Resources Engineering Research Articles

Optimization of PCM Properties for Thermal Energy Storage in Solar Parabolic Trough Systems: A Review

Abstract: Solar Parabolic Trough Systems (PTS) are highly efficient solar thermal technologies for converting concentrated solar radiation into thermal energy. However, their intermittent energy production, primarily due to variations in solar availability, underscores the necessity for effective thermal energy storage (TES) solutions. Phase Change Materials (PCMs) have emerged as a promising means to store and release thermal energy efficiently. This study focuses on the critical task of optimizing PCM properties to enhance thermal energy storage within Solar Parabolic Trough Systems. The selection and fine-tuning of PCM properties are paramount to achieving superior TES performance. Parameters under scrutiny include the melting temperature, latent heat of fusion, thermal conductivity, and cost-effectiveness. Each of these factors plays a pivotal role in the overall efficiency and economic viability of PCM-based TES systems integrated with PTS. Through a thorough review of existing research and recent advancements in the field, this study sheds light on the profound impact of tailored PCM properties. It demonstrates how optimizing these properties can lead to substantial improvements in energy storage capacity, system efficiency, and overall cost-effectiveness. Such optimizations are crucial not only for enhancing the competitiveness of solar thermal technology but also for promoting sustainable energy utilization. The investigation presented herein underscores the significance of PCM property optimization as a strategic pathway toward advancing solar thermal technology. By maximizing energy storage capacity, minimizing thermal losses, and optimizing cost factors, we can unlock the full potential of PTS, making them more reliable and accessible for meeting the world's growing energy demands. This research serves as a valuable resource for engineers, researchers, and stakeholders working towards the integration of PCM-based TES with solar thermal systems. Ultimately, it contributes to the realization of a cleaner, more sustainable energy future, addressing the urgent need to reduce greenhouse gas emissions and our reliance on non-renewable energy sources

Performance of equations for the longitudinal dispersion coefficient: a case study in the Orashi River

Abstract Investigation of the water quality of rivers is a key point in Water Resources Engineering. The longitudinal dispersion coefficient is one of the foremost significant parameters in river water quality monitoring. Several parameters such as hydraulic, morphology, total dissolved solids, and total suspended solids are effective parameters in the determination of the longitudinal dispersion coefficient as revealed by this study. The assessment of the river shows mean hydraulic and geometric properties such as flow, depth, velocity, longitudinal slope, and width to be 354.17 m3/s, 9.61 m, 0.69 m/s, 0.0079,101.63 m and the range of the longitudinal dispersion coefficient as (72–104.4) m2/s. Results obtained by employing the established equations revealed standard error indices and RMSE of the developed equation, and Kashefipour and Falconer equation gives correlation coefficient of about 0.819 and 4.182 and 0.421 and 12.186, respectively, as coefficient of determination and RMSE, and they are more accurate among the empirical equations. However, the newly derived equation for the longitudinal dispersion coefficient performed better when compared with others, indicating the fitness of the developed equation to estimate longitudinal dispersion coefficient.

Energy tunnels: A review of the state of the art and knowledge gaps to harness renewable energy from underground infrastructure

The thermal activation of underground tunnels, also known as energy tunnels, has shown significant potential to harness geothermal and aerothermal energy as a low-carbon and economical solution for space heating and cooling, water heating and road de-icing. The heat and load transfer mechanisms in energy tunnels are, however, far less understood compared to other energy geostructures, due to their complex interaction with the ground and tunnel environment. This paper aims to provide a comprehensive overview of the current state of knowledge on the thermal and thermo-mechanical performance of energy tunnels based on recent analytical models, field tests, model-scale experiments, and numerical studies. Results suggest that a thermal yield ranging from 5 to 6 W/m2 is attainable for each degree Celsius difference between the fluid inlet temperature and the ground temperature in the presence of groundwater flow and tunnel airflow. In the reviewed studies, the thermally induced tunnel structural responses appear to be insignificant compared to those caused by tunnel excavation. However, the long-term thermally induced ground deformation in clay deposits can reach up to 15 mm, which may become a crucial issue and therefore needs to be examined in geotechnical design. As a new area of research, there is a lack of long-term experimental investigations to solidify the current knowledge of the thermal, structural and geotechnical performance of the system. Further research is also required to develop an integrated analysis and design method considering the techno-economic aspects and explore innovative business partnership models. This work can serve as a useful resource for researchers, engineers, and policymakers seeking to further develop and promote the adoption of this technology in infrastructure projects.

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT.

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

Navigating through Noise Comparative Analysis of Using Convolutional Codes vs. Other Coding Methods in GPS Systems

This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. This paper aims to address the issue of limited information regarding the factors that contribute to the effectiveness of the implementation of Convolutional Codes in GPS systems. The research problem revolves around the insufficiency of scholarly sources elucidating the rationale behind the utilization of convolutional codes, specifically in GPS systems, rather than others. Through an in-depth analysis of these factors, this study strives to achieve a comprehensive understanding of the application of Convolutional Codes in GPS. To tackle this research problem, a novel methodology involving comparative analysis is employed. The coding techniques commonly used in satellite communication systems (such as BCH, LDPC, and turbo codes) are carefully examined and compared to the advantages and suitability of Convolutional codes and the Viterbi algorithm for GPS systems. Each coding technique is evaluated based on factors such as error detection and correction capabilities, bandwidth efficiency, computational complexity, and resilience to noise. The key findings of this study shed light on the unique advantages offered by Convolutional codes and the Viterbi algorithm for GPS systems. The analysis reveals that these coding techniques exhibit superior error detection and correction capabilities, efficient bandwidth utilization, and the ability to withstand noise in the GPS communication channel. The results also highlight the computational complexity associated with these techniques, providing valuable insights for the implementation of convolutional codes in GPS systems. Overall, this article contributes to the existing knowledge by providing a comprehensive understanding of the reasons behind the suitability of convolutional codes for GPS systems. The findings of this study serve as a resource for researchers, engineers, and practitioners in the field of satellite communication, aiding in the comprehensive understanding, advancement, and optimization of GPS system designs.

COMPARATIVE ANALYSIS OF SOFTWARE SYSTEMS FOR HYDRAULIC TURBINE FLOW SIMULATION

A comprehensive review of modern software complexes used for calculating spatial flow in hydraulic turbine flow parts was conducted. The widely used software system, Ansys, was analyzed. An overview of Ansys was provided, including its history, popularity within the CFD community, key features, and capabilities for analyzing the flow parts of hydraulic turbines. The preprocessing tools, solver parameters, post-processing functions, and visualization capabilities of Ansys were described. The advantages and limitations of using Ansys for calculating spatial flow in hydraulic turbine flow parts were analyzed. The open-source CFD software complex, OpenFOAM, was discussed. The main functions and capabilities of the OpenFOAM program were described. Information about solver libraries, meshing capabilities, advantages, and limitations for analyzing hydraulic turbines was presented, along with insights into the support from the scientific community and resources available to OpenFOAM users. SolidWorks FlowSimulation, which integrates with SolidWorks software, was examined. The unique features of SolidWorks FlowSimulation for analyzing spatial flow in hydraulic turbines were highlighted. The possibilities of CAD integration and the advantages of accurate geometric models were discussed. The capabilities of parametric analysis were explored, and the advantages and limitations of using SolidWorks FlowSimulation for calculating spatial flow in hydraulic turbine flow parts were analyzed. A comparison of the three software complexes was conducted based on their capabilities, ease of use, accuracy, computational resources required, and cost. An assessment of the advantages and disadvantages of each program was provided, along with recommendations for choosing the most suitable program based on specific use cases, objectives, and user requirements. This article serves as a valuable resource for engineers, designers, and researchers seeking insights into the available software systems for analyzing hydraulic turbine flow parts. It enables them to make informed decisions in selecting the most suitable software system based on their specific requirements, ultimately contributing to the optimization of hydraulic turbine performance and efficiency.

Discharge estimation in a compound channel with converging and diverging floodplains using ANN–PSO and MARS

Abstract The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90.

Application of AI-Based Techniques on Moody’s Diagram for Predicting Friction Factor in Pipe Flow

The friction factor is a widely used parameter in characterizing flow resistance in pipes and open channels. Recently, the application of machine learning and artificial intelligence (AI) has found several applications in water resource engineering. With this in view, the application of artificial intelligence techniques on Moody’s diagram for predicting the friction factor in pipe flow for both transition and turbulent flow regions has been considered in the present study. Various AI methods, like Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), M5 tree (M5), M5Rules, and REPTree models, are applied to predict the friction factor. While performing the statistical analysis (root-mean-square error (RMSE), mean absolute error (MAE), squared correlation coefficient (R2), and Nash–Sutcliffe efficiency (NSE)), it was revealed that the predictions made by the Random Forest model were the most reliable when compared to other AI tools. The main objective of this study was to highlight the limitations of artificial intelligence (AI) techniques when attempting to effectively capture the characteristics and patterns of the friction curve in certain regions of turbulent flow. To further substantiate this behavior, the conventional algebraic equation was used as a benchmark to test how well the current AI tools work. The friction factor estimates using the algebraic equation were found to be even more accurate than the Random Forest model, within a relative error of ≤±1%, in those regions where the AI models failed to capture the nature and variation in the friction factor.

One of the Most Influential Reference Resources for Engineers Around the World

One of the Most Influential Reference Resources for Engineers Around the World

Systems engineering and water resources management: A closer relationship is needed

AbstractSystems engineering can be applied in a broad spectrum of sectors, but only its analysis tool has been applied in the field of water resources management. Because systems engineering has a separate community of practice from water resources, there is little crosstalk between the two fields. As a result, the systems engineering functions that support planning, design, production, procurement, and customer support are not being applied to water systems. Meanwhile, water systems exhibit complexities that have generated a separate field named Integrated Water Resources Management that continues to confuse its followers after several decades. Its methods are applied to a broad spectrum of water issues that affect multiple stakeholders with conflicting interests and involve distinct subsystems, such as water supply or hydropower, as well as combinations of them. Use of systems analysis for such water issues began six decades ago, but it is still a work in progress. Evolving methods of systems engineering offer new possibilities to address problems of water resources management, but they must extend beyond systems analysis, which belongs to multiple disciplines. Examples show possibilities to apply systems engineering methods when water issues exhibit attributes of engineered systems and do not involve social and environmental complexities that cannot be included in system boundaries. Collaboration among systems engineering and water resources management would offer a fertile test bed to advance both fields.

Review on Fast Pyrolysis of Biomass for Biofuel Production from Date Palm

The fast depletion of fossil fuels and growing concerns about environmental sustainability have increased interest in using biomass as a renewable energy source. Fast pyrolysis, a thermochemical conversion process, has emerged as a promising technique for converting biomass into valuable biofuels and bio-based chemicals. The aim of this literature review is to comprehensively analyze recent advances in biomass fast pyrolysis, focusing on the principles, process parameters, product yields, and potential applications of biomass fast pyrolysis. This comprehensive review, based on an in-depth analysis of 61 scientific papers and 4 patents, provides an overview of various biomass technologies (combustion, gasification, pyrolysis) used for biofuel production. It focuses on the principles, benefits and applications of these technologies and serves as a valuable resource for researchers, engineers and policy makers. Based on the wealth of information from rigorously selected sources, we explore the key process parameters and reactor types associated with each technology, providing insight into its efficiency and product composition.

Optimal profitable allocation of associated natural gas resource on a countrywide basis to mitigate flaring

The flaring of large volumes of the natural gas that is produced as a by-product of crude oil, has puzzled the energy-hungry world for long. In recent years, it has also drawn the angst of the environmental and global-warming advocacy groups. The issues behind gas flaring are complex, as they involve a dynamic interplay of geosciences, engineering, economics, finance and natural resource policy-making.The purpose of this research is to analyze the associated gas problem quantitatively from a country-wide perspective. Unlike prior studies, this work focusses on the upstream side where the crucial strategic decisions are made upfront. To this end, a mathematical model is proposed for seeking the optimal integrated management strategy for the said national resource. The model is based on the Mixed Integer Linear Programming (MILP) technique and consists of scores of techno-economic decision variables and constraint equations along with a profit maximization objective.The model is general in nature but is applied herein to Nigeria as a case study. Several simulation runs are made to provide results for explanatory and prediction purposes. Model results indicate, for example, that a minimum gas price of $4.4/Mscf and minimum spend of $22B are jointly required to achieve zero-flaring, yielding a $10B profit. This is achievable via a strategy of reinjecting all associated gas produced from medium and large-sized fields, but gathering and selling all gas from small fields. Capital availability, rather than nationwide government-decreed flaring control, is shown to be the dominant determinant of profitable flaring mitigation. The model has proven to be a valuable tool for establishing the inter-relations between the key problem parameters under various pricing and resource base scenarios, and for prognosticating the trajectory of the gas industry in various jurisdictions.

Hydrothermal carbonization of waste wood: Sustainable recycling of biomass by-products and novel performance enhancer for bitumen

Recycling biomass by-products into bitumen is one of the most effective ways to develop sustainable pavements. To cope with the shortage of petroleum resources and improper disposal of discarded biomass, the hydrothermal carbon (HTC) produced by hydrothermal carbonization of waste wood was adopted to reduce the consumption of bitumen and enhance the performance of bitumen, and the mechanical performances and working mechanism of hydrothermal carbon-modified bitumen (HTCMB) were investigated in this study. The results indicate that HTC significantly enhances bitumen's high-temperature and fatigue properties. When the content of HTC is not more than 3%, its effect on the low-temperature cracking resistance of bitumen can be ignored. The addition of HTC absorbs the polar components in bitumen, thus promoting the dispersion of polar components and effectively preventing the mutual aggregation and crystallization of wax components and asphaltenes in bitumen. The excellent mechanical properties of HTCMB can be attributed to the irregular fibrous composition and porous structures of HTC, which play a good role in reinforcing and connecting the bitumen matrix and dramatically enhances the interaction with bitumen. This study proposes a win–win solution to the shortage of petroleum resources in road engineering and the improper disposal of discarded biomass.

Red mud-based heterogeneous catalyst for hydrothermal resource-based degradation of norfloxacin in pharmaceutical wastewater by hydrogen peroxide

Red mud (RM) was used as a catalyst for the hydrothermal degradation of antibiotics in simulated pharmaceutical wastewater by H2O2 to produce formic acid. Formic acid could be adopted as hydrogen storage, conforming to carbon neutral strategy. When 10 mL norfloxacin (NOR) solution was used at 40 mg/L, the optimal reaction conditions were 0.02 g RM activated at 100 °C, 0.2 mL added H2O2, as well as the reaction time and temperature of 0.5 h and 90 °C, leading to NOR degradation rate of 69.26 % and a formic acid yield of 58.36 % (selectivity: 87.56 %). The RM catalyst presented good activity and stability in reuse. In this study, phase composition and structure of RM catalyst before and after the reaction were comprehensively characterized, and kinetic model was established. Moreover, the key intermediate pathway of NOR degradation was identified by LC-MS. Under the concept of “treating waste with waste”, these results suggested that wastewater-treatment technology of solid waste (Red Mud) was “Fenton-like” catalyst for resource-based degrading high-ecotoxicity antibiotic pollutants in pharmaceutical wastewater. This process could support environmental protection and green development of the pharmaceutical industry, together with hazardous waste-recycling treatment. In short, this research provides a theoretical basis for the subsequent engineering resource treatment of pharmaceutical wastewater.

Recent Advances in Airfoil Self-Noise Passive Reduction

Airflow-induced noise prediction and reduction is one of the priorities for both the energy and aviation industries. This review paper provides valuable insights into flow-induced noise computation, prediction, and optimization methods with state-of-the-art efforts in passive noise reduction on airfoils, blades, and wings. This review covers the combination of several approaches in this field, including analytical, numerical, empirical, semi-empirical, artificial intelligence, and optimization methods. Under passive noise reduction techniques, leading and trailing edge treatments, porous materials, controlled diffusion airfoils, morphing wings, surface treatments, and other unique geometries that researchers developed are among the design modification methods discussed here. This work highlights the benefits of incorporating multiple techniques to achieve the best results concerning the desired application and design. In addition, this work provides an overview of the advantages and disadvantages of each tool, with a particular emphasis on the possible challenges when implementing them. The methods and techniques discussed herein will help increase the acoustic efficiency of aerial structures, making them a beneficial resource for researchers, engineers, and other professionals working in aviation noise reduction.

Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches

In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R2 threshold confirming- (R2 > 0.9855), (R2 > 0.9768), (R2 > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering.

Numerical modelling of wave and tidal current interactions and their impact on wave parameters

In regions where both waves and tidal currents coexist, tidal flow can significantly alter wave parameters and hence affects the estimation of wave energy resources. This study uses a coupled wave-current numerical model to evaluate the influence of wave-current interactions on wave parameters. To achieve this, the simulation was performed in 3 stages. At first, a large-scale North Atlantic wave model was constructed using the spectral wave model TOMAWAC to generate wave conditions and boundary inputs. This wave model was calibrated and validated at four sites around the UK using field measurements. Secondly, a small-scale numerical model covering Pentland Firth and Orkney Waters, Scotland, UK, was chosen, and tidal flow and current speeds were simulated by the three-dimensional flow model TELEMAC 3D. As with the wave model, the flow model was also calibrated and validated with site measurements from an ADCP; thus, both models were validated. In the third stage, a coupled TOMAWAC-TELEMAC 3D model was employed for the small-scale region, and the wave parameters generated by the large-scale model were input as boundary conditions. The TOMAWAC-TELEMAC 3D coupled model was validated with field measurements at two locations in Orkney Waters, where waves and currents coexist. Various wave and tidal currents parameters produced from the coupled model are presented in the paper. To evaluate the wave-current impact on wave parameters, a qualitative and quantitative analysis of these parameters is carried out, and the results are presented and discussed in the paper. The large-scale and small-scale numerical models developed in this study are useful tools for generating wave boundary conditions and wave energy resource assessment, helping researchers and engineers better understand the characteristics of wave-current interactions.

Preface

International Conference on Sustainable Practices in Engineering & Technology (IC-SPET’23) is the AICTE sponsored first international Conference organised by Department of Civil Engineering of Sree Buddha College of Engineering, Pattoor, from 14th to 16th June, 2023 with a focal theme on Sustainability in Civil Engineering. The sub themes were Structural Engineering & Sustainable Construction Technology, Water Resource & Environmental Engineering, & Transportation Engineering, Modelling & Computational Techniques in Civil Engineering and GIS application in Civil Engineering. The conference is planned and organized with an objective of providing a platform and conducive environment for the participants to share innovations in Engineering and Technology and to form international relationships among the researchers involved in Engineering and Technology.The three-day international conference was conducted from 14th June, 2023 to 16th June, 2023 in hybrid mode. The conference was inaugurated by the chief guest Dr. N.P. Rajamane, Professor, Scientist, Founder & Former Head (a) Centre for Advanced Concrete Research (CACR), SRM IST (b) Advanced Materials Lab (AML), CSIR-SERC. Renowned national and international speakers such as Dr. N. P. Rajamane (Scientist, Founder & Former Head - (a)CACR, SRM IST (b) AML, CSIR-SERC), Dr. Kamal Laksiri (Governor, Region 10, ASCE), Dr. Sumi Siddiqua (Associate Professor, The University of British Columbia, Canada), Dr. Tanmay Basak (Professor, Indian Institute of Technology Madras) and Dr. Vinu Unnikrishnan (Assistant Professor, College of Engineering, West Texas, USA) has delivered keynote address on sustainable practices in diverse fields of engineering.To IC-SPET’23, 103 technical papers from various institutions within and outside the state were submitted, out of which 65 papers accepted for presentation. And 17 papers were selected to include in this proceeding of conference through meticulous review process performed by experts in the field.We would like to greatly acknowledge the active contribution of keynote speakers, session chairs, reviewers, authors and participants towards the success of the conference. We express our indebted gratitude to Prof. K. Sasikumar, Hon'ble Chairman of the Sree Buddha Group of Institutions, Prof. V. Prasad (Secretary) and Sri. A. Sunil Kumar (Treasurer) of the Sree Buddha Educational Society, Dr. K. Krishnakumar, the Principal of Sree Buddha College of Engineering and all members of Sree Buddha Educational Society for their invaluable guidance and support for the successful coordination of the event. We appreciate the efforts of the faculty members, staff, and student volunteers of the Department of Civil Engineering, who worked as a team and shared responsibilities for the successful coordination.We would like to acknowledge the financial assistance received from AICTE (All India Council for Technical Education) for organizing the conference. We express our sincere gratitude to all the associating organisations and institutions such as ASCE, IEI Kollam local center, ICI Kochi center, Habilete learning solutions, Indian Institute of Infrastructure and Construction, Qcrete and T. R. Associates for their timely assistance. Finally, we express our sincere thanks to the team of IOP Conference series lead by Cat Leyland and Rachelle Morris for bringing out this proceedings.

One of the Most Influential Reference Resources for Engineers Around the World.

One of the Most Influential Reference Resources for Engineers Around the World.

Water Resource Engineering: A Discipline or a Specialty and Why It Matters

Water Resource Engineering: A Discipline or a Specialty and Why It Matters