Modelling

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city. Full article

A population balance model for the aggregation of iron oxide nanoflowers (IONfs) is presented. The model is based on the fixed pivot technique and is validated successfully for four kinds of aggregation kernels. The extended Derjaguin, Landau, Verwey, and Overbeek (xDLVO) theory is also employed for assessing the collision efficiency of the particles, which is pertinent to the total energy of the interaction. Colloidal stability experiments were conducted on IONfs for two dispersant cases—aqueous phosphate buffered saline solution (PBS) and simulated body fluid (SBF). Dynamic light scattering (DLS) measurements after 24-h of incubation show a significant size increase in plain PBS, whereas the presence of proteins in SBF prevents aggregation by protein corona formation on the IONfs. Subsequent simulations tend to overpredict the aggregation rate, and this can be attributed to the flower-like shape of IONfs, thus allowing patchiness on the surface of the particles that promotes an uneven energy potential and aggregation hindering. In silico parametric study on the effects of the ionic strength shows a prominent dependency of the aggregation rate on the salinity of the dispersant underlying the effect of repulsion forces, which are almost absent in the PBS case, promoting aggregation. In addition, the parametric study on the van der Waals potential energy effect—within common Hamaker-constant values for iron oxides—shows that this is almost absent for high salinity dispersants, whereas low salinity gives a wide range of results, thus underlying the high sensitivity of the model on the potential energy parameters. Full article

Indices of abundance are usually a key input parameter used for fitting a stock assessment model, as they provide abundance estimates representative of the fraction of the stock that is vulnerable to fishing. These indices can be estimated from catches derived from fishery-dependent sources, such as catch per unit effort (CPUE) and landings per unit effort (LPUE), or from scientific survey data (e.g., relative population number—RPN). However, fluctuations in many factors (e.g., vessel size, period, area, gear) may affect the catch rates, bringing the need to evaluate the appropriateness of the statistical models for the standardization process. In this research, we analyzed different generalized linear models to select the best technique to standardize catch rates of target and non-target species from fishery dependent (CPUE and LPUE) and independent (RPN) data. The examined error distribution models were gamma, lognormal, tweedie, and hurdle models. For hurdle, positive observations were analyzed assuming a lognormal (hurdle–lognormal) or gamma (hurdle–gamma) error distribution. Based on deviance table analyses and diagnostic checks, the hurdle–lognormal was the statistical model that best satisfied the underlying characteristics of the different data sets. Finally, catch rates (CPUE, LPUE and RPN) of the thornback ray Raja clavata, blackbelly rosefish Helicolenus dactylopterus, and common mora Mora moro from the NE Atlantic (Azores region) were standardized. The analyses confirmed the spatial and temporal nature of their distribution. Full article

Nowadays considering trends such as digitalization, automated driving as well as electric mobility in products in automotive development processes is a major challenge, which has led to an enormous increase in the number of product functions of technical systems. However, the recognized processes in automotive development are strongly component-oriented and such processes partially support the development of product functions. In order to meet future trends and ensure long term customer satisfaction, a transfer from component-oriented to function-oriented development is necessary. Accordingly, a holistic concept can be useful that enables the integration of customer feedback into the early phase of product development in the context of function-orientation. However, the customer feedback evaluation and their mapping with technical subsystems have been considered mainly in the context of component-oriented development. In this contribution, a method is proposed, which is generated in the context of a product model of product generation engineering. Product Generation Engineering enables the structuring of the development process of a product generation and supports function-oriented development. The Product Model provides customer- oriented development of mechatronic products. The proposed method is achieved in the sense of model-based systems engineering and validated by the exemplarily application of a case study of a specific vehicle. Both the past and current product generations of the specific vehicle are taken into account in the development of the subsequent product generation. Full article

In recent years, bounded distributions have attracted extensive attention. At the same time, various areas involve bounded interval data, such as proportion and ratio. In this paper, we propose a new bounded model, named logistic Truncated exponential skew logistic distribution. Some basic statistical properties of the proposed distribution are studied, including moments, mean residual life function, Renyi entropy, mean deviation, order statistics, exponential family, and quantile function. The maximum likelihood method is used to estimate the unknown parameters of the proposed distribution. More importantly, the applications to three real data sets mainly from the field of engineering science prove that the logistic Truncated exponential skew logistic distribution fits better than other bounded distributions. Full article

Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions. Full article

As the offshore wind industry develops, more lease sites in the intermediate water depth (50–85 m) are being released to developers. In these water depths floating wind turbines with chain catenary systems and fixed-bottom turbines with jacketed structures become cost prohibitive. As such, industry and researchers have shifted focus to floating turbines with taut or semi-taut synthetic rope mooring systems. In addition to reducing the cost of the mooring systems, synthetic systems can also reduce the footprint compared to a chain catenary system which frees areas around the turbine for other maritime uses such as commercial fishing. Both the mooring systems component cost and footprint are pertinent design criteria that lend themselves naturally to a multi-objective optimization routine. In this paper a new approach for efficiently screening the design space for plausible mooring systems that balance component cost and footprint using a multi-objective genetic algorithm is presented. This method uses a tiered-constraint method to avoid performing computationally expensive time domain simulations of mooring system designs that are infeasible. Performance metrics for assessing the constraints of candidate designs are performed using open-source software such as Mooring Analysis Program (MAP++), OpenFAST and MoorDyn. A case study is presented providing a Pareto-optimal design front for a taut synthetic mooring system of a 6-MW floating offshore wind turbine. Full article

In this paper, we present a transition journey of automotive software architecture design from using legacy approaches and toolchains to employing new modeling capabilities in the recent releases of Matlab/Simulink (M/S). We present the seamless approach that we have employed for the software architecture modeling of a mixed-critical electric powertrain controller which runs on a multi-core hardware platform. With our approach, we can achieve bidirectional traceability along with a powerful authoring process, implement a detailed model-based software architecture design of AUTOSAR system including a detailed data dictionary, and carry out umpteen number of proof-of-concept studies, what-if scenario simulations and performance tuning of safety software. In this context, we discuss an industrial case study employing valuable lessons learned, our experience reports providing novel insights and best practices followed. Full article

The simulation of wave propagation and penetration inside ports and coastal areas is of paramount importance to engineers and scientists desiring to obtain an accurate representation of the wave field. However, this is often a rather daunting task due to the complexity of the processes that need to be resolved, as well as the demanding levels of required computational resources. In the present paper, the enhancements made on an existing sophisticated Boussinesq-type wave model, concerning the accurate generation of irregular multidirectional waves, as well as an empirical methodology to calculate wave overtopping discharges, are presented. The model was extensively validated against 4 experimental test cases, covering a wide range of applications, namely wave propagation over a shoal, wave penetration in ports through a breakwater gap, wave breaking on a plane sloping beach, and wave overtopping behind breakwaters. Good agreement of the model results with all experimental measurements was achieved, rendering the wave model a valuable tool in real-life applications for engineers and scientists desiring to obtain accurate solutions of the wave field in wave basins and complex coastal areas, while keeping computational times at reasonable levels. Full article

A rapid-response Lagrangian model for the use in simulation of the transport of a chemical pollutant in the Arabian/Persian Gulf is described. The model is well suited to the provision of a fast response after an emergency due to an accident or a deliberate spill. It is easy to set up for any situation since only requires the modification of a few input files specifying the pollutant properties and release characteristics. Running times are short, even on a desktop PC, which makes it appropriate for a rapid assessment of a hypothetical accident occurring in the region. Baroclinic circulation was obtained from an HYCOM ocean model, and tides were calculated using a barotropic model. The interactions of pollutants with sediments (uptake/release processes) were described using a dynamic approach based on kinetic transfer coefficients and a stochastic numerical method. Some examples of model applications are shown, showing the influence of the geochemical behaviour of the pollutant in its distribution patterns. Full article

Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distributed fixed stations. The work in this paper aims at improving the situation by utilizing machine learning models to process the outputs of multi-sensor devices that are small, cheap, albeit less reliable, thus a massive urban deployment of those devices is possible. The main contribution of the paper is the design of a mathematical model providing sensor fusion to extract the information and transform it into the desired pollutant concentrations. Multi-sensor outputs are used as input information for a particular machine learning model trained to produce the CO, NO2, and NOx concentration estimates. Several state-of-the-art machine learning methods, including original algorithms proposed by the authors, are utilized in this study: kernel methods, regularization networks, regularization networks with composite kernels, and deep neural networks. All methods are augmented with a proper hyper-parameter search to achieve the optimal performance for each model. All the methods considered achieved vital results, deep neural networks exhibited the best generalization ability, and regularization networks with product kernels achieved the best fitting of the training set. Full article

Using moderate electric field (MEF) techniques, Ohmic heating (OH) provides the rapid and uniform heating of food products by applying electric fields to them. A range of theoretical Ohmic heating models have been studied by researchers, but model validation and comparisons using experimental data and model development using system identification techniques from experimental data have not been evaluated. In this work, numerical models, mathematical models, and system identification models for an MEF process were developed. The MEF models were developed and simulated using COMSOL and MATLAB/Simulink software. When simulated, the developed models showed a volumetric rise in the overall food temperature. It was found that upon the application of an electric field, the resultant temperature depends on the electrical conductivity, product temperature, and magnitude of the electric field. For this reason, a systematic approach was used to validate the developed models. Experimental data derived from a commercially available batch Ohmic heater from C-Tech Innovation were used to validate the simulated models. Validation, analysis, and model comparison were conducted to compare developed models with experimental data. The validated simulated model helped improve the understanding of the effect of different critical process parameters of foods with a range of initial conditions. The validated model could accurately predict the temperature of heating under varying electric fields and food products with different thermo–physical properties. Full article

Numerical models are useful tools for studying complex wave–wave and wave–current interactions in coastal areas. They are also very useful for assessing the potential risks of flooding, hydrodynamic actions on coastal protection structures, bathymetric changes along the coast, and scour phenomena on structures’ foundations. In the coastal zone, there are shallow-water conditions where several nonlinear processes occur. These processes change the flow patterns and interact with the moving bottom. Only fully nonlinear models with the addition of dispersive terms have the potential to reproduce all phenomena with sufficient accuracy. The Boussinesq and Serre models have such characteristics. However, both standard versions of these models are weakly dispersive, being restricted to shallow-water conditions. The need to extend them to deeper waters has given rise to several works that, essentially, add more or fewer terms of dispersive origin. This approach is followed here, giving rise to a set of extended Serre equations up to kh ≈ π. Based on the wavemaker theory, it is also shown that for kh > π/10, the input boundary condition obtained for shallow-waters within the Airy wave theory for 2D waves is not valid. A better estimate for the input wave that satisfies a desired value of kh can be obtained considering a geometrical modification of the conventional shape of the classic piston wavemaker by a limited depth $\theta h$, with $\theta \le$ 1.0. Full article

Textual editors are omnipresent in all software tools. Editors provide basic features, such as copy-pasting and searching, or more advanced features, such as error checking and text completion. Current technologies in model-driven engineering can automatically generate textual editors to manipulate domain-specific languages (DSLs). However, the customization and addition of new features to these editors is often limited to changing the internal structure and behavior. In this paper, we explore a new generation of self-descriptive textual editors for DSLs, allowing full configuration of their structure and behavior in a convenient formalism, rather than in source code. We demonstrate the feasibility of the approach by providing a prototype implementation and applying it in two domain-specific modeling scenarios, including one in architecture modeling. Full article

This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach, which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires us to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and trigger the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution. Full article

The behaviour of steel structures is affected by two nonlinearities—the geometric and material nonlinearity—and by the unavoidable presence of imperfections. To evaluate the ultimate capacity of a structure, these effects should be taken into consideration during the design process, either explicitly in the analysis or implicitly through the verification checks. In this context, Eurocode 3 provides several design approaches of different complexity and accuracy. The advantages and disadvantages of these approaches are discussed. Five different methods in conformity with the Eurocode provisions are applied for the design of four moment resisting steel frames of varying slenderness. The influence of nonlinearities and imperfections in respect to the slenderness of the structure is illustrated. The examined methods are compared in terms of the predicted ultimate capacity and their efficiency is assessed against the most accurate between them, i.e., an advanced geometrically and materially nonlinear analysis. It is shown that considerable differences arise between the methods. Nevertheless, except for the commonly used 2nd order analysis followed by cross-section verifications, the remaining methods are mostly on the safe side. Full article

In this paper, different optimization techniques such as multi-objective optimization based on ratio analysis, the -nD angle, information divergence and multi-angle optimization methods were considered for comparative study in order to check their variation in results. These four techniques were considered asthese techniques were made from completely different methodologies, such as MOORA, is one of the latest multi-criteria decision-making methods that cover the weakness of other older methods and the latter three (-nD angle, Information Divergence and MAOT) methods are based on trigonometric methodologies. These optimization techniques were carried out using the effect of EDM process parameters viz., material removal rate and surface roughness as output parameters and current, pulse on time, pulse off time and different electrode materials as input process parameters. These parameters were obtained from the experiments modelled according to the Taguchi experimental design procedure in which the L₁₈ orthogonal array was used to perform experimentation with the material of titanium alloy (Ti-6Al-4V). All the optimization techniques have obtained similar results among which -nD angle and Information Divergence technique tend to be easier and understandable for any similar application. The latter two methods avoid complexity and can be used for optimizing any relevant manufacturing process parameters such as the parameters of EDM, additive manufacturing, etc. Full article

A simple underwater noise model suitable for use with explosive severance of well conductors and piles during the decommissioning of oil and gas subsea structures is introduced and evaluated against data from five projects in the US. This study focuses on a novel model for the determination of sound exposure levels. The model has been developed to enable determination of impact areas for marine mammals and fish. Simulated received underwater sound exposure levels were significantly correlated with measurements for all scenarios. The maximum total error achieved between simulations and measurements was 2.6%, suggesting that predictions are accurate to within 3% of the average measurement. A low relative bias was observed in the simulations when compared to measured values, suggesting only a small systematic underestimate (≤0.5% of average measurement) for most severance operations and a small overestimate (0.14%) for open water blasts. Full article

A simple underwater noise model suitable for use with explosives in the decommissioning of oil and gas subsea structures is introduced and evaluated against data from five projects in the US. The performance of the model is compared to four existing models for open water blasts, and for the severance of well conductors and piles. Simulated received underwater sound pressure levels were significantly correlated with measurements for all scenarios. The maximum total error achieved between simulations and measurements was 3.5%, suggesting that predictions are accurate to within 4% of the average measurement. A low relative bias was observed in the simulations when compared to measured values, suggesting only a small systematic underestimate (≤1% of average measurement) for most severance operations and a small overestimate (1.34%) for open water blasts. Full article