Abstract

Flow channel or flow field designs have a significant impact on the performance of a Proton Exchange Membrane Fuel Cell (PEMFC). These channels are essential for distributing reactant gases, eliminating byproducts like water, and managing heat within the cell. Enhancing the flow field design can improve the efficiency, power output, and overall lifespan of PEMFCs. A three-dimensional multiphysics, two-phase PEMFC model with novel ribs in the form of asymmetrical airfoil in a parallel flow channel has been investigated in the present study. The ribs are placed along the length of the channel equidistantly. With the operating parameters kept constant, effect of different types of ribs on the overall performance of the fuel cell is reported and compared with the Baseline case where no ribs are present. The present model comprises of a single unit PEMFC modelled in the commercial software package ANSYS Fluent version 2023R2. A comparative study on the effects of different ribs shapes like rectangular, circular and asymmetrical airfoil on the overall PEMFC performance is presented. The obstruction in the flow channel is observed to improve the polarization curve, i.e., current vs. voltage (I-V) curve as well as current vs. power (I-P) curve, in other words, the fuel cell performance. Pressure drop is found higher for parallel flow channel with rectangular ribs than baseline without ribs. Thus, this study examines how a different flow channel design with ribs affects PEMFC performance metrics, offering insights into how changes in flow field configuration can propel advancements in fuel cell technology.

Abstract

Extracting heat from a deep geothermal reservoir in a clean and efficient manner has always posed a significant challenge in geothermal exploration. This paper presents a numerical study of the heat extraction performance of a perfect super-long gravity heat pipe (SLGHP) under the assumptions of either an infinitely large heat transfer coefficient inside the SLGHP or a constant phase change temperature throughout the SLGHP. As the phase transition temperature of the working fluid in the pipe may be higher than the temperature of the surrounding formation, it is necessary to eliminate the possibility of heat loss at the top of the SLGHP by setting it to zero or providing a fully thermally insulated boundary. The results show that the heat output and heat output per unit wellbore length increase linearly with well depth, and the greater the given geothermal gradient and formation thermal conductivity, the larger the heat output and heat output per unit wellbore length

Abstract

Indoor air quality is an important factor which affects the cognitive ability and performance of the occupants of a building, hence, fresh air is incorporated into indoor spaces. However, due to high ambient temperatures, typically in tropical zones, will further increase the energy consumption of HVAC systems. Phase change materials (PCM) are widely employed due to their high energy storage capacity as well as keeping the operating temperature unchanged. The study explores the effect of using a PCM-based heat exchanger for reducing the fresh air temperature and, hence reducing the fresh air ventilation load of a building. On using octadecane as PCM, the study observed that for 8 hours of operation, the average temperature reduction in the air for no fins is 1.80 °C, which increases marginally to 1.82 °C for 1% CuO and 4.15 °C on using 24 longitudinal fins. It leads to an energy saving of 3.51%, 3.83% and 7.75% over a conventional HVAC system for a system with no fin, 1% CuO, and 24 fins, respectively. Further, PCM is charged using exhaust air, and PCM solidifies completely in 4 hours. The study provides an energy-efficient solution for the sustainable use of HVAC systems for policy makers and HVAC engineers.

Abstract

This paper presents a comparison of different statistical and machine learning models using five groups of input features to forecast the future demand of a region, comprising 5 adjacent cities (Lahore, Sheikhupura, Kasur, Okara, Nankana), managed by a single government-operated utility. The proposed methodology was constructed using hourly data of electricity load in MW and hourly data of three weather variables with the highest correlation with load, for more than 10 years i.e. from July 2013 to Dec 2023. We have applied LM, SVM, and LSTM models, the most widely used techniques in literature for long term load forecasts. In three years of out-of-sample forecasts with 26041 timesteps, the SVM model performed best with a MAPE value of 2.98% applying ARIMA with Kalman smoothing to fill missing load values for 4 months. However, LSTM performed 2.5 times better than LM and SVM if input features are only weather variables and historical hourly load is not provided as input to training and test sets.

Abstract

The goal of concurrently addressing green transformation (GX), in this study, we aimed to reconcile exploring regional optimization in smart grids and renewable energy variability in Hitachi, Japan. The breakdown of CO2 emissions among Hitachi City’s sectors was estimated because of the lack of detailed data in the Ministry of Environment’s Municipal Emissions Carte. To foster GX, solar power generation using renewable energy are unstable because they are affected by the weather, and depending on the scale of installation, EVs are equipped with storage batteries, and depending on the time of day, they can be charged as a buffer for surplus electricity from renewable energy. If Hitachi City were to install about 1,000MW of solar panels, it could become a zero-carbon city by 2050.

Abstract

Reversible solid oxide cells (ReSOCs) stack consume electrical energy to produce chemical fuel in the electrolyser (SOEC) mode of operation and convert stored fuel into electrical energy during the fuel cell (SOFC) mode of operation. On the whole, the stack operation is endothermic in the SOEC mode and exothermic in the SOFC mode. A mutually compatible balance-of-the-plant (BoP) with thermal integration for the dual mode of operation is necessary for the ReSOC stack. A detailed process model of ReSOC of MWe scale capacity is developed with two layouts for the BoP: an oxygen-based system and an air-based system. Auxiliary power generation in both modes improves the efficiency of the system. Plant-level compatibility shows the plant can be operated at a 1:2.5 charging-to-discharging time ratio, thus making it highly suitable for islanded power operation of the daily residential energy requirement driven primarily by a solar PV-based renewable energy system. In terms of overall performance, the oxygen-based system shows a roundtrip efficiency of around 70% compared to around 55% for the air-based system. This difference may be attributed to air compression in both modes which requires significantly higher parasitic power consumption.

Abstract

Data centers are energy-intensive infrastructures that generate, manage, and store information for our interconnected society. Models based on Artificial Intelligence (AI) such as ChatGPT are increasingly accessible, leading to significant energy consumption and associated carbon emissions.
Assessing the carbon footprint of AI data centers is essential for evaluating their environmental impact and, consequently, promoting responsible AI development and encouraging sustainable practices. In this work, we evaluate an AI data center’s carbon footprint using a life cycle assessment approach. Unlike existing literature, we analyze the entire data center architecture rather than solely focusing on the servers’ footprint. Additionally, we assess the impact of varying the electricity mix and extending the lifetime of servers, providing potentials for emission reductions.

Abstract

This study evaluates the effectiveness of SHAP (SHapley Additive exPlanations) as a feature selection tool for national demand forecasting, addressing the need for a comprehensive understanding of its strengths and limitations. We benchmark SHAP against common selection methods, including variance-based selection, XGBoost, and NSGA-II. Using a 1D Convolutional Neural Network to forecast hourly national energy demand in Panama, we compare the performance of features selected by each method. Our results indicate that SHAP is generally outperformed by XGBoost and may not be the most effective initial approach for feature selection, nor does it consistently rank features by their predictive value. However, SHAP proves more effective in refining smaller sets of features, maintaining model accuracy by eliminating non-contributive features

Abstract

Shallow geothermal utilization systems (especially GSHP systems) are used for heating and cooling, hot water supply, snow melting, and other applications. They are an energy-saving technology with the potential to decarbonize and reduce utility costs. However, GSHP systems have been slower to spread in Japan than in the other countries due to the high cost of GSHP system installation, lack of awareness, and lack of hydrogeological information. Therefore, the Shallow Geothermal and Hydrogeology Team of AIST is engaged in research on social implementation to popularize shallow geothermal utilization system, and this report introduces an example of such research.

Abstract

With the widespread adoption of Carbon Capture and Storage (CCS) technologies and investigations into advanced power cycles such as the supercritical CO2 (sCO2) Brayton cycle, it is particularly crucial to focus on the risks and dynamics associated with accidental leaks under supercritical conditions. One key aspect is the accurate prediction of critical flow rates of supercritical fluids during leakage events. Owing to the scarcity of experimental data on critical flow in sCO2 microchannels, this study harnesses the abundant experimental data from critical flow studies of water, employing machine learning algorithms enhanced by transfer learning to predict the critical flow rates of sCO2. The approach involves pre-training a neural network on critical flow data from water, followed by fine-tuning with sCO2 data, thereby bridging the gap in data availability and enhancing the model’s generality across various parameters. The principal research findings indicate that transfer learning can improve prediction accuracy and adaptability, suggesting that transferring knowledge from extensively studied fluids like water can effectively enhance the predictive performance of less-documented supercritical fluids. This research provides a valuable tool for designing safer CCS and energy systems.