The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects

This book contains publications related to the special topic entitled: "The Latest Scientific Problems Related to the Implementation and Diagnostics of Construction Objects". Construction is a sector of the economy that is characterized by a very high variability of execution conditions and a large variety of building structures. In a period of very rapid economic development, this high variability and diversity generates many new scientific problems that must be solved in order to further improve the quality of production, as well as to reduce the cost and time of construction. The purpose of the issue is to present and discuss the results of the latest research in the broad field of construction engineering, particularly concerning: modification of the composition of construction materials using various micro- and nanomaterials, by-products or wastes; modern methods of controlling construction processes; methods of planning and effective management in construction, as well as methods of diagnosing construction objects. The articles published in this issue deal with theoretical, experimental, applied and modeling research conducted worldwide in the above-mentioned scientific areas

Examining the customer journey of solar home system users in Rwanda and forecasting their future electricity demand

Globally, 771 million people lack access to electricity, out of which 75% live in Sub-Saharan Africa (IEA, 2020b). Electricity grid expansion can be costly in rural areas, which often have low population densities. Solar home systems (SHS) have provided people worldwide an alternative option to gain electricity access. A SHS consists of a solar panel, battery and accompanying appliances. This research aims to advance the understanding of the SHS customer journey using a case study of SHS customers in Rwanda. This study developed a framework outlining households’ pre- to post-purchase experiences, which included awareness and purchase, both current and future SHS usage and finally customers’ upgrade, switching and retention preferences. A mixed methods approach was utilised to examine these steps, including structured interviews with the SHS providers’ customers (n=100) and staff (n=19), two focus groups with customers (n=24), as well as a time series analysis and descriptive statistics of database customers (n=63,299). A convolutional neural network (CNN) was created to forecast individual SHS users’ future electricity consumption in the next week, month and three months based on their previous hourly usage. Despite the volatility of SHS usage data, the CNN was able to forecast individual users’ future electricity more accurately than the naïve baseline, which assumes a continuation of previous usage. The time series analysis revealed an evening usage peak for non-television users, whilst customers with a television experienced an additional peak around midday. SHS recommendations prior and post-purchase were common, highlighting the circular nature of the customer journey. The main purchase reason and usage activity were having a clean energy source and phone charging respectively. A better understanding of the SHS customer journey may increase the number of households with electricity access, as companies can better address the purchase barriers and tap into the power of customer recommendations

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Mesoscale Modeling of Controlled Degradation in Polymer Networks and Melts

Controlled degradation of polymers finds various applications in fields ranging from the design of functional soft materials to recycling of polymers. In several of these applications, the characteristic length scale at which relevant processes occur ranges from nanometers to microns, typically referred to as the mesoscale. Although analytical models and continuum approaches inform our current understanding, analysis of degradation at the mesoscale is exceptionally limited. For modeling degradation at the mesoscale, we use the Dissipative Particle Dynamics (DPD) technique and the LAMMPS simulation software. Within the DPD framework, we model controlled degradation or the breaking of covalent bonds within a polymer as a stochastic process that reproduces first order degradation reaction kinetics. A known limitation of the DPD approach is polymer chains crossing through each other. Previous researchers had developed a modified segmental repulsive potential (mSRP) framework which prevents such crossing of polymers by introducing extra repulsion between the bonds of polymer chains. We modified the existing model in LAMMPS to enable switching off the extra repulsion when a bond is broken. We implemented this feature within the LAMMPS framework, and it is now available for the general scientific community as a part of the online open-source project. Later, we extended this feature to introduce the extra repulsion when a bond is formed to simulate the hydrosilylation reaction used in the synthesis of polymer derived ceramics. As a model polymer network for studying degradation, we use the tetra-arm polyethylene glycol (tetra-PEG) based hydrogel films. Tetra-PEG networks have a uniform network structure and hence superior mechanical properties. We tracked the degradation iii of these networks by measuring the evolution of the weight average molecular weight and dispersity during degradation. By tracking the fraction of degradable bonds broken, we identified the “reverse gel point”, the point where the polymer network dissolves into the surrounding solvent. Additionally, we tracked the erosion or mass loss from the degrading network by accounting for polymer fragments which dissociate and diffuse away from the network. We identified that the mass loss from the network depends on the initial thickness of the hydrogel films. As a second system, we modeled the controlled degradation of nanogels that are either suspended in a single solvent or adsorbed onto a liquid-liquid interface. Controlled degradation of nanogels at an interface provides a dynamic approach to control interface topography at the nanoscale. We tracked the degradation of these particles by analyzing the evolution of their shape and size along with the molecular weights and dispersity in the system. In bulk, the particles swell almost homogenously while at the interface, the particles spread and cover the interface as degradation occurs. We found that the reverse gel point for these particles varies with the total initial number of precursors. The evolution of particle shape and size is significantly affected by the surrounding solvent and the surface tension between the two liquid phases. The final part of this dissertation focuses on developing an initial framework to extend the above approach to model degradation of polyolefin melts under a local temperature gradient. The long term goal of this project is to study thermal degradation of polyolefins caused by introducing microwave absorbing nanosheets and subjecting the polymer to microwave irradiation

Machine Learning and Its Application to Reacting Flows

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Multi-fuel operation of modern engines; on board fuel identification

Modern engines require enhancement of electronic controls to achieve better fuel economy, higher power density and satisfactory emissions levels while operating safely. Military vehicles should be capable to run safely and efficiently on any fuel available in the field, therefore on-board fuel identification and adaptation of engine controls to the type of fuel becomes extremely important. In these conditions, the use of an inexpensive, nonintrusive sensor is highly desirable. The development of a technique based on the measurement of the instantaneous crankshaft speed and engine dynamics could be a convenient solution. Several such methods have been elaborated at the Center for Automotive Research (CAR) in the Mechanical Engineering Department at Wayne State University. Each of these methods yields plausible results regarding on-board fuel identification