Optimal Size Research Articles

Optimal Molecular Dynamics System Size for Increased Precision and Efficiency for Epoxy Materials.

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.

Harmonizing Blasting Efficiency: A Case Study on Evaluation and Optimization of Fragmentation Size and Ground Vibration

Harmonizing Blasting Efficiency: A Case Study on Evaluation and Optimization of Fragmentation Size and Ground Vibration

Effect of varying core density and material on the quasi‐static behaviors of sandwich structure with 3D‐printed hexagonal honeycomb core

AbstractAdditive manufacturing (AM) involves the development of complex, lightweight sandwich structures for the automotive and aerospace industries. These structures are essential for load bearing and impact resistance. Nevertheless, there is a significant obstacle of failure under compressive loading, e.g. through brittle fractures and crushing. To address this issue, this study evaluates the compressive properties, energy absorption and failure damage in quasi‐static tests (flatwise, in‐plane, and flexural) of sandwich composites with 3D‐printed hexagonal honeycomb cores of different unit cells (6, 8 and 10 mm) and materials (polylactic acid (PLA), PLA‐Carbon and PLA‐Wood). The results show that increasing the core density enhances compressive strength, modulus, and energy absorption. An 8 mm unit cell absorbs energy optimally for lightweight structures. In PLA flatwise testing, the 8 mm unit cell absorbed 419.49 J more energy than the 10 mm unit cell. Additionally, PLA‐Wood has better mechanical performance than PLA‐Carbon due to the better filler with the PLA‐ matrix. In flatwise testing with an 8 mm unit, PLA‐Wood absorbs 214.01 J, while PLA‐Carbon absorbs 122.49 J. The failure modes vary depending on tests performed. The study highlights the potential of 3D‐printed honeycomb core structures for load‐bearing applications in various industries, including aerospace and automotive.Highlights Quasi‐static loading behavior of 3D‐printed hexagonal honeycomb cores. Increased core density improves compressive stress, modulus, and absorbed energy. An optimal unit cell size for lightweight 3D printed core structures is 8 mm. PLA‐Wood performs better in energy absorption due to filler compatibility. The failure modes are related to the type of quasi‐static loads applied.

Bi2Te3-Based Thermoelectric Modules for Efficient and Reliable Low-Grade Heat Recovery.

Bismuth-telluride-based alloy has long been considered as the most promising candidate for low-grade waste heat power generation. However, optimizing the thermoelectric performance of n-type Bi2Te3 is more challenging than that of p-type counterparts due to its greater sensitivity to texture, and thus limits the advancement of thermoelectric modules. Herein, the thermoelectric performance of n-type Bi2Te3 is enhanced by incorporating a small amount of CuGaTe2, resulting in a peak ZT of 1.25 and a distinguished average ZT of 1.02 (300-500 K). The decomposed Cu+ strengthens interlayer interaction, while Ga+ optimizes carrier concentration within an appropriate range. Simultaneously, the emerged numerous defects, such as small-angle grain boundaries, twin boundaries, and dislocations, significantly suppresses the lattice thermal conductivity. Based on the size optimization by finite element modelling, the constructed thermoelectric module yields a high conversion efficiency of 6.9% and output power density of 0.31W cm-2 under a temperature gradient of 200 K. Even more crucially, the efficiency and output power little loss after subjecting the module to 40 thermal cycles lasting for 6 days. This study demonstrates the efficient and reliable Bi2Te3-based thermoelectric modules for broad applications in low-grade heat harvest.

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

Improving functional merit of commercially available pea protein isolate by employing a combination of physical, chemical, and enzymatic modification

Pea protein-based diets have garnered global attention as a viable alternative to meat or primary protein sources. Nevertheless, the utilization of pea protein is hindered by issues such as solubility, and emulsifying properties. This research presents a combined set of techniques employed to commercially available Pea Protein Isolate (PPI) to effectively address major setbacks associated with the usage of pea protein in diverse industrial sectors, especially meat. The set integrates pH at 10.0, sonication at three different magnitudes (1,2 and 4 W/mL), heat at 60 °C, and enzyme hydrolysis through Flavourzyme at three different enzyme-to-substrate ratios (0.25%, 0.5%, and 1.0%). Among the twelve treatments, PPI11, PPI12, and PPI13 showed promising outcomes for artificial meat. The treatments exhibited favorable solubility, gelling, emulsifying, and foaming properties while containing the optimum and desirable protein size after undergoing partial hydrolysis comprehended in the gel electrophoresis results. Solubility (for PPI11, PPI12, PPI13) had seen significant (p < 0.05) improvement from 31.03% ± 2.11% (Control) to 64.43% ± 2.09%, 66.23% ± 2.11%, and 69.87% ± 1.57%, accompanied by changes in protein morphology in scanning electron microscopy. Furthermore, treated PPIs were applied to stabilize sunflower oil-in-water emulsion, showing a significant escalation in emulsion capacity (38.46%, 39.42% and 66.61% increase for PPI11, PPI12, PPI13), foaming capacity (46.88%, 51.20%, and 69.60% increase for PPI11, PPI12, PPI13), and foaming stability (30 min) (23.97%, 28.70%, and 30.22% increase for PPI11, PPI12, PPI13). Gelling properties were decisive in this study (2.512 ± 0.1 N, 2.604 ± 0.1 N, and 2.168 ± 0.3 N, for PPI11, PPI12, and PPI13), predominantly in plant-based meat analog perspective. So the mechanism proposed through this research will enable pea protein treated with this set of operations, combining physical, chemical and enzymatic treatments, to further its journey towards considerably improved meat analogs.

Optimum firm size in Vietnam: does subcontracting matter?

Optimum firm size in Vietnam: does subcontracting matter?

Efficacy of intentional undersized thoracic endovascular repair for Stanford type B aortic dissection

ObjectivesThoracic endovascular aneurysm repair (TEVAR) is widely used for the treatment of aortic dissection. Endograft oversizing is a risk factor for stent graft–induced new entry tears and retrograde type A aortic dissection. However, there is no clear consensus on the optimal graft size selection for Stanford type B acute or subacute aortic dissection (TBAD). Herein, we examined the safety and efficacy of TEVAR using an intentionally undersized endograft to treat TBAD. MethodsThis retrospective chart review study included 82 patients who underwent TEVAR for acute or subacute Stanford TBAD between 2015 and 2022. We measured the true lumen diameter just distal to the subclavian artery and opted for a stent graft of the same diameter. In instances where deformation resulting from false lumen pressure displacement was pronounced, we measured the diameter at the site just proximal to the subclavian artery. Patients’ characteristics, procedural, in-hospital, and follow-up data, and aortic remodeling were analyzed. The aortic diameter was analyzed using one-way analysis of variance followed by Dunnett’s test. Survival and freedom from re-intervention were estimated using Kaplan–Meier curves. ResultsThe follow-up rate was 98.4%. The mean age was 58.3 ± 12.3 years and 76 were men (92.7%). The mean diameters of the stent graft and native proximal landing zone were 30.9 ± 3.2 mm and 30.8 ± 3.0 mm, respectively. The oversize rate of the stent graft in relation to the native proximal aortic diameter was 0.3 ± 4.7%. In-hospital mortality was observed in one patient, retrograde type A aortic dissection in one patient, distal stent graft-induced new entry tear in zero patients, and type 1a endoleak in 22 patients (26.8%). Type 1a endoleaks, characterized by antegrade false lumen blood flow originating from the primary entry, in 12 patients spontaneously disappeared within 1 year of follow-up. According to aortic remodeling, 59 (86.8%) patients achieved complete aortic remodeling at the aortic arch level and 51 (75.0%) at the 8th thoracic vertebral level. The diameters of the aortic arch and descending aorta were significantly reduced compared with the postoperative measurements (P <0.001). Survival rates were 97.5% and 93.6% at 1 and 3 years, respectively. Freedom from re-intervention was 84.7% and 84.7% at 1 and 3 years, respectively. ConclusionsIntentionally undersized TEVAR was safe and achieved acceptable aortic remodeling despite a high rate of type 1a endoleaks. A type 1a endoleak was acceptable as it primarily diminished during the mid-term follow-up.

Integrated Load–Source Side Management for Techno-Economic-Environmental Performance Improvement of the Hybrid Renewable Energy System for Rural Electrification

Electricity produced by a hybrid renewable energy system (HRES) is reliable, economical, and environmentally benign. However, effective management of the load-source side is essential to enhance the reliability, affordability, and eco-friendliness of HRES. Therefore, an integrated load–source side management for techno-economic-environmental (TEE) performance improvement of HRES is developed in this paper. The factors considered are namely technical (renewable energy portion, excess energy factor, and unmet load), environmental (particulate matter and carbon emission), and economical (annualized cost of system, total net present cost, and cost of energy). To solve the HRES size optimization problem, a marine predators algorithm (MPA) is used for the first time. HRES is investigated without Load side management (LSM), with LSM, and with LSM integrated with source-side management (SSM). LSM is carried out using the peak shifting technique, which shifts time-movable loads (TMLs) to off-peak hours depending on excess energy availability, taking into account consumer comfort violations and customer behavior uncertainty. For SSM, a comparatively better energy management strategy (EMS) than the load following, cycle charging, and generator order is proposed, which improves the TEE performance of HRES. The integrated load–source side management saves 5% in NPC and 0.008 $/kWh in COE, respectively. To come up with a configuration that is more feasible, a sensitivity analysis for the HRES's component costs and macroeconomic variables is carried out. Furthermore, the optimal configuration COE is slightly higher than that of the most recent study in the literature. By contrasting the MPA result with the HRES PSO result, the accuracy of the MPA result is also confirmed.

Optimization of Bilayer Resistive Random Access Memory Based on Ti/HfO2/ZrO2/Pt.

In this paper, the electrothermal coupling model of metal oxide resistive random access memory (RRAM) is analyzed by using a 2D axisymmetrical structure in COMSOL Multiphysics simulation software. The RRAM structure is a Ti/HfO2/ZrO2/Pt bilayer structure, and the SET and RESET processes of Ti/HfO2/ZrO2/Pt are verified and analyzed. It is found that the width and thickness of CF1 (the conductive filament of the HfO2 layer), CF2 (the conductive filament of the ZrO2 layer), and resistive dielectric layers affect the electrical performance of the device. Under the condition of the width ratio of conductive filament to transition layer (6:14) and the thickness ratio of HfO2 to ZrO2 (7.5:7.5), Ti/HfO2/ZrO2/Pt has stable high and low resistance states. On this basis, the comparison of three commonly used RRAM metal top electrode materials (Ti, Pt, and Al) shows that the resistance switching ratio of the Ti electrode is the highest at about 11.67. Finally, combining the optimal conductive filament size and the optimal top electrode material, the I-V hysteresis loop was obtained, and the switching ratio Roff/Ron = 10.46 was calculated. Therefore, in this paper, a perfect RRAM model is established, the resistance mechanism is explained and analyzed, and the optimal geometrical size and electrode material for the hysteresis characteristics of the Ti/HfO2/ZrO2/Pt structure are found.

Optimizing size and economic feasibility assessment of photovoltaic and energy storage setup in residential applications

Battery energy storage systems (BESSs) are essential in enhancing self-sufficiency, sustainability, and delivering flexibility services. However, adoption of this technology in residential applications is constrained, predominantly due to its suboptimal economic performance. A proper selection of design parameters and optimal resource utilization can significantly enhance performance and establish economic feasibility. This research introduces a photovoltaic (PV)-BESS optimization framework, formulated to ascertain optimal infrastructure sizing, and maximize economic performance. The proposed tool considers various elements, such as energy storage state of health status, renewable profiles, residential load profiles, and prevailing energy market conditions. A novel rule-based, non-linear optimization method is developed, with a focus on maximizing revenue while considering energy storage (ES) degradation to project more accurate and realistic scenarios and payback periods. Employing the Estonian energy market as an illustrative case study, the model probes the potential of integrating ES and PV technology in North European residences. The model achieved the shortest payback period of five years in our case study, underscoring the emerging potential of such technologies in behind-the-meter applications.

Identification of Commercial Stainless-Steel Mesh Size for Electrochemical Oxygen Evolution Reaction.

The study examines the oxygen evolution reaction (OER) electrocatalytic efficiency of various stainless-steel mesh (SSM) sizes in electrolytic cells. Stainless steel is chosen due to its widespread availability and stability, making it an economically viable option. The primary objective of this investigation is to determine the optimal stainless-steel mesh size among those currently widely available on the market. The classification of stainless-steel mesh sizes as SS304 is confirmed by the minimal compositional variations observed across all mesh sizes through cyclic voltammetry (CV) and X-ray fluorescence (XRF) analyses. Remarkably, CV experiments carried out at different scan rates indicate that SSM 200 has the maximum specific electrochemical surface area (ECSA). As a result, SSM 200 demonstrates superior performance in terms of current density response and shows the lowest overpotential in the alkaline medium compared to other stainless-steel mesh sizes. Stability tests conducted under constant voltage further confirm the remarkable stability of SSM 200, making it an ideal anode for electrolytic cell applications. These findings emphasize the cost-effectiveness and high stability of SSM 200, presenting intriguing possibilities for future research and advancements in this field.

Development of a random forest based algorithm for road health monitoring

Road damages, such as potholes and rutting, must be addressed in their early stages to prevent accidents and minimize maintenance costs. The presence of these damages not only causes discomfort to passengers but also accelerates vehicle deterioration. Improper material mixtures and inadequate maintenance practices often contribute to road damage. In recent years, numerous machine learning (ML) algorithms have been developed with a predominant focus on binary classification, specifically targeting potholes. However, roads are susceptible to various damages beyond potholes, necessitating a comprehensive approach for effective solutions. This study introduces a novel algorithm utilizing a random forest (RF) classifier for multi-class classification of road damages. The proposed algorithm is validated through rigorous simulation and field studies. Unlike previous models, this approach embraces a comprehensive perspective by considering a wider range of damages, thereby facilitating a more nuanced and inclusive process for the classification of road damages. In the field study, data has been collected using a set of uni-axial accelerometers and a smartphone camera mounted on a test vehicle. The data has been processed using the sliding window technique, and features have been extracted from each window to train the RF classifier. In this process, an optimal window size has been determined and employed to enhance the effectiveness of feature extraction for training the RF classifier. The proposed algorithm demonstrates significant accuracy in both simulation and field studies, with notable performance in identifying and classifying road damages. The algorithm’s outcomes are utilized to estimate a detailed cost for repairing the identified damages. This paper showcases proficiency in addressing various damages, offering valuable insights for the implementation of cost-effective road maintenance strategies.

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

The integration of solar-hydrogen hybrid renewable energy systems in oil and gas industries for energy efficiency: Optimal sizing using Fick's Law optimisation Algorithm

The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities.The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts.The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation.Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised.This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies.

Towards a zero-waste aquaponics-centered eco-industrial food park

The scarcity of natural resources, rising energy prices and strict environmental policies are incentives for the food industry to reform current production strategies. As part of this shift, the eco-industrial park concept is gaining momentum. Industrial aquaponics, integrating aquaculture and hydroponics, emerges as a pioneering food production system. Its focus on reusing and conserving resources aligns with eco-industrial parks. The novel concept of an aquaponics-centered eco-industrial food park fits into a biobased circular economy and supports large-scale exploitation to overcome current economic constraints that prevent commercial adoption. However, balancing aquaponics-centered eco-industrial food parks to minimize external inputs as well as waste is challenging. The present study aims to investigate how a quasi-dynamic optimization method can be used in the design of aquaponics-centered eco-industrial food parks under different seasonal weather conditions. The food park encompasses an aquaculture-hydroponic and anaerobic digestion system that mineralizes internal waste streams. The study prioritizes enhancing nitrogen and phosphorus use-efficiency and self-sufficiency. The results unveil specific optimal hydroponic system sizes for a standardized 100 m3 aquaculture volume. Cooler regions thrive around 0.6–1.0 ha hydroponic system, while warmer areas excel at 0.2–0.3 ha with corresponding nitrogen and phosphorus use-efficiencies of 90% and 67% and self-sufficiencies of 88% and 95%, respectively. A supernatant nitrification step with light supplementation improved the design with maximum self-sufficiency and high water use-efficiency of 88% for a hydroponic-aquaculture area-volume ratio of 0.22:100. Complete nitrogen and phosphorus self-sufficient, near zero-waste, food park was achieved by introducing mineralization of chicken manure for a hydroponic-aquaculture ratio of 1.1:100. Moreover, the food park yielded a 6.7-fold lower carbon footprint compared to an optimized traditional on-demand coupled aquaponics system with a hydroponic-aquaculture ratio of 0.15:100.

Silicon Solid State Battery: The Solid‐State Compatibility, Particle Size, and Carbon Compositing for High Energy Density

AbstractSolid‐state battery research has gained significant attention due to their inherent safety and high energy density. Silicon anodes have been promoted for their advantageous characteristics, including high volumetric capacity, low lithiation potential, high theoretical and specific gravimetric capacity, and the absence of lethal dendritic growth. Addressing concerns such as low conductivity, pulverization, fracture, dense solid electrolyte interface layer, and low coulombic efficiency has substantially improved the use of silicon electrodes in solid‐state batteries. Researchers have explored carbon additions, solid electrolyte suitability for Si anodes, pressure optimization, and particle size effects (nano/micro) to enhance energy density. Recent studies have investigated the conductivity mechanism, stack pressure, and anode‐solid electrolyte compatibility to improve energy density. Micro‐ and nano‐sized silicon have attracted attention in carbon‐based composites due to their exceptional conductivity, uniform distribution, efficient electron migration, and diffusion channels. The development of solid‐state batteries with high energy density, safety, and extended lifespan has been a major focus. This review sheds light on significant insights and strategic approaches for researchers working on solid‐state silicon‐based systems to overcome existing challenges.

Size Optimization of Truss Structures Based on Genetic Algorithm by Developing Single-point Crossover with Bit-wise Mutation Using FORTRAN Programming

A truss is a collection of axially loaded structural elements connected through pin joints. Optimization of truss structures indicates the determination of best possible conditions that are necessary for achieving the most economical design in terms of lowest possible weights of the truss elements. According to recent studies, Size, Topology and Shape based optimizations are the three possible independent optimization scopes for finding the optimum weight of a truss. The current study focuses on the size optimization of truss structure and aims to identify the most cost-effective sections of the truss using a powerful evolutionary optimization technique named as Genetic Algorithm (GA). Here single point cross over method and bit wise mutation operator are adopted for expanding the search space with the assigned displacement and stress constraints. Fortran based sub routines are developed for each individual steps of GA such as fitness evaluation, selection, cross-over and mutation. Finaly the complete program is run for validation of its outcome. While comparing with the solution of an existing literature regarding a minimization of objective function having two design variables the outcomes tend to show a good correlation. Further, the study is extended for the weight minimization of two widely used truss having 10 members and 17 members respectively with standard Finite Element Method of analysis. Minimum weight of each truss is obtained via convergence plot and compared with the existing literatures. With this simple but efficient global optimization technique optimum weight of truss structure can easily be achieved.