Energy Saving and Energy Efficiency Technologies

Water supply represents a significant electrical load worldwide. The operation of inefficient pumps contributes to increased energy demand and carbon footprint. Current methods for evaluating industrial water pumps present technical and economic challenges that need to be addressed. For these reasons, we developed a practical and cost-effective virtual instrument called the Pumping Evaluation System (PES). The PES tool performs real-time monitoring of electrical, hydraulic, and efficiency data, as well as the Operating Characteristic (OC) curves. The PES tool was implemented on a vertical pump currently used by a municipal water distribution plant in Mexicali, Mexico. A comparative analysis between the experimental data and the manufacturer’s data revealed potential energy savings of 361,455 kWh/year (24.35%) and an equivalent carbon footprint reduction of 157,233 kg of CO_2eq/year if the old pump is replaced by a new one, which is the lowest cost alternative according to a Life Cycle Cost (LCC). Full article

The marine ranching industry in China is transitioning from traditional farming to a digital and intelligent model. The use of new technologies, algorithms, and models in the era of artificial intelligence (AI) is a key focus to enhance the efficiency, sustainability, and resilience of marine ranch operations, particularly in risk and disaster management. This study proposes a methodology for applying deep reinforcement learning to decision making in this domain. The approach involves creating an environmental model based on decision objects and scenarios, determining the number of decision makers, and selecting a single or multi-agent reinforcement learning algorithm to optimize decision making in response to randomly generated disasters. Three core innovations are presented: the development of a disaster simulator for marine ranching scenarios, the application of reinforcement learning algorithms to address risk and disaster management problems in marine ranching. Future research could focus on further refining the methodology by integrating different data sources and sensors and evaluating the social and economic impacts of AI-driven marine ranching. Overall, this study provides a foundation for further research in this area, which is expected to play an increasingly important role in global food production, environmental sustainability, and energy efficiency. Full article

Buildings are responsible for approximately 40% of the world’s energy consumption and 36% of the total carbon dioxide emissions. Building occupancy is essential, enabling occupant-centric control for zero emissions and decarbonization. Although existing machine learning and deep learning methods for building occupancy prediction have made notable progress, their analyses remain limited when applied to complex real-world scenarios. Moreover, there is a high expectation for Transformer algorithms to predict building occupancy accurately. Therefore, this paper presents an occupancy prediction Transformer network (OPTnet). We fused and fed multi-sensor data (building occupancy, indoor environmental conditions, HVAC operations) into a Transformer model to forecast the future occupancy presence in multiple zones. We performed experimental analyses and compared it to different occupancy prediction methods (e.g., decision tree, long short-term memory networks, multi-layer perceptron) and diverse time horizons (1, 2, 3, 5, 10, 20, 30 min). Performance metrics (e.g., accuracy and mean squared error) were employed to evaluate the effectiveness of the prediction algorithms. Our OPTnet method achieved superior performance on our experimental two-week data compared to existing methods. The improved performance indicates its potential to enhance HVAC control systems and energy optimization strategies. Full article

This article summarizes the state-of-the-art knowledge gained from field observations and laboratory studies regarding foam as a liquid controlling agent in porous media. Being the least explored property of foam, its effect and potential have often been overlooked or simply ignored. The aim with this review is therefore to demonstrate the abilities that foam could have to block, reduce, delay, suppress, or divert water flow in porous media. As a liquid controlling agent in porous media, foam has potential for industrial processes that involve fluid injections or fluid withdrawals in porous geological formations, such as improved/enhanced oil recovery (IOR/EOR), matrix-stimulation treatments, underground storage of CO₂, hydrogen, compressed-air or natural gas withdrawal, geothermal energy, and contaminated soil-groundwater remediation processes with unwanted aquifer impacts. Improving the water utilization factor and water management in these applications might result in tremendous energic, economic, and environmental incentives that are worth pursuing. Specific focus in this review is given to the post-foam water injection, which determines the ultimate stability and water-blocking capabilities of the foam treatment. Main parameters and mechanisms that can influence foam stability against water injection/intrusion after generation and placement are assessed and discussed. Unresolved issues are highlighted, which give recommendations for further research and field-scale operations. Full article

The integrated solar combined cycle (ISCC) system is a proven solution for grid-connected power generation from solar energy. How to further improve the ISCC system efficiency and propose a more efficient system solution has become a research focus. A novel gas turbine combined cycle (GTCC) benchmark system is proposed by replacing the conventional steam Rankine bottom cycle with a supercritical CO₂ Brayton cycle, whose output power and efficiency are increased by 9.07 MW and 1.3%, respectively, compared to those of the conventional GTCC system. Furthermore, the novel ISCC systems are established with the parabolic trough solar collector (PTC) and the solar tower (ST) collector coupled to the novel GTCC system. Thermal performance analysis, exergy performance analysis, and the sensitivity analysis of the ISCC systems have been performed, and the results show that the system efficiencies of both ISCC systems are lower than that of the GTCC system, at 57.1% and 57.5%, respectively, but the power generation of the ISCC system with PTC is greater than that of the benchmark system, while that of the ISCC system with ST is less than that of the benchmark system. The photoelectric efficiency of the ISCC system with PTC is 27.6%, which is 2.1% greater than that of ISCC system with ST. In the ISCC system with PTC, the components with the highest exergy destruction and the lowest exergy efficiency are the combustion chamber, and PTC, respectively. ST is the component with the highest exergy destruction and the lowest exergy efficiency in the ISCC system with ST. With the increase in direct normal irradiance (DNI), the total output power, solar energy output power, and photoelectric efficiency of the ISCC system with PTC increase, while the system efficiency decreases; the solar energy output power and photoelectric efficiency of the ISCC system with ST increase, while the total output power and system efficiency decrease. The photoelectric efficiency of the ISCC system with PTC is greater when the DNI is greater than 600 W/m²; conversely, the photoelectric efficiency of the ISCC system with ST is greater. After sensitivity analysis, the optimal intercooler pressure for the ISCC system is 11.3 MPa. Full article

In order to address the issue of power loss resulting from partial shadow and enhance the efficiency of photovoltaic power generation, the photovoltaic array reconfiguration technology is being increasingly utilized in photovoltaic power generation systems. This paper proposes a reconfiguration method based on improved hybrid particle swarm optimization (HPSO) for the photovoltaic array of TCT (total-cross-tied) structure. The motivation behind this method is to get the best reconfiguration scheme in a simple and efficient manner. The ultimate goal is to enhance the output power of the array, save energy, and improve its overall efficiency. The improved HPSO introduces the concept of hybridization in genetic algorithms and adopts a nonlinear decreasing weight method to balance the local search and global search ability of the algorithm and prevent it from falling into the local optimal solution. The objective function used is the variation coefficient of the row current without the weight factor. This approach saves time and balances the row current of the array by altering the electrical connection of the component. In the 4 × 3 array, the improved HPSO is compared with the Zig-Zag method. In the 9 × 9 array, the improved HPSO is compared with the CS (competence square) method and the improved SuDoKu method. The simulation results show that the power enhancement percentage of the improved HPSO is between 6.39% and 28.26%, and the power curve tends to single peak characteristics. The improved HPSO has a smaller mismatch loss and a higher fill factor in the five shadow modes, which can effectively improve the output power, and it is convenient to track the maximum power point later. Full article

Two prototypes with energy efficiency calculations have been developed to enable real-time efficiency assessment and data collection. The results of the experiment demonstrate that the use of microprocessor technology and the Internet of Things can significantly improve the efficiency and accuracy of energy audits in power transformers. The prototypes developed in this study provide real-time efficiency assessment and data collection, enabling more effective energy management and cost savings for industrial users. During the experiment, it was found that resonance can cause the same losses as a poor power factor of the system, highlighting the importance of addressing energy quality issues in addition to energy efficiency. These findings have important implications for energy efficiency policies and practices in the context of climate change mitigation and rising energy prices. Full article

Sports centres constitute major energy consumers. This article presents the proposed energy performance upgrade process and the achieved results for the municipal sports centre in Arkalochori, Greece. The facility consists of a swimming pool centre, an outdoor 8 × 8 football court, and two tennis and basketball courts. It operates with considerably high energy consumption due to the lack of any measure towards its energy efficiency improvement since its initial construction in 2002. Due to the significantly high heating cost, the swimming pool centre remains operative only during the summer period. The energy performance upgrade of the facility was holistically approached through all possibly applicable passive and active measures: insulation of opaque surfaces and replacement of openings, construction of a new, bioclimatic enclosure for the swimming pool’s centre and conversion of the current outdoor facility to an indoor one, installation of heat pumps for indoor space conditioning and swimming pool heating, installation of a solar–combi system for domestic hot water production, upgrade of all indoor and outdoor lighting equipment and installation of a photovoltaic plant on the new enclosure’s roof for the compensation of the remaining electricity consumption. With the proposed measures, the municipal sports centre is upgraded to a zero energy facility. The payback period of the investment was calculated at 14 years on the basis of the avoided energy procurement cost. The swimming pool’s centre operation is prolonged during the entire annual period. This work has been funded by the Horizon 2020 project with the acronym “NESOI” and was awarded the public award of the “Islands Gamechanger” competition of the NESOI project and the Clean Energy for EU Islands initiative. Full article

Integrated solar combined cycle (ISCC) systems play a pivotal role in the utilization of non-fossil energy; however, the efficient application of solar energy has emerged as a primary issue in the study of ISCC systems. Therefore, it is extremely urgent to propose the best optimization scheme for ISCC under different operating conditions. In this paper, according to the idea of temperature matching and cascade utilization, the optimization of the ISCC system is carried out with the genetic algorithm for the whole working conditions, and the optimization schemes with the highest photoelectric efficiency and system efficiency under different working conditions are derived. In comparison with two optimization schemes with different objective functions, the conclusion can be drawn that: At 100% gas turbine load—30% DNI and 100% gas turbine load—100% DNI working conditions, respectively, the maximum system efficiency of 56.32% and the maximum solar photoelectric efficiency of 35.5% are attained. With the decreasing of gas turbine load, the solar energy integration position will gradually change from the topping cycle to the bottom cycle; with the gas turbine load variation from 100% to 75%, the optimal photoelectric efficiency model prefers two-stage integration, and up to 141.3 MW of solar energy could be integrated, which is greater than the maximum value of 127.1 MW for the optimal system efficiency model. Regarding the heat collection choice of bottom cycle, the optimal photoelectric efficiency model prefers the high-pressure boiler (HPB), while the optimal system efficiency model prefers the high-pressure superheater (HPS). The comparison between the optimal solution and the actual cases confirms the correctness of the optimization results and provides guidance for the subsequent ISCC study. Full article

The present study investigates electricity consumption, carbon dioxide (CO₂) emission, and economic growth decoupling using data from 1971 to 2020 for the economy of China. The study uses decoupling analysis (DA) as the prime methodology for analysis. Furthermore, the findings put forward a significant contribution to an economic picture of the economy of China and a sizeable addition to related research and findings under the assigned issues discussed in the study. The study’s main contribution is to decouple electricity consumption from the gross domestic product (GDP), which is rare in the existing literature in the context of China. Moreover, the study shows the decoupling of environment affects electricity consumption, and GDP growth. The DA model shows that electricity consumption is the main driving force enhancing economic growth. However, industrialization has increased greenhouse gases, global warming, and climate change due to production and consumption. China’s economy uses coal for energy resources, which indicates that China produces a large proportion of electricity with coal, which causes high CO₂ emissions. Finally, further analysis with the Granger causality test confirms the main findings. Full article

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. Full article

Passenger cars account for the largest share of GHG emissions in the road sector. However, given that the number of heavy-duty vehicles registered is lower but accounts for about a quarter of GHG emissions in the road sector, it is necessary to reduce carbon dioxide (CO₂) emissions by improving the fuel efficiency of heavy-duty vehicles. However, experiments using dynamometers during the vehicle development process consume a lot of time and cost. Conversely, simulations can quantitatively analyze the sensitivity of parameters and accelerate optimization. Therefore, in this study, we modeled a heavy-duty vehicle using an AVL Cruise simulation and analyzed the effects of payload, air drag coefficient, and rolling resistance on fuel economy, CO₂ emission, and the valid window ratio among the moving average window (MAW) for three driving routes. When the average vehicle speed was higher, the effect of the air drag coefficient on fuel economy was high. Additionally, when the average vehicle speed was lowered, the effect of the reduced rolling resistance on improving fuel efficiency was higher than that of the reducing air drag. Thus, the fuel efficiency improvement rate according to each 10% decrease in rolling resistance was higher by 2.2%, on average, in the low average speed route. Additionally, it was confirmed that the valid window ratio was high when driving in a section with a high vehicle speed first. Thus, the valid window ratio was almost 100% in the test of the route conditions starting from the highway section. Full article

Nowadays, mobility represents a key sector to achieve the goal of carbon neutrality. Indeed, the development of hybrid powertrains is contributing to a reduction in the environmental impact of vehicles. One of the most promising energy-saving solutions is regenerative braking, which enables deceleration while recovering energy, otherwise wasted. Even though much scientific community effort has been addressed to the optimization of this technology in the automotive field, the increase of energy storage systems efficiencies enables the overcoming of the constraints related to the reuse of electric energy in railway vehicles. This solution could be extremely useful for those railway vehicles which operate on non-electrified lines, where traction is usually provided by diesel engines. For this reason, the present work focuses on how regenerative braking technology could be exploited in diesel-powered rail applications. In further detail, a diagnostic train working on real railway lines has been considered as a case study. Given the real duty-cycle of the vehicle, a simulation model has been developed with the aim of evaluating the amount of energy recovered during braking phases and, consequently, the fuel saving and the avoided CO₂ emissions. As a result, the analysis shows an improved energy efficiency of propulsion system. Compared with a pure diesel operation, it leads to fuel savings of 20%, a reduction of CO₂ emissions of 22.3 kg with 23.25 kWh stored in the battery at the end of the route. Full article

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles. Full article

Trains are a large-capacity means of transportation, and they are preferred for long as well as short distances. Although trains are one of the most efficient modes of transportation for freight and passengers, they consume a significant amount of energy. Therefore, energy-efficient approaches have been studied over the years. Various optimal-control methods that integrate dynamic programming (DP) algorithms have been introduced to reduce the overall energy consumption. The purpose of optimizing the operation speed of the train according to the operating conditions using the DP algorithm is to find a speed profile that consumes minimum energy, under the condition that the target travel time is satisfied according to the given mileage. Here, a specific weight is applied to the cost function to find a velocity profile that satisfies the target travel time. In this case, the computation time increases proportionally to the number of times the weight is changed. In addition, because the weight versus the target travel time has a non-linear characteristic, various approaches have been proposed to reduce the number of iterations according to the weight change to satisfy the target travel time. This study suggests a method to quickly and effectively find the optimal solution for electric trains in a different way from previous studies. We present a DP algorithm for matrix processing, by arranging multiple weights within the applicable minimum and maximum weights and applying them to the cost function. The time taken to find the optimal solution can be reduced by half compared to the existing one, and the travel time and energy consumption corresponding to each weight can be checked at once. In addition, this result can be used as an indicator for effectively changing or establishing an electric-train operation plan. For a detailed comparison between the proposed and existing methods, the execution time results for each number of weights under the same calculation conditions are presented. In addition, to verify that there are no errors in the multi-weighting process, some of the multi-weighting coefficients were used to check whether the speed profile in the single-weighted calculation method was consistent. Full article

Natural gas is a fossil fuel that has been widely used for various purposes, including residential and industrial applications. The combustion of natural gas, despite being more environmentally friendly than other fossil fuels such as petroleum, yields significant amounts of greenhouse gas emissions. Therefore, the optimization of natural gas consumption is a vital process in order to ensure that emission targets are met worldwide. Regarding residential consumption, advancements in terms of boiler technology, such as the usage of condensing boilers, have played a significant role in moving towards this direction. On top of that, the emergence of technologies such as smart homes, Internet of Things, and artificial intelligence provides opportunities for the development of automated optimization solutions, which can utilize data acquired from the boiler and various sensors in real-time, implement consumption forecasting methodologies, and accordingly provide control instructions in order to ensure optimal boiler functionality. Apart from energy consumption minimization, manual and automated optimization solutions can be utilized for balancing purposes, including natural gas demand response, which has not been sufficiently covered in the existing literature, despite its potential for the gas balancing market. Despite the existence of few research works and solutions regarding pure gas DR, the concept of an integrated demand response has been more widely researched, with the existing literature displaying promising results from the co-optimization of natural gas along with other energy sources, such as electricity and heat. Full article

This paper ascertained the performance of the evacuated tube solar water heater (SWH) coupled with an auxiliary electric heater with reference to the replaced electric water heater with the same storage tank capacity (200 L) in a building. It also examines the influence of the uptake of the SWHs in the community due to different campaign methods. The study evaluated the performance of a 4 kW electric water heater and a 2 kW input SWH with an auxiliary electric heater, and quantified the annual energy and cost savings. A survey using questionnaires was conducted among 150 residences in Dimbaza based on the house representative’s perceptions to replace their electric water heaters with solar water heaters (based on the monetary saving inscribed on the solar water heaters, the sensitization of the target population on the environmental benefits of the solar water heaters and both the monetary savings and environmental benefits). The findings revealed that by replacing the electric water heater with the solar water heater with an auxiliary electric heater, the annual electricity savings due to hot water heating was 4408.99 kWh and the net present value payback period was 4.32 years. The desire of the household representatives to replace their existing electric water heaters with solar water heaters due to the campaign strategies increased from 75 to 126. This study is capable of providing a mechanism to increase the penetration of solar water heaters and justifying the techno-economic viability of solar water heaters. Full article

This study investigates improvements in low-cost latent heat storage material calcium chloride hexahydrate (CaCl₂.6H₂O). Its melting point is between 25 and 28 °C, with relatively high enthalpy (170–190 J/g); however, this phase change material (PCM) shows supercooling and phase separation. In CaCl₂.6H₂O incongruent melting causes lower hydrates of CaCl₂ to form, which affects the overall energy storage capacity and long-term durability. In this work, PCM performance enhancement was achieved by adding SrCl₂.6H₂O as a nucleating agent and NaCl/KCl as a stabilizer to prevent supercooling and phase separation, respectively. We investigated the PCM preparation method and optimized the proportions of SrCl₂.6H₂O and NaCl/KCl. Thermal testing for 25 cycles combined with DSC and T-history testing was performed to observe changes in enthalpy, phase transitions and supercooling over the extended period of usage. X-ray diffraction was used to verify crystalline structure in the compounds. It was found that the addition of 2 wt.% of SrCl₂.6H₂O reduced supercooling from 12 °C to 0 °C compared to unmodified CaCl₂.6H₂O. The addition of 5 wt.% NaCl or KCl proved to effectively suppress separation and the melting enthalpy achieved was 169 J/g–178 J/g with congruent melting over 25 cycles, with no supercooling and almost no reduction in the latent heat. Full article

Due to the chronic shortage of energy-related analytical data and disintegration of building energy regulations, numerous existing residential buildings in Petra (Jordan) and many cities worldwide suffer from poor building energy design. This paper aims at investigating the potential of applying energy-saving standards in order to improve the whole-building energy consumption of low-rise residential buildings in mild and dry climate zones. Representative buildings were selected based on a field survey. Proposed strategies focused on applicable solutions such as envelope components, and energy-related systems were set. The models were created using Autodesk Revit, and then the results were generated by the EnergyPlus engine. The findings showed that the application of building energy standards greatly impacts the overall energy end-use, where up to 30% reduction can be achieved by applying the Jordanian code, and up to 45% by applying the American standard. This work provides guidance for the residential building industry and policymakers in Jordan and many other countries with similar building characteristics and climate zones. Full article

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab^® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation. Full article

The rapid transition from an efficiency-oriented to a renewable energy-based green environment raises questions about the sustainability of cogeneration models in the coming era of climate change. For securing the technological competitiveness of a cogeneration model in terms of sustainability, it is essential to come up with alternatives that can flexibly respond to changes in the market conditions. From the surveyed field operation data of the cogeneration model applied to an apartment complex, it was found that the actual operation performance may differ significantly from the theoretical expectation. Through diagnostic simulation analysis, the main cause of the disappointing performance in the case of the current cogeneration model after installation has been assessed, and the importance of a consistent operation strategy was demonstrated by the event-based correlation analysis based on field operation data. The impact of the rapid expansion and dissemination of the renewable energy market on the relative primary energy savings benefit evaluation of the cogeneration model was analyzed for various operating conditions. Full article

Water-pumped storage systems have become an ideal alternative to regulate the intermittent power delivered by renewable energy sources. For small-scale operations, a type of centrifugal pump coupled to asynchronous machines represents an adequate solution due to their techno-economic feasibility in addition to their ability to operate as reversible systems. This work provides a novel port-Hamiltonian modelling approach to an integrated reversible hydropump–turbine system, that can be switched from motor pump to turbine-generator by employing a conventional hydraulic switch. Our modelling strategy provides a clear physical interpretation of the energy flow from the mechanical to electrical domains. Then, the model was built with multi-domain storing and dissipating elements and the interconnection of well-defined input–output port pairs. The system’s internal energy, i.e., Hamiltonian function, can be exploited for energy-shaping control strategies. The performance of our modelling approach is validated via numerical simulations. Full article

Off-chip memory access has become the performance and energy bottleneck in memory-constrained neural network accelerators. To provide a solution for the energy efficient processing of various neural network models, this paper proposes a dataflow optimization method for modern neural networks by exploring the opportunity of single-layer and inter-layer data reuse to minimize the amount of off-chip memory access in memory-constrained accelerators. A mathematical analysis of three inter-layer data reuse methods is first presented. Then, a comprehensive exploration to determine the optimal data reuse strategy from single-layer and inter-layer data reuse approaches is proposed. The result shows that when compared to the existing single-layer-based exploration method, SmartShuttle, the proposed approach can achieve up to 20.5% and 32.5% of off-chip memory access reduction for ResNeXt-50 and DenseNet-121, respectively. Full article

The rate of technological progress is an important metric used for predicting the energy consumption and greenhouse gas emissions of future light-duty fleets. A trade-off between efficiency and performance is essential due to its implications on fuel consumption and efficiency improvement. These values are not directly available in the Brazilian fleet. Hence, this is the main gap in knowledge that has to be overcome. Tendencies in all relevant parameters were also unknown, and we have traced them as well, established on several publications data and models. We estimate the three indicators mentioned above for the Brazilian fleet from 1990 to 2020. Although the rate of technological progress was lower in Brazil than that in developed countries, it has increased from 0.39% to 0.61% to 1.7% to 1.9% in subsequent decades. Performance improvements offset approximately 31% to 39% of these efficiency gains. Moreover, the vehicle market is shifting toward larger vehicles, thus offsetting some efficiency improvements. We predict the fleet fuel efficiency for the years 2030 and 2035 using the above-mentioned factors. The predicted values for efficiency can vary by a factor of two. Thus, trade-off policies play a vital role in steering toward the desired goals of reducing the transportation sector’s impact on the environment. Full article

For the requirements of rigorous CO₂ and emissions regulations, steam assist technology is an effective method for thermal efficiency enhancement. However, few studies apply steam assist technology in modern internal combustion engines. Stimulated by its application prospects, the present study proposes a thermodynamic analysis on the in-cylinder steam assist technology. An ideal engine thermodynamic model combined with a heat exchanger model is established. Some critical parameters, such as steam injection temperature, injection pressure and intake pressure, are calculated under different steam injection masses. The thermal efficiency boundaries are also analyzed at different compression ratios to investigate the maximum potential thermal efficiency of the technology. The analysis shows that the in-cylinder steam-assisted cycle has the potential to increase engine efficiency considerably. Both steam injection temperature and injection mass improve thermal efficiency. Considering the energy trade-off relationship between steam and exhaust gas, the maximum gain in thermal efficiency achieved with the cycle is 14.5% at a compression ratio of 10. The optimum thermal efficiency can be increased from 54.0% to 59.71% by increasing the compression ratio from 10 to 16. The mechanism lies in the specific heat ratio enhancement from a thermodynamic perspective, which improves the thermal-heat conversion efficiency. The results provide considerable guidance for the future experimental and numerical studies of in-cylinder steam assist technology into modern engines. Full article

A one-dimensional dual-pressure steam turbine (ST) model for the marine Rankine cycle is built in this paper. Based on constructal theory, the optimal design of the dual-pressure ST is performed with a fixed total volume of the high- and low-pressure STs. The total power output (PO) of the dual-pressure ST is maximized. Seventeen parameters, including the dimensionless average diameters (DADs) of the stages, steam inlet angles (SIAs) of the stages, average reaction degrees (ARDs) of the stages, and volume ratio of the high-pressure ST are taken as optimization variables. The optimal structure parameters of the stages are gained. It reveals that the total PO of the dual-pressure ST is increased by 2.59% by optimizing the average diameter of the Curtis stage, and the change in the total PO is not obvious by optimizing the average diameter of the third stage of the low-pressure ST. Both the total PO and the corresponding efficiency of the dual-pressure ST are increased by 10.8% after simultaneously optimizing 17 variables with the help of the Matlab optimization toolbox. The novelty of this paper is introducing constructal theory into turbine performance optimization by varying seventeen structure, thermal and flow parameters, and the result shows that the constructal optimization effect is remarkable. Optimal designs of practical STs can be guided by the optimization results gained in this paper. Full article