Proposed Modeling Approach Research Articles

Business models for local energy market and circular economy establishment in municipalities: A case study in an Austrian municipality

Energy communities often lack authority for establishment and operation management. Municipal authorities could take the role of such community operators. Therefore, Local Sustainable Municipalities are introduced, providing a local energy market on the municipal level with inclusion of sustainable resource utilization. The analyses include examinations of the scope of local markets in a municipality, portfolio investigations on different waste treatment plants and greywater system installation analyses. Furthermore, different adopted municipality strategies and their impact on municipal portfolio and market operation are examined. A clustering-based optimization framework for portfolio and market optimization is developed to perform these analyses. The proposed modeling approach leads to a significant model size reduction compared with hourly data optimization while providing location determination and portfolio estimation. The results indicate energy-sharing differences between municipal markets and local markets in energy communities. Decentralized energy provision is similar to centralized energy provision but on the municipal level. Furthermore, results show that waste incineration energy recovery can provide dispatchable low-emission energy with a high level of energy security and should be supported until the energy transition is more advanced. Finally, results on local strategies show that specific municipal goals always lead to increased costs for the municipality.

Techno-eco-environmental analysis of a waste-to-energy based polygeneration through hybrid renewable energy system

Dumping of solid waste and its open burning have not just polluted the earth but also have made a loss of potential energy contained in it. The proposed study aims to conceptualize the design and analysis of a Waste-to-Energy based polygeneration renewable energy system with no outage in order to increase social hygiene at the lowest economy, environment-friendly, and energy efficient. The optimization of system is done with the help of HOMER@ Pro software. The result shows that the optimal solution can supply 59,330 kWh/year of electricity, 17,842 kWh/year of heat with 19.89 °C increase in temperature (used for Space heating), 57.51 kg/day cooking gas, 269.31 kg/day transport fuel (biogas), and 1395 kg/day compost with cost of energy Rs. 5.49/kWh and net present cost of Rs. 38.87 lakh. The emission analysis shows that the system generates only 5469 kg/year of CO2 emission and avoids 40,772 kg/year of CO2 emission, earns yearly carbon credit of 40.7 units. The sensitivity analysis shows that the increase in the capacity shortage decreases reliability and the economic factors except for O&M cost and increases the renewable energy factor and the absence of continuous energy supply. Moreover, the proposed model approaches sustainable development goal 6 & 7.

Prediction of Progressive Frost Damage Development of Concrete Using Machine-Learning Algorithms

Understanding the deterioration mechanism of concrete structures in cold climates that are susceptible to frost damage from repeated freezing and thawing cycles is imperative for ensuring their durability and serviceability. This study analyzed the impact of freeze–thaw (FT) exposure on concrete structural behavior by developing three machine-learning approaches—artificial neural networks (ANN), random forests (RF), and support vector machines (SVM)—to quantify the progressive loss in compressive strength after repeated FT cycles. The results demonstrate that all of the proposed models can predict the deteriorated compressive strength of concrete and align closely with the experimental results. The ANN model demonstrated the highest prediction accuracy with an R2 of 0.924, exhibiting a higher prediction accuracy than RF and SVM models. Sensitivity analysis using Shapley additive explanations (SHAP) revealed that concrete with an initially high strength, along with a lower water–cement ratio and air entrainment, exhibited the least reduction in compressive strength after freezing–thawing cycles, underlining the positive impact of these factors on the FT durability of concrete. The proposed modeling approach accurately predicts the residual compressive strength after FT exposure, enabling the selection of optimal concrete materials and structural designs for cold climates.

A smart component model replacement approach for refined simulation of large nonlinear RC structures

This paper proposes a novel smart component model replacement (SCMR) approach that is not only efficient for large-scale nonlinear structural analysis, but also accurate in the refined simulation of components of interest. The method creates a FE model with a relatively coarse mesh to simulate the entire structure. During analysis, when a component is identified to predefined criteria defined by the users, its model will be replaced by another more refined “substructure” model that can simulate the damage and failure more accurately. A few “replacement” strategies are presented for smoothly transiting between different models: (1) A gradually transiting strategy is presented to avoid the possible jumps of responses due to the model differences to guarantee numerical stability; (2) The historical boundary conditions are applied to the substructure model to address the historical dependence of responses; (3) The boundary connection between main and substructure is considered by using a flexible client/server technology, to simplify the replacement. The SCMR approach is verified by three application examples. The results demonstrate that the proposed modeling approach can efficiently perform large-scale nonlinear structural analysis, while capturing the failure mechanism in RC structures well.

On evolving network models and their influence on opinion formation

In this paper, we propose a new model for continuous time opinion dynamics on an evolving network. As opposed to existing models, in which the network typically evolves by discretely adding or removing edges, we instead propose a model for opinion formation which is coupled to a network evolving through a system of ordinary differential equations for the edge weights. We interpret each edge weight as the strength of the relationship between a pair of individuals, with edges increasing in weight if pairs continually listen to each other’s opinions and decreasing if not. We investigate the impact of various edge dynamics at different timescales on the opinion dynamics itself. This is done partly through analytic results and partly through extensive numerical simulations of two case studies: one using bounded confidence interaction dynamics in the opinion formation process (as in the classical Hegselmann–Krause model) and one using an exponentially decaying interaction function. We find that the dynamic edge weights can have a significant impact on the opinion formation process, since they may result in consensus formation but can also reinforce polarisation. Overall, the proposed modelling approach allows us to quantify and investigate how the network and opinion dynamics influence each other.

Fast and efficient inversion methods for crack sizing using Bz magnetic signature

AbstractFast estimation is a critical feature of the proposed modeling approach as a “forward” fast solver that has a key role in solving the “inverse” problem involved in crack sizing, especially for real‐time applications. To this end, two different inversion methods have been proposed to estimate the crack depth. The first one uses an interpolation model of the 3D direct model simulation data. The second approach is based on artificial neural networks (ANN). In this article, the rotating uniform eddy current (RUEC) probe is used to detect the normal magnetic component Bz which is defined as the characteristic signal for reconstructing the crack length and depth concurrently. Using the first approach, The Bz characteristic 3D surface is presented, and this can be modeled by a fitted polynomial interpolation equation. Thus, the crack depth can be inverted using this equation referring to the experimental or simulated Bz signal and the crack length. The ANN is outlined to determine the crack depth based on the simulated Bz signature. The move from the first inversion method to the second was flexible and useful, in which the interpolation model is used as a defect signal generator for fast building of a large and efficient database for ANN training. Both of the proposed methods proved the objectivity and accuracy of the inversion results and offered more robust engineering support for automated NDT, reducing production time and increasing productivity.

Integrated tire wear buildup and rainfall-runoff model to simulate tire wear particles in stormwater

This paper presents an approach to integrate tire wear buildup and rainfall-runoff models to simulate tire wear buildup on road surfaces and its subsequent transport in stormwater runoff events. To do so, a buildup model is presented based on vehicle kilometers traveled, vehicle type, vehicle speed, and road roughness within a watershed. This buildup model was integrated into an EPA SWMM model that simulated the runoff of tire wear particles in twelve watersheds in the San Francisco, CA bay area. Results demonstrate that tire wear particle buildup within the watersheds ranged between 0.4 and 0.51 (kg/km2) per hour. Applied to the SWMM model, total event mean tire wear concentrations ranged between 0.5 and 67 μg/L. These concentrations were linearly correlated to depth-integrated samples collected at the outlet of each of the watersheds (R2 = 0.66). The proposed modeling approach can ultimately be applied to create solutions to an emerging stormwater contaminant.

An experimentally validated anisotropic visco-hyperelastic constitutive model for knitted fabric-reinforced elastomer composites

A new anisotropic visco-hyperelastic constitutive model has been developed to predict the time-dependent deformation response of flexible, knitted fabric-reinforced elastomer composites. These composites exhibit a time-dependent response due to the viscoelastic behavior of the elastomer, while the anisotropic reinforcement effect is attributed to the knitted fabrics. The proposed modeling approach features an invariant-based strain energy density function that incorporates the architecture of knitted fabrics and the hyper-viscoelastic behavior of elastomers. In this study, the elastomer is considered as a viscous neo-Hookean material, and the knitted fabrics are modeled as cords with negligible stiffness in bending. The proposed material model is implemented in commercial Finite Element Analysis (FEA) software, Abaqus (version 2017), through a user-defined material subroutine, UMAT. This approach enables the determination of the constitutive behavior of the composite based on the constituent elastomer, fiber properties, and fabric architectures. The proposed model is verified through a discrete 3D FEA model, in which the elastomer and fabric reinforcement are modeled explicitly. The approach is further validated through physical testing including uniaxial tension, torsion, and three-point bending. Since the proposed strain energy-based constitutive model can predict the deformation response of knitted fabric-reinforced elastomer composites based on the fabric architecture, it can be employed as an efficient modeling tool to aid the design of knitting pattern for target mechanical properties.

Predicting sample injection profiles in liquid chromatography: A modelling approach based on residence time distributions

The pharmaceutical and bio-pharmaceutical industries rely on simulations of liquid chromatographic processes for method development and to reduce experimental cost. The use of incorrect injection profiles as inlet boundary condition for these simulations may, however, lead to inaccurate results. This study presents a novel modelling approach for accurate prediction of injection profiles for liquid chromatographic columns. The model uses the residence time distribution theory and accounts for the residence time of the sample through the injection loop, connecting tubes and heat exchangers that exist upstream of the actual chromatographic column, between the injection point and the column inlet. To validate the model, we compare simulation results with experimental injection profiles taken from the literature for 20 operating conditions. The average errors in the predictions of the mean and variance of the injection profiles result to be 8.98% and 8.52%, respectively. The model, which is based on fundamental equations and actual hardware details, accurately predicts the injection profile for a range of sample volumes and sample loop-filling levels without the need of calibration. The proposed modelling approach can help to improve the quality of in-silico simulation and optimization for analytical chromatography.

Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques

Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.

Data-Driven-Based Vector Space Decomposition Modeling of Multiphase Induction Machines

For contemporary variable-speed electric drives, the accuracy of the machine's mathematical model is critical for optimal control performance. Basically, phase variables of multiphase machines are preferably decomposed into multiple orthogonal subspaces based on vector space decomposition (VSD). In the available literature, identifying the correlation between states governed by the dynamic equations and the parameter estimate of different subspaces of multiphase IM remains scarce, especially under unbalanced conditions, where the effect of secondary subspaces sounds influential. Most available literature has relied on simple RL circuit representation to model these secondary subspaces. To this end, this paper presents an effective data-driven-based space harmonic model for n-phase IMs using sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover the IM governing equations. Moreover, the proposed approach is computationally efficient, and it precisely identifies both the electrical and mechanical dynamics of all subspaces of an IM using a single transient startup run. Additionally, the derived model can be reformulated into the standard canonical form of the induction machine model to easily extract the parameters of all subspaces based on online measurements. Eventually, the proposed modeling approach is experimentally validated using a 1.5 Hp asymmetrical six-phase induction machine.

Lumped plasticity model for simulating the inelastic earthquake response of CFT columns

A simplified approach for simulating the seismic behavior of concrete-filled steel tube (CFT) columns using a lumped plasticity modeling approach based on a nonlinear spring model is proposed in this paper. Model parameters were calibrated considering plastic deformation, load–deformation during unloading and reloading cycles, and material strength degradation using an experimental database comprising 123 CFT column specimens (47 circular and 76 rectangular). 80% of the calibrated samples were used to develop predictive equations for the model parameters based on six input variables using multiple linear regression. The resulting equations were verified by the remaining 20% of all specimens; the verification results agreed with the experimental data and demonstrated better results compared to that of an existing fiber-based beam-column model. The proposed modeling approach was applied to model three frames with different stories (three, six, and nine stories) to perform nonlinear dynamic analyses. Then, their fragility curves were developed and compared with the curves obtained using existing fiber-based frame models. The proposed modeling approach increased the seismic vulnerability of the frames and reduced the analysis computational time.

A network-level management system to mitigate the global warming potential of road pavements

ABSTRACT This study details a new, network-level optimization tool aimed at supporting transportation agencies in their efforts to reduce the global warming potential of their road pavement infrastructure. Through a two-stage bottom-up algorithm that integrates with a comprehensive cradle-to-grave life cycle assessment, the proposed tool learns optimal management policies for individual pavement sections and uses that information to guide network-level allocation choices. Through a realistic case study based on data made available by a state department of transportation, this study demonstrates that the proposed modelling approach identifies management strategies expected to reduce the global warming potential of a pavement network by up to 4.8% over 20 years relative to a more traditional, reactive management approach. The resulting model presented in this paper can support agencies in achieving ambitious targets to reduce the global warming potential of their paved infrastructure systems.

Simulation of Welding Residual Stresses in Large Structures – First Results of Thermal Analyses

AbstractSimulation of welding residual stresses and distortions has been applied mostly for small‐scale components, smaller than those applied in civil engineering structures, due to the high requirements in computational capacities. In the present study, work hypotheses, which can reduce the computational load of welding simulations for an accepted sacrifice in accuracy, are discussed. A series of thermal analyses is carried out, in order to check the validity of the proposed modelling approaches, which refer to the application of simple weld heat source models and hybrid shell‐solid element models. Application of local‐global models is also discussed. The present study aims to work as a first step towards the establishment of an engineering approach for the simulation of welds in civil engineering structures with sufficient accuracy and reduced requirements for computational capacity.

Failure analysis of prestressed concrete containment vessels under internal pressure considering thermomechanical coupling

After a loss of coolant accident (LOCA) in the prestressed concrete containment vessels (PCCVs) of nuclear power plants, the coupling of temperature and pressure can significantly affect the mechanical properties of the PCCVs. However, there is no consensus on how this coupling affects the failure mechanism of PCCVs. In this paper, a simplified finite element modeling method is proposed to study the effect of temperature and pressure coupling on PCCVs. The experiment results of a 1:4 scale PCCV model tested at Sandia National Laboratory (SNL) are compared with the results obtained from the proposed modeling approach. Seven working conditions are set up by varying the internal and external temperatures to investigate the failure mechanism of the PCCV model under the coupling effect of temperature and pressure. The results of this paper demonstrate that the finite element model established by the simplified finite element method proposed in this paper is highly consistent with the experimental results. Furthermore, the stress-displacement curve of the PCCV during loading can be divided into four stages, each of which corresponds to the damage to the concrete, steel liner, steel rebar, and prestressing tendon. Finally, the failure mechanism of the PCCV is significantly affected by temperature.

Modelling the continuum of serious traffic injuries in police-hospital linked data by applying the random parameters hazard-based duration model

Injury severity in police crash reports is usually recorded in three to five classes, including property damage, slight, moderate, serious, and fatal injuries. Among these classifications, serious injuries are commonly classified as cases where a road user is admitted to a hospital. In this classification, the length of hospital stay is not differentiated, whether one day or ten days, as long as the road user has been admitted to the hospital. As such, the inferences drawn from assuming that all serious injuries (1 if a road user is admitted to the hospital; 0 otherwise) are at the same severity level inherently suffer from aggregation bias and may not provide a thorough understanding of this severity category. This study proposes a hazard-based duration modelling approach to examine the severity of serious injury crashes measured in a continuous spectrum. Specifically, using the length of hospital stay as the measure of serious injuries, a random parameters hazard-based duration model with heterogeneity in means was applied to model serious injury crashes obtained by linking crash records in police and hospital databases. To address temporal instability, the injury records sources from Abu Dhabi, United Arab Emirates (UAE), between 2015 and 2019 were modelled separately for each year. The results showed that factors positively associated with more serious injury severity (prolonged length of hospital stay) are rural areas, high posted speed limits of 100–160 km/h, overturned crashes, speeding, impaired driving, involvements of a heavy vehicle, nighttime crashes, lack of restraint usage, and injuries to the head or lower extremities. In particular, speeding violations during nighttime are positively associated with more serious injuries. Furthermore, the means of the random parameters of head injury are positively influenced by speeding, lack of restraint usage, and motorcycle involvement through the heterogeneity-in-mean specification of the hazard-based duration model. The proposed modelling approach to model serious traffic injuries using a hazard-based duration model provides a comprehensive understanding of the factors associated with serious injuries.

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions

Hydrated electrons (eaq−) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq−), while the same OCs demonstrate notable discrepancies in keaq− values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq− and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq− values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon–carbon double bonds, and molecular topology on the degradation of OCs by eaq−. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains.

Modelling Stormwater Runoff Changes Induced by Ground-Mounted Photovoltaic Solar Parks: A Conceptualization in EPA-SWMM

A modelling framework for the simulation of stormwater runoff in ground-mounted photovoltaic solar parks is proposed. Elements in the solar park and their mutual interactions during precipitation events are conceptualized in EPA-SWMM. We demonstrate the potential of the framework by exploring how different factors influence runoff formation. Specifically, we carry out simulations for different sizes of the installation, soil types and input hyetographs. We also show the effect of ground cover, by changing the surface roughness. Outflow discharge from the park is compared to that from a reference catchment to evaluate variations of peak flow and runoff volume. Results highlight no practical changes in runoff in the short term after installation. However, in the long term, modifications in soil cover may lead to some potential increase of runoff. For instance, increments of the peak flow from the solar park up to 21% and 35% are obtained for roughness coefficient reductions of 10% and 20%, respectively. The proposed modelling approach can be beneficial for studying hydrological impacts of solar parks and thus for planning measures for their mitigation.

Understanding groundwater storage and drainage dynamics of a high mountain catchment with complex geology using a semi-distributed process-based modelling approach

Mountainous groundwater systems are usually characterized by great geological complexity and highly heterogeneous hydraulic properties. For that reason, proper system characterization, monitoring and modelling remain challenging. In this study, we investigated a geologically complex alpine catchment (from 1400 to 2900 m asl) in the Dolomites (Italian Alps) by combining hydrogeological field investigation, hydrological monitoring and modelling. A semi-distributed process-based hydrological model was applied to simulate the continuous measured catchment discharge in a period of three years, which covers a large variation of hydrodynamic conditions. The model structure couples the sequential hydrogeological units within the studied catchment: (1) the fractured dolomitic rocks as bedrock aquifer and 2) the unconsolidated deposits accumulating on the slopes and at the valley floor as porous aquifer. In order to evaluate the model structure and parameterization in depth, we applied a multi-step evaluation approach considering both parameter sensitivity and uncertainty. The modelling results demonstrate that the newly developed model can reproduce most discharge behavior of aquifers. The model indicates a seasonal dynamic linkage between surface and subsurface storage units during different flow conditions. Besides the matrix and conduit flow in fractured dolomitic aquifer, it highlights the important role of unconsolidated sediments (porous aquifer) to the storage and discharge behavior of the entire groundwater system. Furthermore, with the comprehensive model evaluation we learned the model performance deficit during extreme high flow conditions and proposed a more detailed hydrogeological conceptual model. We believe that the proposed modelling approach can be transferred to other high mountain catchments with similar hydro(geo)logical characteristics to characterize the collective behavior of aquifer systems, explore conceptual model weakness and optimize catchment monitoring work.

A step towards digital operations—A novel grey-box approach for modelling the heat dynamics of ultra-low temperature freezing chambers

Ultra-low temperature (ULT) freezers store perishable bio-contents and have high energy consumption, which highlight a demand for reliable methods for intelligent surveillance and smart energy management. This study introduces a novel grey-box modelling approach based on stochastic differential equations to describe the heat dynamics of the ULT freezing chambers. The proposed modelling approach only requires temperature data measured by the embedded sensors and uses data from the regular operation periods for model identification. The model encompasses three states: chamber temperature, envelope temperature, and local evaporator temperature. Special attention is given to the local evaporator temperature state, which is modelled as a time-variant system, to characterise the time delay and dynamic variations in cooling intensity.The model has three states, of which a time-variant model with nonlinear input for the local evaporator temperature state is specifically established to adapt to the variation of the cooling intensity at the position of the embedded chamber control probe. Two ULT freezers with different operational patterns are modelled. The unknown model parameters are estimated using the maximum likelihood method. The results demonstrate that the models can accurately predict the chamber temperature measured by the control probe (RMSE <0.19°C) and are promising to be applied for forecasting future states. In addition, the model for local evaporator temperature can effectively adapt to different operational patterns and provide insight into the local evaporation cooling supply status. The proposed approach greatly promotes the practical feasibility of grey-box modelling of the heat dynamics for ULT freezers and is a step forward in future digital operationscan serve several potential digital applications. A major limitation of the modelling approach is the low identifiability, which can potentially be addressed by inferring model parameters based on relative parameter changes.