for kappa number feedback control and applied to a dual vessel continuous digester located in Western Canada. The problem was complicated by the need to apply heat at multiple locations in the cook. In this study, the problem was reduced from a multiple to a single input system by identifying three potential single variable permutations for temperature adjustment. In the end, a coordinated approach to the heaters was adopted. The process was perturbed and modeled as a simple first order plus dead time model and implemented in generalized predictive control (GPC). The GPC was then configured to be equivalent to Dahlin’s controller, which reduced tuning parameterization to a single closed loop time constant. The controller was then tuned based on robustness towards a worst-case dead time mismatch of 50%. The control held the mean value of the kappa number close to the setpoint, and a 40% reduction in the kappa number’s standard deviation was achieved. Different kappa number trials were run, and the average fiberline yield for each period was evaluated. Trial results suggested yield gains of 0.3%–0.5% were possible for each 1 kappa number target increase.

Stabilized weighted reduced order methods for parametrized advection-dominated optimal control problems governed by partial differential equations with random inputs

Abstract In this work, we analyze Parametrized Advection-Dominated distributed Optimal Control Problems with random inputs in a Reduced Order Model (ROM) context. All the simulations are initially based on a finite element method (FEM) discretization; moreover, a space-time approach is considered when dealing with unsteady cases. To overcome numerical instabilities that can occur in the optimality system for high values of the Péclet number, we consider a Streamline Upwind Petrov–Galerkin technique applied in an optimize-then-discretize approach. We combine this method with the ROM framework in order to consider two possibilities of stabilization: Offline-Only stabilization and Offline-Online stabilization. Moreover we consider random parameters and we use a weighted Proper Orthogonal Decomposition algorithm in a partitioned approach to deal with the issue of uncertainty quantification. Several quadrature techniques are used to derive weighted ROMs: tensor rules, isotropic sparse grids, Monte-Carlo and quasi Monte-Carlo methods. We compare all the approaches analyzing relative errors between the FEM and ROM solutions and the computational efficiency based on the speedup-index.

A cluster-based incremental potential approach for reduced order homogenization of bones.

We develop a cluster-based model order reduction (called C-pRBMOR) approach for efficient homogenization of bones, compatible with a large variety of generalized standard material (GSM) models. To this end, the pRBMOR approach based on a mixed incremental potential formulation is extended to a clustered version for a significantly improved computational efficiency. The microscopic modeling of bones falls into a mixed incremental class of the GSM framework, originating from two potentials. An offline phase of the C-pRBMOR approach includes both a clustering analysis spatially decomposing the micro-domain within an RVE and a space-time decomposition of the microscopic plastic strain fields. A comparative study on two different clustering approaches and two algorithms for mode identification is additionally conducted. For an online analysis, a cluster-enhanced version of evolution equations for the reduced variables is derived from an effective incremental variational formulation, rendering a very small set of nonlinear equations to be numerically solved. Several numerical examples show the effectiveness of the C-pRBMOR approach. A striking acceleration rate beyond 104 against conventional FE computations and that beyond 103 against the original pRBMOR approach are observed.

Precision Offloading in Edge Computing: Leveraging Predictive Model

The intended effect of the investigation is to provide sophisticated prediction and decision-making models in order to optimize service delivery and improve the Quality of Experience (QoE) for users. This research tackles the problems that are associated with job offloading in edge computing settings. In order to reduce service latency and improve overall performance, the Bi-Directional Long Short-Term Memory (B-LSTM) model is used. This model provides the ability to forecast task creation and server load. In order to accommodate the particular qualities of different devices, the Selective Objective Offloading Decision (SOOD) approach is presented. This method makes use of the TOPSIS methodology to turn server assessment into a decision-making issue that involves several criteria. A considerable increase of 98.4% in user quality of experience is achieved by the SOOD paradigm. In addition, the Rapid Offloading Decision (ROD) model is presented in order to manage unexpected work patterns. This is accomplished by using the log information of surrounding devices, which results in instantaneous and dependable offloading choices. Through the usage of prediction algorithms and selective decision-making, this research gives a complete strategy to improving the efficiency of edge computing. The goal of this technique is to maximize the utilization of servers and the user experience.

Technological Advancements and Their Impact on Organisational Information Security

This paper explores the crucial role of strategic management and integration of information security within organisations to safeguard against diverse threats in a rapidly evolving digital environment. It posits that effective management of information security significantly enhances organisational resilience and protects strategic assets amidst global information exchange and technological advancements. The research employs a systematic literature review method, analysing existing definitions and perspectives on information security, supplemented by an analytical examination of potential vulnerabilities within corporate security systems and a case study of the IBM model in order to provide a comprehensive approach to information security in practice. The study shows that technological progress and the development of comprehensive security frameworks by governmental and non-governmental organisations directly improve the effectiveness of information security measures. Furthermore, it emphasises information as a strategic asset that requires a holistic security approach incorporating organisational and legal measures. The findings advocate for a multidimensional approach to information security, highlighting the necessity of continuous adaptation to effectively combat evolving threats. This comprehensive analysis contributes to the scholarly discourse on information security management, offering insights that can inform future research and practical applications in enhancing organisational resilience.

A fractional order SIR model describing hesitancy to the COVID-19 vaccination

This study introduces a SIR (Susceptible-Infectious-Recovered) model using fractional derivatives to assess the population's hesitancy to the COVID-19 vaccination campaign in Portugal. Leveraging the framework developed by Angstmann [1], our approach incorporates fractional derivatives to best describe the nuanced dynamics of the vaccination process. We begin by examining the qualitative properties of the proposed model. To substantiate the inclusion of fractional derivatives, empirical data along with statistical criteria are applied. Numerical simulations are performed to compare both integer and fractional order models. An epidemiological interpretation for the fractional order of the model is provided, in the context of a vaccination campaign.

Understanding the influence of impedance transitions of acoustically reflective vertical surfaces on the frequency-dependent effectiveness of absorptive ceilings in large non-Sabinian empty spaces

An unexpected phenomenon was observed during measurements of reverberation time in empty spaces to be fitted out, in which the only sound absorbing treatment consists of mineral fiber tiles on the ceiling, a material typically highly absorbing at mid and high frequencies during laboratory measurements: this involves a systematic increase of the reverberation time in octave bands between 1 kHz and 4 kHz in the measured empty spaces. The physical understanding of this surprising observation has focused on the question of the influence of impedance transition on sound scattering, and the influence of the frequency-dependent scattering properties of acoustically reflective vertical surfaces of different natures (glazing, plasterboard lining, masonry) of these empty spaces on the efficiency of a sound absorbing ceiling. These scattering phenomena were studied and modeled in a computer model in order to verify the model's adjustment hypothesis, an essential initial step before the study of the acoustic parameters in future fitted spaces and the effectiveness of acoustic solutions for space planning of open plane offices. Good consistency between the measurements and the empty model was observed when the frequency-dependent scattering behaviour of the vertical reflecting surfaces of different nature was taken into account in the simulation.

Device packaging and integration optimization based on neural network method: Effect of microchannel structure on heat sink performance

With the continuous improvement of power density of electronic device packaging, microchannel structure based on advanced packaging integration technology has become one of the hot topics in thermal management research. In this paper, two heat sink models are numerically simulated: rectangular microchannel and microchannel-pinfin hybrid. The effects of microchannel structure, inlet/outlet arrangement and pinfin design on heat sink performance were studied. The heat sink performance is evaluated by the maximum temperature at hotspot area, the total thermal resistance and the mean absolute temperature deviation (MATD). The research results show that at the same Reynolds number, the hybrid structure with pinfin has better heat sink performance. Compared with the rectangular microchannel structure, the maximum temperature at the hotspot area is reduced by 20.45 K and the total thermal resistance is reduced by 5.9 %. Compared with the three inlet/outlet arrangements of I-type, U-type and Z-type, the I-type hybrid structure has the best heat sink performance. The average absolute temperature deviation is about 10 K at high Reynolds number, and the temperature distribution is more uniform. In addition, the heat sink performance is improved by increasing the pinfin size to expand the heat transfer area. This paper uses simulation results to build an artificial neural network model in order to quickly and accurately predict numerical results under different parameter conditions. The network model has good accuracy and robustness, with a mean absolute error of 0.376 %. The prediction model takes heat source power, Reynolds number and pinfin structural parameters as input. Rapidly predicting the performance of heat sinks with different structural parameters under different boundary conditions will effectively save experimental and computational costs. This research provides new ideas and tools for optimizing heat sink design, and helps improve the application of microchannel cooling technology in electronic devices.

Vibration damping of sandwich panels using the Acoustic Black Hole effect

Among the existing passive vibration control techniques, the use of the acoustic black hole (ABH) is a solution that has the advantage of not increasing the mass of the structure in which it is integrated. In this paper, the benefits of inserting an ABH into a three-layer sandwich panel (fibreglass skins and honeycomb core) are investigated. It is known that an ABH effect can be achieved in a panel by locally reducing its stiffness (using a reduction in plate thickness according to a power law profile) and increasing local damping (using a coating of viscoelastic material). A sandwich panel induces a shear effect that strongly affect the wave propagation and lead to specific properties to the ABH effect. The equations of motion of a thick, symmetrical sandwich panel with non-uniform characteristics are obtained within the framework of zig-zag theory by applying Hamilton's principle. These equations lead to a semi-analytical model of order 6, from which an effective bending stiffness, dependent on both frequency and space can be derived. Using this model, it is demonstrated that the sandwich effective bending stiffness favours the ABH effect. This interesting property is also validated by experimental tests on sandwich panels using laser vibrometry.

Enhancing vibroacoustic reduced order models for digital twin development in industrial applications

In recent years, the growing interest towards the Digital Twin has prompted extensive exploration in both academic and industrial domains. To realize this technology, the development of simulation models that seamlessly run parallel to the physical assets is necessary. Model Order Reduction (MOR) plays a pivotal role, significantly easing the computational demands of 3D models while maintaining high fidelity. Krylov-based methods have proven successful in various vibroacoustic applications. However, state-of-the-art contributions employ suboptimal modeling strategies, leading to computationally inefficient offline phases and limited scalability. The linear Finite Element Method (FEM) exhibits lower efficiency than high-order FEM; Astley-Leis infinite elements, used to enforce non-reflecting conditions, constrain models to spherical domains, proving inefficient for elongated structures. In this work, advanced techniques-FEM Adaptive Order (FEMAO) and flexible infinite elements- are employed to alleviate the computational burden of the MOR process. The advantages of combining MOR, FEMAO, and flexible infinite elements are explored through various numerical examples. Specifically, we highlight the efficiency of output-based MOR in handling models with a high number of inputs, showcasing practical applications, especially in electric motor noise analysis. In conclusion, these numerical techniques reduce the computational burden of the offline phase, unlocking the Digital Twin for large-scale industrial models.

Total harmonic distortion estimation in piezoelectric micro-electro-mechanical-system loudspeakers via a lumped elements approach

In the context of the increasing trend towards miniaturization required by new wireless audio devices, piezoelectric micro-electro-mechanical-system (MEMS) loudspeakers are getting extensive attention. Finite Element Methods (FEM) and Lumped Element Methods (LEM) models able to accurately predict their linear response have been recently proposed in the literature. However, a nonlinear model able to predict the Total Harmonic Distortion (THD) of theses types of devices is to date not available. In this paper we present a FEM- assisted lumped-parameters equivalent circuit accounting for piezoelectric hysteresis which demonstrates good agreement with experimental THD results carried out on piezoelectric MEMS loudspeakers prototypes fabricated by STMicroelectronics. The nonlinear electro-mechanical domain is simulated through a Reduced Order Model (ROM) which considers as basis function the pre-stressed undamped actuated mode of the device computed via FEM, to simulate loudspeaker diaphragms of arbitrarily complex geometry. The parameters of the acoustical circuit are instead derived through analytical formulas.

Robustness and reliability of state-space, frame-based modeling for thermoacoustics

The Galerkin modal expansion is a well-known method used to develop reduced order models for thermoacoustics. A known issue is the appearance of Gibbs-type oscillations on velocity fluctuations at the interface between subdomains and at boundary conditions. Recent work of Laurent et al. (2019) [20] and Laurent et al. (2021) [23] have shown that it is possible to overcome this issue by using an over-completed frame, instead of a Galerkin modal basis. However, the low-order modeling based on this frame modal expansion may generate spurious modes. In this paper, the origin of these non-physical modes is identified and a method is proposed to automatically remove them from the outcome. By preventing any interaction between the physical and non-physical components, the proposed methodology drastically improves the robustness and reliability of the frame modal expansion modeling for thermoacoustics.

An efficient framework for design optimization using parametric reduced order modeling in a virtual prototyping phase

Virtual Prototype Assembly is a tool enabling the virtual assembly of components, the prediction of noise and vibration performance, and the evaluation of component modification scenarios. FRF-based substructuring is the technique used to assemble the components, either obtained from experimental measurements or numerical simulation models. However, numerical simulations of components can be time-consuming, especially when exploring several component design modifications of large models. The paper proposes a framework combining Virtual Prototype Assembly with a parametric Model Order Reduction technique for vehicle design optimization. The proposed parametric Model Order Reduction technique generates a Reduced Order Model of a component maintaining an explicit dependency on material parameters and properties, such as: Young's Modulus, density, Poisson coefficient, connection stiffness, etc.. This enables fast and efficient simulation of components designs without the need of recomputing numerical models, thus streamlining the component design optimization process. The effect of component design modifications will be assessed at the predicted target vibration levels on an academic application case.

Reduction of vortex-induced vibrations of a cantilevered hydrofoil with passive piezoelectric shunt

This work first investigates the ability of a low order fluid-structure model to fit the vortex-induced vibrations (VIV) observed on a truncated hydrofoil in a hydrodynamic tunnel. A particular VIV area is scrutinized, for which a hydrodynamic excitation mechanism due to a Karman-type vortex wake organization successively locks the first torsional and second bending mode of the cantilevered hydrofoil. Coupling two structure oscillators with a Van der Pol wake oscillator satisfactorily reproduces the amplitude response and the lock-in frequency. In order to build a low order model allowing to optimize control strategy, a fourth degree of freedom corresponding to the electric circuit of a resonant piezoelectric shunt has been added. Composed of an inductance and a resistance connected to a piezoelectric patch, the passive shunt was tuned to minimize the vibration amplitude in the frequency lock-in range. Model predictions are finally compared with experimental results.

A stable model order reduction scheme for an acoustic finite element model with perfectly matched layers

Perfectly matched layers (PMLs) are often used in an exterior acoustic finite element model to simulate the Sommerfeld radiation condition. However, the time-domain model is often too large to efficiently handle the transient simulation due to the frequency-dependent stretching function and the number of elements needed per wavelength. Krylov subspace-based model order reduction can alleviate this problem by only matching the important moments of the original model. However, stability-preservation is not guaranteed, which is the key to perform time-domain simulation. This work proposes a stability-preserving model order reduction scheme for the time-domain PMLs. By assessing the stability conditions of time-domain PMLs, a two-step strategy is applied. Firstly, a one-sided split basis is used to preserve the form of Lyapunov function of the original model such that a stable intermediate reduced order model (ROM) results. Secondly, an unsplit basis is applied on the modal transformation of this intermediate ROM to reduce the size further. The proposed method is verified by numerical simulations.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 2. Validation of calibration workflow

This paper is the second in a series of two that describes the application of discrete element method (DEM) and reduced order modeling to predict the effect of disturbances in the concentration of drug substance at the inlet of a continuous powder mixer on the concentration of the drug substance at the outlet of the mixer. In the companion publication, small-scale material characterization tests, a careful DEM parameter calibration and DEM simulations of the manufacturing process were used to develop a reliable RTD models. In the current work, the same calibration workflow was employed to evaluate the predictive ability of the resulting reduced-order model for an extended design space. DEM simulations were extrapolated using a relay race method and the cumulative RTD was accurately parameterized using the n-CSTR model. By performing experiments and simulations, a calibrated DEM model predicted the response of a continuous powder mixer to step changes in the inlet concentration of an API. Thus, carefully calibrated DEM models was used to guide and reduce experimental work and to establish an adequate control strategy. In addition, a further reduction in the computational effort was obtained by using the relay race method to extrapolate results. The predicted RTD curves were then parameterized to develop reduced order models and used to simulate the process in a matter of seconds. Overall, a control strategy evaluation tool based on high-fidelity DEM simulations was developed using material-sparing small-scale characterization tests.

Microencapsulated phase change material in 3D-printable mortars

The present study investigates the potential of replacing sand with microencapsulated phase change materials (MEPCM) in 3D-printable mortar to provide a promising way to improve thermal performance in 3D-printed buildings. Adding MEPCM significantly enhanced the rheological properties and early hardening evolution of cementitious mortar for 3D printing applications without the need for viscosity modifier agents. In hardened mortars, microstructural analysis and thermal cycling experiments confirmed that MEPCM remained intact and stable within the cementitious environment. The thermal properties of the treated mortars, including latent heat and thermal conductivity, were improved for energy-saving applications. Despite this, the compressive strength of the mortars dropped considerably by increasing the concentration of MEPCM while a strength of above 20 MPa was maintained. Simulation results from 3D Finite Element Method (FEM) and 1D reduced order model (ROM) closely matched the experimental data from printed walls in a thermal setup, validating the use of 1D ROM simulations for long-term predictions. In a case study, a printed wall where MEPCM replaced 80 % of the sand showed a ∼40 % reduction in energy consumption compared to mortar without MEPCM under real weather conditions.

Fractional order modeling of ecological and epidemiological systems: ambiguities and challenges

Fractional order modeling of ecological and epidemiological systems: ambiguities and challenges