Parametric Model Research Articles

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53 %. Building energy consumption for high-rise pavilion typology is saved by 9.64 % than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design factors on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Combustion and extinction characteristics of an ethanol pool fire perturbed by low–frequency acoustic waves

To study combustion and extinction characteristics of flames perturbed by acoustic waves, the evolution of the shape of the pool fire, mass loss rate (ma′) of fuel, and mechanisms of extinction of the flames under acoustic perturbations were studied. Moreover, based on parameters of combustion characteristics of flames, parametric models of ma′ and flame extinction were developed. The research results demonstrate that the shapes of unperturbed flames change during combustion, however, when perturbed by acoustic waves, the flame shapes become irregular and the flame fronts become disorderly. Compared with the combustion of unperturbed flames, ma′ is increased under acoustic perturbation. There are upper and lower limits for the change of ma′, so the models of upper and lower limits of ma′ in the concentrated area were established. Low–frequency acoustic waves tend to extinguish the pool fire, and the increased distance of acoustic response and acoustic source power can promote flame extinction. A linear relationship between flame extinction and acoustic frequency was semi-quantitatively characterized by a modified Damköhler (Da*) number. Based on the critical oscillation–induced displacement for extinguishing a pool fire, local acoustic parameters were characterized, and a critical model for local displacement during the extinction of the pool fire was established.

Numerical and Experimental Determination of Selected Performance Indicators of the Liquid Flat-Plate Solar Collector under Outdoor Conditions

The paper proposes applying an in-house mathematical model of a liquid flat-plate solar collector to calculate the collector time constant. The described model, proposed for the first time in an earlier study, is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all collector components. The formulated differential equations are solved iteratively using an implicit difference scheme. To obtain a stable numerical solution, it is necessary to use appropriate steps of time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model’s practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. By comparing, on an ongoing basis, the measured and calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The simplicity of the model and the low computational demands enable such comparisons in an online mode.

Personalized Architectural Design Under the Cooperation of Parametric Modeling

Personalized Architectural Design Under the Cooperation of Parametric Modeling

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

Reliability of dynamic causal modelling of resting-state magnetoencephalography.

This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

Isothermal Amplification Using Temperature-Controlled Frequency Mixing Magnetic Detection-Based Portable Field-Testing Platform.

Sensitive magnetic nucleic acid (NA) detection via frequency mixing magnetic detection (FMMD) requires amplified NA samples for which a reliable temperature control is necessary. The feasibility of recombinase polymerase amplification (RPA) was studied within a newly integrated temperature-controlled sensor unit of a mobile FMMD based setup. It has been demonstrated that the inherently generated heat of the low frequency (LF) excitation signal of FMMD can be utilized and controlled by means of pulse width modulation (PWM). To test control performance in a point of care (PoC) setting with changing ambient conditions, a steady state and dynamic response model for the thermal behavior at the sample position of the sensor were developed. We confirmed that in the sensor unit of the FMMD device, RPA performs similar as in a temperature-controlled water bath. For narrow steady state temperature regions, a linear extrapolation suffices for estimation of the sample position temperature, based on the temperature feedback sensor for PWM control. For any other ambient conditions, we identified and validated a lumped parameter model (LPM) performing with high estimation accuracy. We expect that the method can be used for NA amplification and magnetic detection using FMMD in resource-limited settings.

Interpretable data-driven prediction methods for atmospheric chloride deposition rate

Precise estimation of atmospheric chloride deposition is crucial in environmental science, providing valuable insights into atmospheric pollution, metal corrosion, and the hydrologic cycle. The distribution of atmospheric chloride deposition involves multiple processes of production, transportation, and deposition, making it a problem with well-defined physical mechanisms. Although numerous parametric models have been proposed for atmospheric chloride distribution prediction, they present challenges in terms of generalizability and prediction accuracy due to simplifications of the holistic process. In contrast, non-parametric machine learning algorithms can analyze complex issues and have good prediction performance, but they are used less often in atmospheric chloride deposition modeling. Therefore, we develop a genetic algorithm optimized quantile regression forest (GA-QRF) model, incorporating environmental variables associated with the holistic deposition process to predict the distribution of atmospheric chloride deposition. QRF is an ensemble learning method known for delivering precise and interpretable predictions, coupled with effective uncertainty estimation. The proposed model results achieved an R2 value of 0.935 for median prediction and 98.2% coverage of the intervals for uncertainty quantification on a dataset collected from six countries. In addition, in order to verify the consistency of the black-box machine learning model with the domain knowledge, several interpretation methods are used to deconstruct the proposed model. Considering the spatial variability of atmospheric chloride deposition, the proposed model was further used to create deposition distribution maps.

Computational biomechanics for a standing human body: Modal analysis and simulation.

We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS-DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full-body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS-DYNA is used as the supercomputing and simulation platform. Four sets of low-frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low-frequency mode in modal analysis. Such a "walking mode" shows the in-phase and out-of-phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS-DYNA computer codes are also included for possible use by other researchers.

Semi-empirical model of the twin-screw refrigeration compressor with capacity control devices

Adopting twin-screw refrigeration compressors with capacity control devices has become the dominant trend to meet varying cooling demands. To analyze the compressor performance with different capacity control methods and further explore the potential, a semi-empirical model with 16 characteristic parameters is proposed. Detailed working processes under the part-load condition (PC) are considered and the corresponding calculations, including pressure loss, power loss, and leakage are innovatively refined. Subsequently, a prototype experiment was carried out for parameters identification and model validation. A good agreement between the experimental and simulated values is attained, with average relative errors of 3.46 % and 1.15 % for shaft power and mass flow rate under the variable frequency condition (VFC) and 3.18 % and 4.63 % under the PC. Thermodynamic simulation results indicate that both shaft power and mass flow rate are linearly correlated with frequency and the efficiencies are nearly stable over the whole interval. However, as the load decreases, the performance decays significantly and the most remarkable change occurs at a position of between 0.9 and 1. The volumetric and adiabatic efficiencies of the VFC are 3.65 and 2.26 times higher than that of the PC when the mass flow rate reaches the lower limit.

Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses

Abstract We consider continuous-time survival and event-history settings, where our aim is to graphically represent causal structures allowing us to characterize when a causal parameter is identified from observational data. This causal parameter is formalized as the effect on an outcome event of a (possibly hypothetical) intervention on the intensity of a treatment process. To establish identifiability, we propose novel graphical rules indicating whether the observed information is sufficient to obtain the desired causal effect by suitable reweighting. This requires a different type of graph than in discrete time. We formally define causal semantics for the corresponding dynamic graphs that represent local independence models for multivariate counting processes. Importantly, our work highlights that causal inference from censored data relies on subtle structural assumptions on the censoring process beyond independent censoring; these can be verified graphically. Put together, our results are the first to establish graphical rules for nonparametric causal identifiability in event processes in this generality for the continuous-time case, not relying on particular parametric survival models. We conclude with a data example on Human papillomavirus (HPV) testing for cervical cancer screening, where the assumptions are illustrated graphically and the desired effect is estimated by reweighted cumulative incidence curves.

The Distributed Parameter Model of an Electro-Pneumatic System Actuated by Pneumatic Artificial Muscles with PWM-Based Position Control

Today, the analysis and synthesis methods of electro-pneumatic systems with position control actuated by pneumatic artificial muscles (PAMs) are quite well known. In these methods, pneumatic artificial muscle is considered as an object with lumped parameters. However, the PAM is an object with distributed parameters, where the pressure, density, and mass flow rate of gas are varied along the bladder length. Thus, in the case of certain design parameters of the pneumatic artificial muscle and certain frequencies of the supply pressure, resonant gas oscillations affected by the wave processes in the bladder may occur. Thereby, in the PWM-driven PAM-actuated system, certain operation frequencies of the control pneumatic valve can cause oscillations of gas in the bladder and in the connected pipeline. These processes could lead to vibrations of the executive device. To solve this practical problem, a distributed parameter model of the PAM that takes into account the pressure fluctuations in the bladder and in the pipeline was elaborated. Also, in this work, a new method in which the wave processes are described by ordinary differential equations instead of partial differential equations was proposed.

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

A robust domain distribution alignment discriminative network driven by physical samples for rotor-bearing fault diagnosis

The diagnostic performance of most intelligent models depends on having a sufficient number of labeled samples, which are difficult to obtain in real-world scenarios. Physics-guided sample generation emerges as a promising remedy for addressing the scarcity of labeled samples during model training. However, existing studies have tended to overlook the coupling effect between the marginal and conditional distributions of physical and real samples, leading to domain confusion and a subsequent reduction in model generalization and diagnostic accuracy. Therefore, this study introduces a novel approach: the physically driven sample-based domain distribution alignment discriminative network (DDADN), which integrates dynamic modeling and domain adaptation. Specifically, a lumped parameter model is established to generate complete defect samples, subsequently optimized using the Pearson correlation coefficient (PCC). Next, an enhanced joint distribution adaptation (EJDA) mechanism is employed to harmonize distributions across domains, effectively aligning both overall and class-specific distributions within the domains and thus achieving adaptive domain confusion. Meanwhile, to ensure robust feature classification and extraction, an adaptive softmax (A-softmax) loss function is introduced. Finally, in unlabeled real operating conditions, the proposed multitask scheme adeptly transfers knowledge between physical and real signals, yielding impressive classification accuracies of 97.78% and 98.34% for the experimental and Case Western Reserve University (CWRU) datasets, respectively. These results underscore the superiority of the proposed scheme in terms of learning transferable features and maintaining high diagnostic accuracy and generalization capability.

Stealth Unmanned Aerial Vehicle Penetration Efficiency Optimization Based on Radar Detection Probability Model

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions.

Machine learning-based causal inference for evaluating intervention in travel behaviour research: A difference-in-differences framework

Causal inference with the difference-in-differences (DID) framework is popular in identifying causal effects with observational data and has started to be applied in recent travel behaviour studies. Most relevant transportation research adopts the conventional linear parametric DID model, which is known to be inflexible and restrictive. This study applies non-parametric DID estimators facilitated by machine learning (ML) models for causal inference in a variety of data scenarios. Semi-parametric and doubly robust estimators are established and integrated with the ML-based cross-fitting pipeline. Simulation studies and empirical case studies are conducted to showcase the ability of ML-based DID to detect causal effects from both simulated and real-world datasets. Results suggest that the proposed methods outperform conventional DID models in all data scenarios. Light working models are generally preferred over hyperparameter-dependent ones for their comparable performance, lower computational burden, and higher levels of compatibility to real-world empirical analysis. Empirical case studies also demonstrate how the proposed DID method could be applied to evaluate the impacts of various interventions on travel behaviour in different contexts. The present study adds to the existing travel behaviour literature by leveraging machine learning algorithms and non-parametric estimators to the impact evaluation of external interventions on travel characteristics and expanding the application of causal inference approaches in transportation research.

The effect of the Solar System Ephemeris on the search for the nano-Hz gravitational wave background

Abstract The detection of the nano-Hz gravitational-wave background (GWB) is one of the main targets of Pulsar timing arrays (PTAs). The detection can be achieved via searching for a common signal with quadrapolar correlation between pulsar pairs. Errors in the Solar-system ephemeris (SSE) can induce dipolar correlations in PTA data, which may affect the results of GWB searches, especially when the data quality is not high enough to constrain the correlation pattern. We investigate the effect of unmodelled SSE errors on GWB searches with PTAs, using simulations with properties based on the European Pulsar Timing Array dataset. When the GWB signal is strong, SSE errors have little effect on the GWB search results, including parameter inference and model selection. When the GWB signal is weak, SSE errors can lead to overestimation of the GWB amplitude. However, model comparison would show strong support for dipolar correction, which implies the source of the common signal to be SSE-related and helps avoid its misidentification as a GWB signal. This indicates that SSE error is unlikely to be the main source of the common signal detected recently with real PTA data. We also use simulations to test the ability of the SSE model LINIMOSS in absorbing SSE-error signals and leaving the GWB signal intact. We show that marginalizing LINIMOSS planetary parameters with infinite priors is good at absorbing SSE errors, but may also lead to improper absorption of a GWB signal. Caution is therefore required when setting appropriate limits on the priors of SSE parameters.

Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.

Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

ABSTRACT Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the “deformed state”, with their ideal configuration, reconstructed using VP algorithms, considered the “original state”. The outcomes of this comparison facilitate the investigation of the components’ structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.