Parametric Model Research Articles

Aggregate scheduling potential evaluation of large-scale air conditioning loads

As an adjustable load, the air conditioning (AC) load is a high-quality resource for power system operation regulation. However, due to the large number of AC loads and the heterogeneity among individual users, the scheduling potential of AC loads is difficult to accurately calculate. An aggregate scheduling potential evaluation of large-scale AC loads is proposed. Firstly, according to the operation characteristics of ACs, the aggregation models of central ACs and household split-type ACs are constructed by using the equivalent thermal parameter model and Monte Carlo sampling method respectively. Secondly, based on the proposed aggregation models of large-scale AC loads, the AC loads are rescheduled by resetting the set temperature, and the scheduling potential calculation model of central ACs and residential ACs is obtained. Finally, case studies show that AC loads have considerable scheduling potential when the indoor temperature is maintained within the range of human comfort.

Modelling and operation characteristics of air-cooled PEMFC with metallic bipolar plate used in unmanned aerial vehicle

The air-cooled proton exchange membrane fuel cell stack composed of the metallic bipolar plate with simple system and lightweight characteristics, is regarded as an ideal power source for unmanned aerial vehicles. A lumped parameter model is developed to analyze the operational characteristics at heights in the range of 0–3000 m. The temperature should not exceed 60 °C to ensure safe and effective operation. The peak power decreases by 20% and the stack has good adaptability to altitudes. The performance degradation can be largely attributed to the lower oxygen pressure at high altitudes through normalization analysis. It is more significant at higher currents owing to increased air starvation. The increase of the air stoichiometry is conducive to heat dissipation and reduces mass transfer loss. The voltage initially increases and then decreases as the air stoichiometry increases, and the optimal air stoichiometry corresponding to the maximum voltage is determined. Appropriately increasing the air stoichiometry can improve the consistency of the temperature and voltage distributions.

Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition.

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

From Scans to Parametric BIM: An Enhanced Framework Using Synthetic Data Augmentation and Parametric Modeling for Highway Bridges

From Scans to Parametric BIM: An Enhanced Framework Using Synthetic Data Augmentation and Parametric Modeling for Highway Bridges

Stochastic ordering in multivariate extremes

AbstractThe article considers the multivariate stochastic orders of upper orthants, lower orthants and positive quadrant dependence (PQD) among simple max-stable distributions and their exponent measures. It is shown for each order that it holds for the max-stable distribution if and only if it holds for the corresponding exponent measure. The finding is non-trivial for upper orthants (and hence PQD order). From dimension $$d\ge 3$$ d ≥ 3 these three orders are not equivalent and a variety of phenomena can occur. However, every simple max-stable distribution PQD-dominates the corresponding independent model and is PQD-dominated by the fully dependent model. Among parametric models the asymmetric Dirichlet family and the Hüsler-Reiß family turn out to be PQD-ordered according to the natural order within their parameter spaces. For the Hüsler-Reiß family this holds true even for the supermodular order.

A mechanical device for precise self-administration of ocular drugs

Individuals with glaucoma are routinely prescribed and required to self-administer eye drop-based medication multiple times a day. Unfortunately, the process of self-administering eye drops requires a level of dexterity, strength, and visual acuity that the elderly population often do not possess, resulting in poor adherence and worsening condition. In this paper, we detail a prototyped eye drop assist device designed to safely and reliably deliver prescription medications. The ergonomic device safely anchors the eye drop bottle tip an optimal distance away from the face, aligns to the pupil, and provides feedback to the user when their head is at an appropriate tilt for a successful eye drop administration. We experimentally verify that the device improves hand steadiness, provides accurate head tilt feedback, precisely positions the bottle tip, and predictably delivers eye drops into the eye according to a parameterized model.

A metamaterial bone plate for biofixation based on 3D printing technology

Bone plate design and production methods are critical for producing metamaterial bone plates. This study investigated the design and development process of porous structures and metamaterial bone plates for biofixation applications. We designed the porous structures for bone plates using parametric modeling and fused them via the implicit surface fusion method. Likewise, we designed the metamaterial bone plate structure using reverse reconstruction and topological optimization. Thereafter, we utilized three-dimensional (3D) printing for producing and post-processing the metamaterial bone plate. We observed a reduced stress-shielding effect after topological optimization. Additionally, the resultant diamond porous structure maintained a high porosity under pressure. The 3D-printed bone plates and fillers had a bright porous surface, with clear pore structures and good connectivity. The assembly of the 3D-printed femur, bone plate, filler, and standardized screw indicated a good fitting, and the filler could be fixed by the inclined surface. Taken together, the findings of this study established the foundation for the prospective application of metamaterial bone plates in biofixation.

Neural Garment Dynamics via Manifold‐Aware Transformers

AbstractData driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human body model and are limited to garment geometries that were seen during training. In this work, we take a different approach and model the dynamics of a garment by exploiting its local interactions with the underlying human body. Specifically, as the body moves, we detect local garment‐body collisions, which drive the deformation of the garment. At the core of our approach is a mesh‐agnostic garment representation and a manifold‐aware transformer network design, which together enable our method to generalize to unseen garment and body geometries. We evaluate our approach on a wide variety of garment types and motion sequences and provide competitive qualitative and quantitative results with respect to the state of the art.

Information fusion-based maintenance strategies selection for coastal concrete bridges using recycled fishing nets

Repurposing recycled materials for coastal concrete bridge maintenance addresses both engineering requirements and marine ecosystem preservation. This study explored the application of recycled nylon (RN) and polyethylene (PE) from discarded fishing nets in coastal concrete beam maintenance. Specifically, these fibers were incorporated into the polymer cement mortar (PCM) to produce two repair materials. Then, thirteen unrepaired or preventive/post-repaired concrete beams were exposed to the tidal zone for one or two years, followed by beam bending tests and the establishment of the corresponding parametric Finite element modeling (FEM). As an alternative to traditional heavy-parameter models, a novel lightweight crack segmentation model (LiteSqueezeSeg) was proposed for quantitatively analyzing beam surface crack properties. Lastly, experimental, simulated, and quantitative crack information were fused to comprehensively assess the beam performance. Results showed that both PCM-RN and PCM-PE effectively inhibit chloride ion penetration. The proposed LiteSqueezeSeg model (95.66% accuracy) achieved nearly identical performance to Deeplab v3 + but only with about 1/6 parameters. Fused results revealed that PCM-PE repaired beams exhibited superior mechanical performance but inferior ductility compared to PCM-RN. Moreover, both PCM-PE and PCM-RN repaired beams demonstrated that preventive repairs achieved more balanced performance than post repairs. Consequently, utilizing PCM-RN for preventive repair emerged as an optimal candidate. This study significantly contributes to the utilization of waste fishing nets in coastal concrete structure maintenance.

Is lithium from geothermal brines the sustainable solution for Li-ion batteries?

The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li ore deposits such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering life cycle assessments. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. Climate change impacts of selected cases vary within 18–59 kg CO2eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO2eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO2eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of fossil energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.

Numerical Model Test of Spilled Oil Transport Near the Korean Coasts Using Various Input Parametric Models

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

Multispectral Holographic Intensity and Phase Imaging of Semitransparent Ultrathin Films.

In this paper, we demonstrate a novel optical characterization method for ultrathin semitransparent and absorbing materials through multispectral intensity and phase imaging. The method is based on a lateral-shearing interferometric microscopy (LIM) technique, where phase-shifting allows extraction of both the intensity and the phase of transmitted optical fields. To demonstrate the performance in characterizing semitransparent thin films, we fabricated and measured cupric oxide (CuO) seeded gold ultrathin metal films (UTMFs) with mass-equivalent thicknesses from 2 to 27 nm on fused silica substrates. The optical properties were modeled using multilayer thin film interference and a parametric model of their complex refractive indices. The UTMF samples were imaged in the spectral range from 475 to 750 nm using the proposed LIM technique, and the model parameters were fitted to the measured data in order to determine the respective complex refractive indices for varying thicknesses. Overall, by using the combined intensity and phase not only for imaging and quality control but also for determining the material properties, such as complex refractive indices, this technique demonstrates a high potential for the characterization of the optical properties, of (semi-) transparent thin films.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BackgroundMetabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.ResultsThe results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.ConclusionsOur results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

A Multi-objective Optimization of Window and Light Shelf Design in Office Buildings to Improve Occupants’ Thermal and Visual Comfort

In office buildings, an efficient design of windows and using light shelves as a passive design strategy significantly influence the thermal and visual comfort of occupants while enhancing the productivity and health of users. This study proposes a multi-objective optimization for the optimal design of windows and light shelves in office buildings to improve occupants' comfort. Initially, a parametric model was developed using Grasshopper parametric software. Afterward, the Honeybee energy and daylight plugin was employed for simulating thermal and visual conditions, and finally, multi-objective optimization was conducted with the Octopus plugin. This plugin can determine the best solution as a compromise decision for maximizing occupants' comfort. In this paper, an office building in Tehran has been chosen as a case study. The decision variables are window-to-wall ratio (WWR), shading control strategy, viewpoint, the transmission of glass, light shelf length, and light shelf height. The objective functions of the study are the annual average Predicted Percentage of Dissatisfied (PPD) and the annual average Discomfort Glare Probability (DGP). According to the results, the proposed optimization model leads to an 18.5–70.1% and 9.3–57.1% reduction in DGP and PPD indexes, respectively. The study findings provide practical and useful instruction for architects to select optimal specifications of windows and light shelves to develop occupants' thermal and visual comfort in office buildings.

Creation of Historical Building Information Modelling (HBIM) Library, A Case Study of Registered House (No:56), Akçakoca

Building Information Modelling (BIM) is a methodology created by adapting information technologies to the construction industry, creating 3D parametric and object-based models, and adding time and cost data to the models. In recent years, the creation of digital cultural heritage is a current field in terms of protecting cultural heritage and transferring it to future generations. Development of technology and the increase in opportunities, destinations that cannot be physically visited due to access difficulties or pandemics are trying to transfer their cultural richness to digital environments. For this purpose, Historical Building Information Modelling (HBIM), which is the version of BIM applied to historical buildings, emerges as an important tool. The digitalization of cultural heritage structures by modelling is a process that requires intensive labour due to the lack of uniform details, and the fact that they were made with different construction techniques at different times and periods. For this reason, this process will be improved with the creation, dissemination, classification, and accessibility of parametric object libraries with HBIM. In this direction, the research method; is based on the analysis and synthesis of the literature review and case study in particular on modelling and library studies as a case study. As a field study, it was created in three dimensions with HBIM on a registered civil architecture example that has lost its originality in Düzce province, Akçakoca district, and the building and library elements were modelled and transferred to BIM. The original state of the building has been created to be experienced interactively. As a result, the building and its elements are defined by parametric objects. With the development of the HBIM library, the elements of the structures of similar typology become digitally accessible and digital legacies are created much more easily and effectively. HBIM studies create a comprehensive three-dimensional data set in digital documentation, restitution periods and restoration studies.

Nonlinear impedance characteristics of a gas hydraulic energy absorber and performance prediction for railway transportation of valuable goods

It is meaningful to investigate the nonlinear impedance characteristics of gas hydraulic energy absorbers (GHEAs) and their potential application in modern railway transportation of valuable goods. Fluid-mechanics-based full parametric modelling of a GHEA used in a high-speed train is performed, both simulations with the developed mathematic model and physical experiments are carried out on the static and dynamic characteristics of the GHEA. The agreement between the results of the simulation and the experiment with tolerable deviations validates the nonlinear mathematic model. Longitudinal train dynamics (LTD) modelling and simulation of a Chinese rapid freight train with the validated GHEA model or the conventional MT-2 friction-type energy absorber model is finally conducted. The results show that when the train uses GHEAs instead of MT-2 energy absorbers, the coupler forces and vehicle accelerations of the train are significantly reduced, and the vehicles operate more steadily in situations such as train startup, emergency braking and shunting operations; thus, gas hydraulic energy absorber is protective and would be alternative for equipping rapid freight trains for the transportation of valuable goods. The obtained nonlinear mathematic model in terms of the structural, oil and gas property parameters of the GHEA is useful and meaningful in further parameter design optimization of GHEAs and optimal specification of GHEAs for rapid freight trains.

DESIGN AND ANALYSIS OF CONNECTING ROD USING ANSYS

Connecting Rod is an important component of an internal combustion engine. It serves as an intermediate link between the piston and the crankshaft. It converts the reciprocating motion of piston into rotary motion of crankshaft. It is responsible for transferring power from piston to crankshaft and from there on to the transmission.The most commonly used materials in the production of Connecting Rod are Steel (for production of engines), Aluminium (ability to absorb high impact at the expense of durability, light in weight), or Titanium (combination of strength and lightness at the expense of affordability).The Objective of this project is to Design and Analyse Connecting Rod using different materials. A parametric model of Connecting Rod is modelled using CATIA V5 Software and Analysis is carried out using ANSYS 14.0 Software. Different materials taken into consideration are 42CrMo4, Al7075-T651, Al6061-1.5%SiC-1.5%B4C, Al7068.Performance parameters like Von-misses stress, Deformation and Factor of safety are analysed using ANSYS. From our work, it was observed that Al7068 has more Stiffness, less Stress and less Weight. Keywords:Al7075-T651,Al6061-1.5%SiC-1.5%B4C, Al7068, Aluminium, Connecting Rod

Numerical study of hemodynamic flow in the aortic vessel of Williams syndrome patient with congenital heart disease

Congenital arterial stenosis such as supravalvar aortic stenosis (SVAS) are highly prevalent in Williams syndrome (WS) and other arteriopathies pose a substantial health risk. Conventional tools for severity assessment, including clinical findings and pressure gradient estimations, often fall short due to their susceptibility to transient physiological changes and disease stage influences. Moreover, in the pediatric population, the severity of these and other congenital heart defects (CHDs) often restricts the applicability of invasive techniques for obtaining crucial physiological data. Conversely, evaluating CHDs and their progression requires a comprehensive understanding of intracardiac blood flow. Current imaging modalities, such as blood speckle imaging (BSI) and four-dimensional magnetic resonance imaging (4D MRI) face limitations in resolving flow data, especially in cases of elevated flow velocities. To address these challenges, we devised a computational framework employing zero-dimensional (0D) lumped parameter models coupled with patient-specific reconstructed geometries pre- and post-surgical intervention to execute computational fluid dynamic (CFD) simulations. This framework facilitates the analysis and visualization of intricate blood flow patterns, offering insights into geometry and flow dynamics alterations impacting cardiac function. In this study, we aim to assess the efficacy of surgical intervention in correcting an extreme aortic defect in a patient with WS, leading to reductions in wall shear stress (WSS), maximum velocity magnitude, pressure drop, and ultimately a decrease in cardiac workload.

Virtual design of woven fabrics based on parametric modeling and physically based rendering

Textures of woven fabrics are usually designed and produced according to geometric laws in the 2D plane. Physically Based Rendering (PBR) can further optimize and enrich the texture effect, but its application to the more complex 3D structures has been limited. This work reports a method that uses PBR and parametric modeling to construct woven textured materials with centimeter and millimeter level 3D structures. The method can design the structures of various woven fabrics without the need for analyzing the fabric structure details and transfer the inherently iterative work of fabric design to the digital space. The design can be directly applied to mainstream 3D modeling software for virtual presentations in different applications, hence improving the efficiency of woven fabric design and the fidelity of virtual presentation of fabric materials.

Long-term outcomes after hospitalization for atrial fibrillation or flutter.

Atrial fibrillation (AF) and flutter are common causes of hospitalizations but contemporary long-term outcomes following these episodes are uncertain. This study assessed outcomes up to 10 years after an acute AF or flutter hospitalization. Patients hospitalized acutely with a primary diagnosis of AF or flutter from 2008-17 from all public and most private hospitals in Australia and New Zealand were included. Kaplan-Meier methods and flexible parametric survival modelling were used to estimate survival and loss in life expectancy, respectively. Competing risk model accounting for death was used when estimating incidence of non-fatal outcomes. A total of 260 492 adults (mean age 70.5 ± 14.4 years, 49.6% female) were followed up for 1 068 009 person-years (PY), during which 69 167 died (incidence rate 6.5/100 PY) with 91.2% survival at 1 year, 72.7% at 5 years, and 55.2% at 10 years. Estimated loss in life expectancy was 2.6 years, or 16.8% of expected life expectancy. Re-hospitalizations for heart failure (2.9/100 PY), stroke (1.7/100 PY), and myocardial infarction (1.1/100 PY) were common with respective cumulative incidences of 16.8%, 11.0%, and 7.1% by 10 years. Re-hospitalization for AF or flutter occurred in 21.3% by 1 year, 35.3% by 5 years, and 41.2% by 10 years (11.6/100 PY). The cumulative incidence of patients undergoing catheter ablation of AF was 6.5% at 10 years (1.2/100 PY). Patients hospitalized for AF or flutter had high death rates with an average 2.6-year loss in life expectancy. Moreover, re-hospitalizations for AF or flutter and related outcomes such as heart failure and stroke were common with catheter ablation used infrequently for treatment, which warrant further actions.