Modelling, Volume 2, Issue 2 (June 2021) – 9 articles

The wind energy industry showed rapid growth in the past decade, pushing designs to the physical limits. In the last few years, the exponential growth of the wind turbine sizes capped, and the performance upgrades are reached with optimization processes. The first wave was on major parts, but with time advancing the “cost out” strategies are pushed to minor components. A major problem is service costs and the continuous search for missing spare parts in the market. The main aim of this study is to identify when is the best entry point for the additive manufacturing (AM) technology by the hydraulic manufacturer wind turbine companies. From the commercial application for expensive prototypes, it has evolved to economical home use applications. The newly available machines allow printing parts with competing precision to machining equivalents. The material selections range from plastics to metals with mechanical properties equally good or better. This project aims to provide a comprehensive review of the implementation of AM for hydraulic systems in wind turbines. Application screening was done by desk research and on AM technologies. Scientific research has been found on the topic for benchmarking, viability, and cost models. It has been found that there are still missing data for the mechanical properties of the available materials. The result of the decision-weighted matrix shows that the business could gain a competitive advantage by the AM implementation in terms of resources savings and productivity. Although from the technological and market perspective it is justified to initiate before further action the business should review its organization viability. Full article

Pressurized irrigation distribution systems (PIDSs) play a vital role in irrigation intensification, especially in the Mediterranean region. The design, operation and management of these systems can be complex, as they involve several intertwined processes which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested. This DSS has been developed to bear in mind the needs of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans which provide the best services to farmers. Hence, four modules were integrated in DESIDS: (i) irrigation demand and scheduling module; (ii) hydraulic analysis module; (iii) operation and management module; and (iv) design and rehabilitation module. DESIDS was tested on different case studies, proving itself a valuable tool for irrigation district managers, as it provides a wide range of decision options for the proper operation and management of PIDSs. The developed DSS can be used as a platform for future integrations and expansions, and to include other processes needed for better decision-making support. Full article

The goal of a Home Energy Management System (HEMS) is that of purposely shaping the cumulative energy consumption curves of domestic appliances by imposing suitable monitoring and control policies. The development of HEMS, like the development of general Cyber-Physical Systems (CPSs), is challenging, as it requires the exploitation of suitable methodological approaches which are able to deal jointly with the continuous and discrete behaviours of a CPS. In this paper, a methodological approach for HEMS is advocated which relies on the use of the Theatre actor system with hybrid actors. As a key feature, Theatre enables the same actor model to be used during the analysis, design, prototyping and implementation phases of the system. For property assessment, a Theatre model is reduced to Uppaal hybrid timed automata for analysis by statistical model checking. As a significant modelling example, a HEMS is proposed which implements an admission control strategy able to maintain the in-home energy consumption under a given threshold. Instead of reacting to an overload condition, the strategy is able to prevent an overload upfront by predicting the effect that the admission of a new load will have on the consumption curve of the whole system. Full article

The electrification of the powertrain requires enhanced performance of lithium-ion batteries, mainly in terms of energy and power density. They can be improved by optimising the positive electrode, i.e., by changing their size, composition or morphology. Thick electrodes increase the gravimetric energy density but generally have an inefficient performance. This work presents a 2D modelling approach for better understanding the design parameters of a thick LiFePO₄ electrode based on the P2D model and discusses it with common literature values. With a superior macrostructure providing a vertical transport channel for lithium ions, a simple approach could be developed to find the best electrode structure in terms of macro- and microstructure for currents up to 4C. The thicker the electrode, the more important are the direct and valid transport paths within the entire porous electrode structure. On a smaller scale, particle size, binder content, porosity and tortuosity were identified as very impactful parameters, and they can all be attributed to the microstructure. Both in modelling and electrode optimisation of lithium-ion batteries, knowledge of the real microstructure is essential as the cross-validation of a cellular and lamellar freeze-casted electrode has shown. A procedure was presented that uses the parametric study when few model parameters are known. Full article

High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian networks—a type of probabilistic network for reasoning under uncertainty—have proven to be a reliable and robust technique for the modeling and risk assessment of domino effects. In the present study, applications of Bayesian networks to modeling and safety assessment of domino effects in petroleum tank terminals has been demonstrated via some examples. The tutorial starts by illustrating the inefficacy of event tree analysis in domino effect modeling and then discusses the capabilities of Bayesian network and its derivatives such as dynamic Bayesian network and influence diagram. It is also discussed how noisy OR can be used to significantly reduce the complexity and number of conditional probabilities required for model establishment. Full article

This paper explores the possibility and plausibility of developing a hybrid simulation method combining agent-based (AB) and system dynamics (SD) modeling to address the case study of produced water management (PWM). In southeastern New Mexico, the oil and gas industry generates large volumes of produced water, while at the same time, freshwater resources are scarce. Single-method models are unable to capture the dynamic impacts of PWM on the water budget at both the local and regional levels, hence the need for a more complex hybrid approach. We used the literature, information characterizing produced water in New Mexico, and our preliminary interviews with subject matter experts to develop this framework. We then conducted a systematic literature review to summarize state-of-the-art of hybrid modeling methodologies and techniques. Our research revealed that there is a small but growing volume of hybrid modeling research that could provide some foundational support for modelers interested in hybrid modeling approaches for complex natural resource management issues. We categorized these efforts into four classes based on their approaches to hybrid modeling. It appears that, among these classes, PWM requires the most sophisticated approach, indicating that PWM modelers will need to face serious challenges and break new ground in this realm. Full article

Deploying of Fifth Generation and Beyond Fifth Generation (5G/B5G) wireless networks will require wider coverage, flexible connectivity, low latency, support for a large number of user devices, and more bandwidth. This article explores the paradigm that Remotely Piloted Air Systems (RPASs) or Unmanned Aerial Vehicles (UAVs) are integrated as a communication platform with cellular networks using radio access. It is important to know the possibilities and ways of such integration for effective interaction with RPASs. This paper studies the issues of ensuring the required Quality of Service (QoS) during heavy traffic and the choice of necessary data transmission modes for this. Models of RPAS communication channels with different architectures were created. The relationships between models’ performance and traffic parameters were obtained using the NetCracker Professional 4.1 software. The dependencies of the Average Utilization (AU) on the Transaction Size (TS) were analyzed. The effects of different bandwidths and the Bit Error Rate (BER) were studied. The traffic characteristics in all models were compared. Full article

Properly modeling the shadowing effects during wireless transmissions is crucial to perform the network quality assessment. From a mathematical point of view, using composite distributions allows one to combine both fast fading and slow fading stochastic phenomena. Numerous statistical distributions have been used to account for the fast fading effects. On the other hand, even though several studies indicate the adequacy of the Lognormal distributon (LNd) as a shadowing model, they also reveal this distribution renders some analytic tractability issues. Past works include the combination of Rayleigh and Weibull distributions with LNd. Due to the difficulty inherent to obtaining closed form expressions for the probability density functions involved, other authors approximated LNd as a Gamma distribution, creating Nakagami-m/Gamma and Rayleigh/Gamma composite distributions. In order to better mimic the LNd, approximations using the inverse Gamma and the inverse Nakagami-m distributions have also been considered. Although all these alternatives were discussed, it is still an open question how to effectively use the LNd in the compound models and still get closed-form results. We present a novel understanding on how the $\alpha$-$\mu$ distribution can be reduced to a LNd by a limiting procedure, overcoming the analytic intractability inherent to Lognormal fading processes. Interestingly, new closed-form and series representations for the PDF and CDF of the composite distributions are derived. We build computational codes to evaluate all the expression hereby derived as well as model real field trial results by the equations developed. The accuracy of the codes and of the model are remarkable. Full article

The spread of infectious diseases is a complex system in which pathogens, humans, the environment, and sometimes vectors interact. Mathematical and simulation modelling is a suitable approach to investigate the dynamics of such complex systems. The 2019 novel coronavirus (COVID-19) pandemic reinforced the importance of agent-based simulation models to quickly and accurately provide information about the disease spread that would be otherwise hard or risky to obtain, and how this information can be used to support infectious disease control decisions. Due to the trade-offs between complexity, time, and accuracy, many assumptions are frequently made in epidemiological models. With respect to vector-borne diseases, these assumptions lead to epidemiological models that are usually bounded to single-strain and single-vector scenarios, where human behavior is modeled in a simplistic manner or ignored, and where data quality is usually not evaluated. In order to leverage these models from theoretical tools to decision-making support tools, it is important to understand how information quality, human behavior, multi-vector, and multi-strain affect the results. For this, an agent-based simulation model with different parameter values and different scenarios was considered. Its results were compared with the results of a traditional compartmental model with respect to three outputs: total number of infected individuals, duration of the epidemic, and number of epidemic waves. Paired t-test showed that, in most cases, data quality, human behavior, multi-vector, and multi-strain were characteristics that lead to statistically different results, while the computational costs to consider them were not high. Therefore, these characteristics should be investigated in more detail and be accounted for in epidemiological models in order to obtain more reliable results that can assist the decision-making process during epidemics. Full article