Simplified Parametric Model Research Articles

Research on the Design of Recessed Balconies in University Dormitories in Cold Regions Based on Multi-Objective Optimization

Thermal comfort and daylighting are vital components of dormitory environments. However, enhancing indoor lighting conditions may lead to increased annual energy consumption and decreased thermal comfort. Therefore, it is crucial to identify methods to reduce buildings’ energy costs while maintaining occupants’ thermal comfort and daylighting. Taking the dormitory building of Songyuan No. 2 at Shandong Jianzhu University of Architecture, which is located in a cold region, as an example, a field measurement analysis was conducted on the recessed balconies within the dormitory. The measured data were analyzed and utilized to simulate the annual energy consumption, thermal comfort predicted mean vote (PMV), and useful daylight illuminance (UDI) values of the dormitory units using the Grasshopper platform with the Ladybug and Honeybee plugins. The different depths of the balconies and window-to-wall ratios have a significant impact on the indoor physical environment and energy consumption, leading to the design of independent variables and the construction of a simplified parametric model. The simulation results underwent multi-objective optimization using genetic algorithm theory through the Octopus platform, resulting in a Pareto optimal solution set. Comparisons between the final-generation data and simulations of the original Song II dormitory unit indicate potential energy savings of up to 2.5%, with a 25% improvement in indoor thermal comfort satisfaction. Although there was no significant improvement in the UDI value, all the solution sets meet the minimum requirement of 300 lux specified by relevant regulations, according to the simulated average illuminance levels on the indoor work plane. Finally, the 60 optimal solution sets were further screened, filtering out sets deviating excessively from certain objectives, to identify 6 optimal solutions that are more balanced and exhibit a higher overall optimization rate. These findings offer detailed data references to assist in the design of dormitory buildings in cold regions.

Group sparse Bayesian learning for data-driven discovery of explicit model forms with multiple parametric datasets

Extracting the explicit governing equations of a dynamic system from limited data has attracted increasing attention in the data-driven modeling community. Compared to black-box learning approaches, the sparse-regression-based learning method enables discovering an analytical model form from data, which is more appealing due to its white-box nature. However, distilling explicit equations from real-world measurements with data uncertainty is challenging, where many existing methods are less robust. Moreover, it is unclear how to efficiently learn a parametric system from multiple data sets with different parameters. This paper presents a group sparse Bayesian learning approaches to uncover the explicit model forms of a parametric dynamical system with estimated uncertainties. A deep neural network is constructed to improve the calculation of derivatives from noisy measurements. Group sparsity is leveraged to enable synchronous learning from a group of parametric datasets governed by the equations with the same functional form but different parameter settings. The proposed approach has been studied over a few linear/nonlinear ODE systems in explicit and implicit settings. In particular, a simplified parametric model of intracranial dynamics was identified from multiple synthetic datasets with different patient-specific parameters. The numerical results demonstrated the effectiveness of the proposed approach and the merit of synchronous learning from multiple datasets in a group sparsifying Bayesian setting.

Effect of an inter-vehicle damper on the dynamics of a high-speed train

It is meaningful to understand the effect of inter-vehicle damper and its parameters on high-speed train dynamics in the context of train inter-vehicle suspension design or optimization. In this study, Multi-body System (MBS) dynamics modelling of a high-speed CRH train with inter-vehicle damper is firstly carried out, in which a simplified-parametric model was proposed and validated to depict damping characteristics of the damper. Simulation results using the MBS model show that inter-vehicle damper devised in high-speed train can obviously reduce lateral vibrations at the connecting passage of adjacent compartments without increasing the wheelset lateral shift force and derailment coefficient, thus it can improve overal ride comfort of the CRH train. In addition, inter-vehicle damper can resist severe lateral shocks and guarantee safety when two high-speed trains pass each other. The developed MBS model of the CRH train is an accurate and useful platform, the obtained results in this study are also valuable in following vibration analysis and inter-vehicle suspension optimization of the CRH train.

A feasibility study of a tube bundle exchanger with phase change materials: A case study

Latent heat thermal storage offers a flexible service to members of a heat district grid as the stored heat can be used to reduce the morning peak demand, limiting the consequences for the production facilities. This work investigates the optimized design of a latent heat thermal storage reactor, integrated in an existing building supplied by district heating, for demand-side management applications. The storage reactor was designed as a tube bundle heat exchanger in which a commercial-grade paraffin was used as the phase change material. The optimized design variables that maximized the use of the Phase Change Material were the tube pitches and the fin heights. The integration of the storage reactor with the energy system required the control and prediction of its state of charge and the power output. These parameters are usually evaluated through 3D numerical dynamic simulations that require a large computational effort, which is beyond the capability of basic microcontroller systems. A simplified parametric model has been used to overcome this issue. A load-tracking algorithm was embedded in an existing building program. The algorithm was able to reduce the thermal power peak by 62 kWh from 6:00 to 9:00 a.m. Decreasing the pitch from 95 mm to 82 mm led to a +34% increase in the use of the PCM. Similar results were obtained when the fin height was increased from 20 mm to 32 mm. The results of this investigation suggest that the proposed model could be applied to similar buildings fed by district heating network systems.

Parametric numerical study of dislocation density distribution in Czochralski-grown germanium crystals

Dislocations strongly affect the quality of Czochralski-grown germanium crystals used for various applications, however, the influence of different growth parameters on the dislocation distribution has not been extensively studied in the literature. In the present work, a simplified parametric crystal growth model is introduced which is suitable for large-scale numerical studies. The time-dependent temperature and dislocation density distributions in the crystal are calculated and analyzed. The Alexander–Haasen model is applied for dislocation modeling. Model parameters are taken from previously published discrete dislocation dynamics simulations, establishing a novel coupling between mesoscopic and macroscopic simulations. A W-shaped radial distribution of the dislocation density is obtained in the simulations. It is shown that the effective ambient temperature and crystallization front deflection have the strongest influence on the dislocation distribution. The results are compared to the experiment, showing a qualitative agreement, but also several deviations. Limitations of the considered dislocation modeling approach and the simplified thermal model are discussed.

Deep Learning for Parametric Channel Estimation in Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) communication with a low bit error rate depends upon the availability of accurate Channel State Information (CSI) at the base station. The massive MIMO systems can be either deployed using time division duplexing with channel reciprocity assumption or by availing frequency division duplexing, which requires closed-loop feedback for CSI acquisition. The channel reciprocity simplifies transmission in time division duplexing; however, it suffers a bottleneck due to pilot contamination, whereas transmission in frequency division duplexing is challenged by channel estimation complexity, CSI feedback, and overall delay in CSI transfer. This paper proposes a simplified parametric channel model, its deep neural network aided estimation along with pilot decontamination for time division duplexing and a low rate parametric feedback and improved precoding for frequency division duplexing based massive MIMO systems. This novel framework integrates the massive MIMO parametric estimation and deep learning for improved estimation and precoding. Our proposed model also offers a unified approach for CSI acquisition with a performance bound on channel correlation in fast time-varying conditions. A theoretical model has been presented using Gaussian assumptions and validated by Monte-Carlo simulations. The results show total nullification of pilot contamination and high-performance gains when the proposed technique is employed for estimation.

The preliminary thermo–mechanical design, simulation and optimization, of LAD onboard the eXTP space mission

The eXTP (enhanced X-ray Timing and Polarimetry) mission is a major project of the Chinese Academy of Sciences (CAS) and China National Space Administration (CNSA) currently performing a phase B study and proposed for a launch in 2027. The eXTP scientific payload envisages a suite of instruments offering unprecedented simultaneous wide-band X-ray timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study and it is expected to provide key hardware elements, including the Large Area Detector (LAD) composed of 40 modules for a total effective area of 3.0 m2 at 6.0 keV. In this paper, we describe the preliminary study that led to the design solutions adopted for the most important thermo-mechanical drivers of the LAD Module, which have been elaborated and used for the demonstration of compliance to the system requirements at spacecraft level. We report in particular the mechanical design for the Module and its components, the thermal analysis for each range temperature of the worst cases (coldest case) with the evaluation of the thermoelastic deformation of a reduced module and the results of a campaign of static and dynamic finite element analysis of a simplified parametric model with an accurate mesh processing and the preliminary optimization of the critical components.

Asymptotic Theory of Flapping Wing Propulsion in Extreme Ground Effect

This study, dedicated to flapping wing propulsion in immediate proximity to a wall or between closely spaced flat walls, makes use of the method of matched asymptotic expansions. Its purpose is to create a simplified parametric model of such a propulsion system based on a single major assumption: immediate closeness of oscillating wing to a solid wall. In the case of a rectangular finite-aspect ratio wing, analytical expressions have been obtained for the coefficients of instantaneous and period-averaged thrust force as well as for the efficiency of the propulsor as a function of distance from the wall, Strouhal number and wing aspect ratio for selected cases of heaving, pitching and combined oscillations. It is shown that for some oscillation modes closeness to the ground results in increases in thrust and efficiency, and that optimally combined (considering ratio of the amplitudes and phase shift of contributing motions) heave–pitch oscillations allow to maximize thrust or efficiency of the flapping-wing propulsor. Increase in aspect ratio and decrease in Strouhal number (relative frequency of oscillations) in the case of heaving invariably brings about the increase in the ideal efficiency. An example is provided of a non-planar extreme ground effect application considering oscillations of a ring-wing embracing circular cylinder. A rule is derived for recalculation of the characteristics of the flapping-wing propulsor near a flat wall onto the characteristics of the same wing operating in a narrow canal between parallel walls. This rule can also be applied to evaluate propulsive properties of a ring-wing oscillating between co-axial cylindric surfaces.

Simplified parametric model for analyzing the behavior of stone pavements subjected to horizontal traffic loads

Urban stone pavements have always represented the historical and cultural value of a city. They are appreciated thanks to the use of natural materials of different colors, shapes and sizes that are laid in different laying patterns, responding not only to structural requirements but also to ecological and aesthetic needs producing an attractive landscape. Since stone block pavements consist of individual elements with irregular side surfaces that interact with each other, an important parameter that must be considered in the design is their structural resistance to horizontal stresses mainly due to braking, turning and acceleration of vehicles. In this work, the possibility of using a parametric model to study the behavior of pavements made of stone cubes subjected to horizontal loads was analyzed. Each stone element was modeled by a rigid body while the inter-elements and element-ground interactions using specific features that allow to insert a visco-elastic interaction among the bodies and the limits to failure, distinguishing between tension and compression. This model has allowed to analyze, at the same mechanical parameters (stiffness, damping, etc.), the behavior of such pavements subjected to horizontal loads as a function of laying pattern and the dimensions of the stone cubes. From the modeling, it has emerged that both the size of the elements as well as their laying pattern have a relevant influence on the behavior of the stone element pavements subjected to horizontal loads and must therefore be considered in the design.

Effect of Tension-Compression Asymmetry Response on the Bending of Prismatic Martensitic SMA Beams: Analytical and Experimental Study.

This paper aims to analytically derive bending equations, as well as semi-analytically predict the deflection of prismatic SMA beams in the martensite phase. To this end, we are required to employ a simplified one-dimensional parametric model considering asymmetric response in tension and compression for martensitic beams. The model takes into account the different material parameters in martensite twined and detwinned phases as well as elastic modulus depending on the progress of the detwinning process. In addition, the model considers the diverse slope of loading and unloading in martensite detwinned phases favored by tension and compression. To acquire general bending equations, we first solve the pure bending problem of a prismatic SMA beam. Three different phases are assumed in the unloading procedure and the effect of neutral fiber distance from the centerline is also considered during this stage. Then according to the pure bending solution and employing semi-analytical methods, general bending equations of an SMA beam are derived. Polynomial approximation functions are utilized to obtain the beam deflection–length relationship. To validate the attained analytical expressions, several three- and four-point bending tests were conducted for rectangular and circular SMA beams. Experimental data confirm the reasonable accuracy of the analytical results. This work may be envisaged to go deep enough in investigating the response of SMA beams under an arbitrary transverse loading and stress distribution during loading and unloading, as well as findings may be applicable to a good prediction of bending behavior.

Optimization of an EMU train carbody by the value of the natural bending frequency

Finding ways to optimize the body structure is an important point in the design of new electric train cars. Reducing the mass of the body leads to a lightening of the parts of the rolling stock associated with it, a reduction in energy consumption for operation and a decrease in wear in the “wheel — rail”system. Reducing the weight of the body is possible by assigning optimal rigidity to its main load-bearing elements. Increasing the rigidity of the body with a constant mass is also an important task to obtain the standard dynamic properties of the car body.The article presents method for optimizing the body structure based on calculating the value of its first frequency of natural bending vibrations. The calculation was carried out by the finite element method using a simplified beam-shell parametric model. Within the optimization calculations, 3125 working versions of sections of the main load-bearing structural elements with different rigidity were considered — bracing and cross-beams of the frame, inter-window racks and cross-beams of the roof. The sensitivity of the value of the natural vibration frequency to the change in the rigidity of the main bearing elements without taking into account the change in mass is analyzed. It was found that the rigidity of the frame bracing and cross beams has the greatest influence on the frequency value. It is shown that the ratio of the rigidity of the main bearing elements does not remain constant for optimal design options and depends on the design of the body, the target values of its mass and rigidity. When mass is limited, it is possible to choose designs that are characterized by the greatest overall body rigidity and are the most optimal in terms of manufacturability. By limiting the values of natural vibration frequency, it is possible to choose a body structure with the lowest metal mass. The presented approach allows making decisions on body modification based on the required parameters of mass and (or) the frequency of natural bending vibrations. This approach can be used in pre-design studies of the bodies of new passenger rolling stock.

Parametric Modeling of Depth-Limited Wave Spectra under Hurricane Conditions with Applications to Kinetic Umbrellas against Storm Surge Inundation

This paper presents a simplified parametric model for the estimation of depth-limited hurricane wave spectra, accounting for swell and wind-sea components, for coastal engineering applications. The model was evaluated against observations obtained from three shallow water sites in Florida during Hurricane David in September 1979. It was revealed that the parametric approach increases in accuracy with decreasing distance to the storm center and generally provides a conservative representation of the significant wave height, albeit overestimating the peak wave frequency. The model was subsequently adopted to evaluate the performance of tilted hyperbolic paraboloidal (hypar) shells (referred to as “kinetic umbrellas”) as an adaptable alternative to conventional floodwalls via smoothed particle hydrodynamics (SPH). The introduction of hypar geometry proved superior to conventional sloped barriers in reducing overtopping waves but decreases in effectiveness at levels of inundation greater than two-thirds the deployed height. Furthermore, umbrellas exhibiting larger geometrical warping were more capable at suppressing overtopping but must sustain larger base shear forces when subjected to irregular waves consistent with landfalling hurricanes.

BVS stent optimisation based on a parametric model with a multistage validation process

Developing new polymer-based bioresorbable vascular scaffolds (BVS) requires a new structural geometry, as these polymer materials are characterised by substantially lower stiffness than metallic alloys. Sufficient radial strength of the stent is essential, as insufficient vessel wall support can lead to a significant recoil (reduction of the vessel lumen) and can cause dangerous complications. To assess the stress development and deformation of the stent, finite element analysis (FEA) was used. First, a complex FEA of the inflation and crimping processes of a bioresorbable coronary stent was performed. An optimisation procedure based on the simplified parametric FE model was subsequently performed to obtain the optimal geometric parameters of the analysed structure. The objective of the optimisation was to maximise the vessel diameter after the BVS implantation. The optimisation procedure was based on automatic model-generating scripts, numerical analyses and implementation of genetic algorithms. Finally, a comparison of the parametric and complex models results was performed, as well as a final validation at several stages (structural studies, computational fluid dynamics (CFD) simulations and in vivo testing). The validation confirmed that the presented optimisation methodology represents a valid and useful tool for the stent design process.

Deep Photometric Stereo Networks for Determining Surface Normal and Reflectances

This article presents a photometric stereo method based on deep learning. One of the major difficulties in photometric stereo is designing an appropriate reflectance model that is both capable of representing real-world reflectances and computationally tractable for deriving surface normal. Unlike previous photometric stereo methods that rely on a simplified parametric image formation model, such as the Lambert's model, the proposed method aims at establishing a flexible mapping between complex reflectance observations and surface normal using a deep neural network. In addition, the proposed method predicts the reflectance, which allows us to understand surface materials and to render the scene under arbitrary lighting conditions. As a result, we propose a deep photometric stereo network (DPSN) that takes reflectance observations under varying light directions and infers the surface normal and reflectance in a per-pixel manner. To make the DPSN applicable to real-world scenes, a dataset of measured BRDFs (MERL BRDF dataset) has been used for training the network. Evaluation using simulation and real-world scenes shows the effectiveness of the proposed approach in estimating both surface normal and reflectances.

GA-Adaptive Template Matching for Offline Shape Motion Tracking Based on Edge Detection: IAS Estimation from the SURVISHNO 2019 Challenge Video for Machine Diagnostics Purposes

The estimation of the Instantaneous Angular Speed (IAS) has in recent years attracted a growing interest in the diagnostics of rotating machines. Measurement of the IAS can be used as a source of information of the machine condition per se, or for performing angular resampling through Computed Order Tracking, a practice which is essential to highlight the machine spectral signature in case of non-stationary operational conditions. In these regards, the SURVISHNO 2019 international conference held at INSA Lyon on 8–10 July 2019 proposed a challenge about the estimation of the instantaneous non-stationary speed of a fan from a video taken by a smartphone, a pocket, low-cost device which can nowadays be found in everyone’s pocket. This work originated by the author to produce an offline motion-tracking of the fan (actually, of the head of its locking-screw) and obtaining then a reliable estimate of the IAS. The here proposed algorithm is an update of the established Template Matching (TM) technique (i.e., in the Signal Processing community, a two-dimensional matched filter), which is here integrated into a Genetic Algorithm (GA) search. Using a template reconstructed from a simplified parametric mathematical model of the features of interest (i.e., the known geometry of the edges of the screw head), the GA can be used to adapt the template to match the search image, leading to a hybridization of template-based and feature-based approaches which allows to overcome the well-known issues of the traditional TM related to scaling and rotations of the search image with respect to the template. Furthermore, it is able to resolve the position of the center of the screw head at a resolution that goes beyond the limit of the pixel grid. By repeating the analysis frame after frame and focusing on the angular position of the screw head over time, the proposed algorithm can be used as an effective offline video-tachometer able to estimate the IAS from the video, avoiding the need for expensive high-resolution encoders or tachometers.

Comparative Study on Multi-type Domes of Filament-Wound Composite Pressure Vessels

Filament-wound Composite Pressure Vessels (FCPV) have been used widely in the aerospace and commercial industries. Multi-type FCPVs have been applied to different mission scenes. In this paper, four types of domes (helical winding, helical winding ellipsoid, polar winding, polar winding ellipsoid) are studied based on a simplified parametric model, and a modified meridian computing method is employed for polar and helical winding domes after the inflection points. At last, four FCPV cases of different type of domes are calculated and discussed in detail. Results indicate that the parameters of the same winding pattern dome are similar, so it is suggested to use ellipsoid domes in prior for the convenience of fabrication and avoiding the inflection point at the meridian.

Effect of the nonlinear displacement-dependent characteristics of a hydraulic damper on high-speed rail pantograph dynamics

A new simplified parametric model, which is more suitable for pantograph–catenary dynamics simulation, is proposed to describe the nonlinear displacement-dependent damping characteristics of a pantograph hydraulic damper and validated by the experimental results in this study. Then, a full mathematical model of the pantograph–catenary system, which incorporates the new damper model, is established to simulate the effect of the damping characteristics on the pantograph dynamics. The simulation results show that large $$F_{\mathrm{const}}$$ (saturation damping force of the damper during compression) and $$C_{\mathrm{0}}$$ (initial damping coefficient of the damper during extension) in the pantograph damper model can improve both the raising performance and contact quality of the pantograph, whereas a large $$C_{\mathrm{0}}$$ has no obvious effect on the lowering time of the pantograph; the nonlinear displacement-dependent damping characteristics described by the second item in the new damper model have dominating effects on the total lowering time, maximum acceleration and maximum impact acceleration of the pantograph. Thus, within the constraint of total lowering time, increasing the nonlinear displacement-dependent damping coefficient of the damper will improve the lowering performance of the pantograph and reduce excessive impact between the pantograph and its base frame. In addition, damping performance of the new damper model would vary with the vehicle speeds, when operating beyond the nominal-speed range of the vehicle, the damping performance would deteriorate obviously. The proposed concise pantograph hydraulic damper model appears to be more adaptive to working conditions of the pantograph, and more complete and accurate than the previous single-parameter linear model, so it is more useful in the context of pantograph–catenary dynamics simulation and further parameter optimizations. The obtained simulation results are also valuable and instructive for further optimal specification of railway pantograph hydraulic dampers.

Internal Model Control for Rudder Roll Stabilisation and Course Keeping of a Surface Marine Craft

This paper presents a novel control design procedure for simultaneous course keeping and rudder roll stabilisation for a surface marine craft. We address the design using the Internal Model Principle. This approach has been used to analyse fundamental limitations of rudder roll stabilisation due to unstable zero dynamics, but it has not been exploited for full control system design. The design is based on roll sensitivity shaping and uses a simplified parametric model of the roll-induced wave motion. This design provides opportunities for adapting the performance due to changes in environmental and sailing conditions. This is a need driven by a well-known issue leading to performance degradation in this control problem, which is due to the presence of unstable zero dynamics and significant changes in the power spectrum of the disturbance.

Optimization design for crashworthiness of special school bus seat based on simplified parametric model

In order to accelerate the optimization process of automotive seats, decrease the amount of computations, and realize forward design of seat performance, a simplified parametric model (SPM) is introduced in this paper during the optimization process of a school bus seat. The validity of the SPM of the seat and the feasibility of the modeling method are proved. The results calculated by the SPM show a good consistency with the shell element model. The SPM can also accurately reflect the deformation and stress of the seat in dynamic tests. Through mechanical analysis and energy analysis of the seat frame, the dynamic performance of the seat is improved to meet regulatory requirements. The SPM has the advantages of simple process, less number of units, time savings in modeling and calculation, and it provides the possibility to quickly analyze the performance of school bus seats and improve seat structure.

Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus.

This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.