bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Modified Simulated Annealing-Based Method for Hybrid Lattice Support Structure Design in LPBF Additive Manufacturing
Authors: White Lisha , Liang Xuan , Zhang Guanglu , Cagan Jonathan , Zhang Yongjie Jessica 
Published: September 2024

Abstract
When designed effectively, support structures play a critical role in quickly dissipating heat and mitigate part distortion without driving up excessive costs within the additive manufacturing metals technique of Laser Powder Bed Fusion (LPBF). Lattices, composed of individual unit cells strategically arranged to achieve a desired function, are a promising solution as a support structure. Prior research utilizing gradient-based optimizers to design lattice support structures for heat dissipation poses challenges regarding limited design domain exploration and non-differentiable objective functions. Non-gradient-based optimizers are an alternative solution but existing optimizers, such as traditional simulated annealing (SA), are known to be more computationally expensive compared to gradient-based optimizers, rendering it challenging to optimize the heat dissipation of lattice support structures. This paper introduces a modified SA-based method to design lattice structures for LPBF by efficiently optimizing the distribution of a library composed of various types of unit cells, thereby creating hybrid lattice support structures (hLSS). A stage-dependent annealing swapping strategy is created and integrated into the method for efficient design domain exploration. Homogenization approximation and equivalent static loading are also performed in each iteration step to make the design optimization process computationally tractable. Two case studies validate the method by designing hLSS for a cantilever beam and a bracket. The results of these case studies show the method's ability to achieve material cost savings of up to 61% and post-processing cost savings of up to 62% when compared to a solid support domain while satisfying manufacturing constraints.
Acquired Angle Error Correction Based on Variation of an Angle Detection Signal Intensity in Rotary Encoders
Authors: Sato Akihiko , Kakinuma Yasuhiro 
Published: September 2024

Abstract
Rotary encoders are used in various machines to measure the angular position of a rotating axis. Therefore, rotary encoders are required not only to be highly accurate, but also usable in various environments. When using a rotary encoder on a machine, acquired angle errors occur due to the eccentricity between the encoder and the machine, the deformation of the scale, and the shaft runout of the rotation axis. Various methods have been adopted to reduce such angle errors. However, these methods have problems such as the large size and high cost of the encoder, the necessity of additional devices for measuring, and increasing installation labor, so the environment in which they can be implemented is limited. In this study, we have proposed a method to correct the acquired angle errors based on variation of an angle detection signal intensity in the sensor head. This method can correct the errors on the machine without using multiple sensor heads or additional devices. The angle error estimated by this method is compared with that measured by the external equipment of a reference encoder. While the maximum angle error was 28.36″ without correction, the residual error with correction of the acquired error can be reduced to 1.75″ or less. From this result, the proposed method can improve the measurement accuracy of the rotary encoder already installed on the machine. Therefore, this method is an effective means for achieving both high rotational accuracy on machines and ease of correcting an error in rotary encoder machines.
Improving Control of Energy Systems With Reinforcement Learning: Application to a Reversible Pump Turbine
Authors: Tubeuf Carlotta , aus der Schmitten Jakob , Hofmann René , Heitzinger Clemens , Birkelbach Felix 
Published: September 2024

Abstract
Pumped hydro storage power systems are crucial to account for grid instabilities by providing flexibility services. To further increase flexibility, the acceleration of switching between operating modes is necessary. This can be achieved through precise and automated process control with reinforcement learning (RL). Besides the benefits of RL, safety concerns inhibit industrial-scale applications for process control with RL.
We present measures to increase the reliability and stability of RL algorithms to enable applications for the control of energy systems. We demonstrate the viability of our approach by applying it to the control of the pump start-up process of a reversible pump turbine. To train the RL algorithm, we use a simulation model that accurately represents the test rig of a pump turbine located at the laboratory of TU Wien. Our results show that RL is suitable for finding optimal control strategies that can compete with traditional approaches. However, finding the optimal policy still requires a lot of computational effort. Future research will focus on optimizing the RL framework and then transferring the results to the real machine unit at the test facility.
Investigation of Driver Gas Mixtures in a Shock Wave Reformer for Enhanced Hydrogen Pyrolysis
Authors: Madiot Ghislain , Tüchler Stefan , Akbari Pejman , Copeland Colin D. 
Published: September 2024

Abstract
The wave reformer, developed by New Wave Hydrogen, Inc. (NWH2), harnesses shock waves resulting from the pressure exchange between two gases to initiate thermal decomposition reactions in a hydrocarbon gas to generate hydrogen. This article investigates the influence of various operating parameters, including driver gas mixtures and operating pressure on the overall hydrogen conversion within an 8-ports wave reformer. The objective is to start with a peak pressure region and reaction zone away from the end-wall toward the center of the wave reformer, allowing for more time for high-temperature initiation.
The main intention of the work is to consider the role of the gas composition of the driver gas (energy input) on the pyrolysis of methane using shock-wave heating. This study provides a comprehensive comparative analysis of the effects of driver gas properties on the flow rate, velocity, temperature, and pressure distribution within the wave reformer. Utilizing a Quasi-2D (Q2D) model, simulations yield valuable insights into how these parameters impact the performance of the technology.
Key findings include the critical role of driven outlet back pressure in driving mass flow within the cycle and its subsequent influence on maximum temperature. Most interestingly, the choice of driver gases was found to profoundly influence the temperature and density fields and plays a significant role in the mass flow ratio of the two gases.
This research enhances our understanding of wave reformer technology and its sensitivity to various operational parameters. The insights gained are instrumental in optimizing wave reformer performance for efficient hydrogen conversion.
Optimal Operation of a District Heating System Using a PV-Assisted CO2 Heat Pump and Thermal Energy Storage
Authors: Soleimani Shima , Liaqat Kashif , Temming Jörg , Kösters Heiner , Schaefer Laura 
Published: September 2024

Abstract
This work examines a combined-component, fifth-generation district heating system (DHS) with an emphasis on CO2 emission reduction and greater adaptability to diverse heat sources. There are two primary contributions resulting from this analysis. First, a mathematical framework is created to simulate a combined photovoltaic (PV)-assisted CO2 heat pump (HP) with thermal energy storage (TES) to provide domestic hot water (DHW) for a district of 13 houses. Subsequently, this paper applies a mixed-integer nonlinear optimization approach to operating the system, employing a non-dominated sorting genetic algorithm (NSGA-II). The multi-objective optimization is performed to find the optimal trade-off between maximizing the coefficient of performance (COP) of the system and maximizing system self-sufficiency from a locally installed solar-PV system. Optimization is performed over 72 hours in the Fall, using Miami as a case study. The optimal time-resolved charging profiles and HP output water temperature as decision variables are extracted from the Pareto frontier. The results of the Pareto front show that when the system’s self-sufficiency goes up from 71% to 81%, the COP decreases slightly from 4.55 to 4.36. This means a 14% increase in self-sufficiency leads to a small 4.3% decrease in COP.
Renewable Energy Option in the Sudan
Authors: Ibrahim Nagwa 
Published: September 2024

Abstract
Energy is now considered the primary driving force behind the growth of key economic sectors, particularly energy-intensive industrial sectors. Sudan’s abundant sunshine, averaging 3,000 hours annually, makes it an ideal location for solar power generation. Sudan has a unique chance to diversify its energy mix and decrease its reliance on fossil fuels due to these resources. Sudan has a significant potential for renewable energy, particularly solar and wind power. 65% of its population lives in rural areas. 22.22% of the rural population have electricity access. The residential sector consumes 40% of electricity, of which 70% is generated by hydropower, with five major dams in Sudan contributing significantly to this output. Following the 2011 separation of South Sudan from Sudan, the country faced challenges in providing sustainable energy to meet the growing needs of the domestic, industrial, and agricultural sectors. Sudan’s electricity sector is facing a crisis, with 60% of the population without power and millions experiencing continuous power cuts due to limited available capacity. The research explores Sudan’s renewable energy policies and potential for sustainable energy supply, highlighting the country’s potential for maximizing renewable energy production through solar and wind resources. This study aims to provide a realistic assessment of the feasibility of renewable energy development in Sudan.in this research to find out if renewable energy option in the Sudan, I use a questionnaire Includes 458 samples drawn from various societal sectors 88.8% of the sample agreed when asked if it is essential to produce electricity from renewable energy sources rather than petroleum derivatives.
Wind and Solar Renewable Energy System Estimation With Batteries Using the Monte Carlo Sampling Approach
Authors: Manoharan Yogesh , Headley Alexander John 
Published: September 2024

Abstract
The factors involved in the declining costs of electricity produced from renewable energy are government regulations, policy obligations, and incentives and the goal to reduce the carbon footprint in the energy sector. Therefore, installing renewables can be considered an advantage in the energy industry, but determining the proper sizes for large-scale renewable energy can be challenging due to their inherent intermittency and generation uncertainty in the face of climate change. These concerns can be mitigated by installing energy storage systems (ESSs) of a suitable size. Therefore, to avoid oversizing of renewable plus ESSs an optimization program is needed. To make the optimization program estimations reliable the impact of the uncertainties associated with wind speed and solar irradiance must assessed, studied, and integrated into the algorithm. These uncertainties influence energy generation and the system size estimation. Here we present the Monte Carlo method to quantify the effect of uncertainty on system sizing. The proposed algorithm is shown through a case study of a utility near Oklahoma City, which has significant renewable energy potential. Using the probability distributions from the 15-year data from atmospheric radiometric measurements (ARM) of solar and wind in 15-minute time intervals 1000 possible scenarios of year to year are generated by the Monte Carlo sampling method. These scenarios are used in the optimization program for least-cost system sizing for 10 years.
This proposed optimization framework is applicable to estimate the system sizes for residential and utilities that are interested in minimizing the electricity cost with renewable energy and BESS.
Experimental Study of a Lab Scale Carbonator for CO2 Capture Purpose
Authors: Lorreyte Clarisse , Parmaksiz Hazal , Lachmann Bruno , Tescari Stefania , de Oliveira Lamark , D’Arc de Fátima Palhares Dayana , Moumin Gkiokchan , Rincon Duarte Juan Pablo , Fend Thomas , Roeb Martin , Sattler Christian 
Published: September 2024

Abstract
Carbon Capture and Utilization (CCU) and Calcium Looping (CaL) have emerged as promising technologies for reducing CO2 emissions, particularly in industries like cement production. The synergy of these processes relies on a reversible chemical reaction involving calcium oxide (CaO), carbon dioxide (CO2), and calcium carbonate (CaCO3). Two reactors, a calciner and a carbonator, facilitate this process, releasing and capturing CO2 from CaCO3 and CaO particles, respectively.
This study introduces a lab-scale carbonator designed for CO2 capture within the context of CCU and CaL technologies. The innovative design details are elaborated upon in this paper. Experimental tests, varying parameters such as CO2 concentration (10–30 vol%) and the mass flow of CaO particles, were conducted to assess carbonation efficiency. CO2 capture efficiencies between 64 and 82% were measured.
While these results are preliminary, indicating the potential of the novel reactor concept, an ongoing study further explores and refines these findings. In addition to unveiling the design intricacies, this study emphasizes the feasibility of integrating the novel reactor concept into a CaL cycle, showcasing its adaptability to a broader size distribution of particles.
Techno-Economic Modeling of CO2 Hydrate Slurry Formation for Carbon Sequestration
Authors: Bhati Awan , Bahadur Vaibhav 
Published: September 2024

Abstract
Significant carbon sequestration capacity (up to 10 Gigatons/yr) will be needed by 2050 to limit the Earth’s temperature rise to &lt; 1.5 °C. The current worldwide capacity is ∼40MT/yr, which highlights the need for the development of new and scalable sequestration approaches. One novel technology for long-term sequestration of CO2 is the deposition of CO2 hydrates (ice-like solids made with water and CO2) on the seabed (under marine sediments or with artificial sealing). This involves rapid formation of CO2 hydrate slurries in a bubble column reactor (BCR) by bubbling CO2 gas at high flow rates in a BCR with the unreacted CO2 being recirculated; this approach is being pioneered by the present research group. This study utilizes recent experimental results on ultra-fast hydrate formation to conduct a techno-economic analysis of the hydrate slurry-making process. All analysis is conducted for a 1 Megaton/yr sequestration project, which is expected to run for 30 years. Our analysis shows that the total cost of hydrate slurry production is $16.2/ton. Such projects would require an initial investment of $74M, and the energy requirement will be 641 MWh/day. Contributions of each part of the process to the total cost are identified. Our results show that gas recirculation in a BCR contributes minimally (0.04%) to the overall energy requirement. Furthermore, the cost of BCR is only 0.3% of the total investment cost. This suggests that a low conversion of gas into hydrates in each pass of the BCR is not detrimental from a techno-economic standpoint. The findings of this study set the stage for more detailed analysis of hydrates-based sequestration, which is essential to add this technology to the existing bank of established carbon sequestration solutions.
Leak Tightness of Valves for the Hydrogen Industry
Authors: Fischer Felix , Schmitz Katharina 
Published: September 2024

Abstract
As part of the energy transition from a fossil-based system to a system based on climate-friendly energy sources, a new energy carrier for mobile applications is needed, replacing conventional fossil-based fuels. With its high energy density, compressed hydrogen is a promising candidate for such an energy carrier. In recent times, there has been an increased industry and research interest in hydrogen technology.
Due to the inherent difficulty as well as dangers of working with inflammable and low-viscosity hydrogen, there is a need for the development of specialized components for the transport and storage of pressurized hydrogen. Due to hydrogen-specific damage on polymeric seals, metallic sealing is the preferable method to hinder the flow of hydrogen through closed valves or unwanted leakage between connecting components such as screw fittings. Until the writing of this paper, the sealing mechanism of seals formed by metals for gases has not been well understood. Existing theories describing liquid leakage through metallic seals cannot be applied without modification to describe gaseous systems.
This article was written to describe how gas leakage can be modeled. In it, a procedure based on the contact mechanics model developed by Persson is presented. It discusses possible solutions to modify the existing flow models utilizing this theory and presents possible adaptations of this theory to sealing relevant to the hydrogen industry.
To support the presented methods, the flow models developed for this paper were compared to measurements on a sample system. The leakage was described using a valve consisting of a steel ball lying on a steel seat, sealing off pressurized air. This experimental validation highlights the successes and shortcomings of the used methods and can be used for the development of a refined model in future works.
Fluid Flow and Combustion Characteristics of Triangular Rotary Engines With Port Configuration Designs
Authors: Hsieh Chiu-Fan , Wang Sheng-Yao 
Published: September 2024

Abstract
The utilization of hydrogen energy in Triangular Rotary Engines (TRE) presents a promising opportunity for the development of environmentally friendly engines and advanced power generation equipment. However, the primary challenge lies in the design and performance prediction of TRE. This study employs Computational Fluid Dynamics (CFD) to investigate the fluid flow characteristics of a side-ported triangular rotating engine. The impact of varying the number of ports, port positions, and port designs on rotary engine performance is thoroughly examined and compared with the efficiency of a previously modeled triangular rotary peripheral port engine. The analysis considers two types of side ports, single and double, and evaluates three different port designs based on the angle of the port slopes. Additionally, two port position configurations for intake/exhaust openings are explored to comprehensively study port opening geometry. The fluid simulation results reveal differences in mass flow rate, average velocity, and average pressure between peripheral and side-ported TRE, as well as between single and double side-ported configurations. Furthermore, predictions based on fluid moment and vortex number statistics suggest that the side-ported TRE offers lower fluid flow resistance, lower risk of leakage, and higher combustion efficiency compared to the peripheral-ported TRE. Moreover, vortex number statistics in combustion and non-combustion zones are introduced to predict leakage risk and combustion performance, respectively. Predictions indicate that the side-ported TRE has a relatively low risk of leakage and higher combustion efficiency compared to the peripheral-ported TRE. These predictions are further validated through combustion simulations using hydrogen fuel.
Sensor Anomalies Characterization and Detection via Machine Learning Methods for Nuclear Power Plants
Authors: Pohlmann Liam M. , Bhowmik Palash K. , Wang Congjian , Sabharwall Piyush 
Published: September 2024

Abstract
Next-generation nuclear reactors pose challenges for effective system monitoring and data management, necessitating the differentiation of anomalous sensor data from noise. While digital twin models hold promise, fundamental analyses using simplified models are needed. This study proposes a foundational approach utilizing simulated data from PCTRAN-generated datasets to emulate reference pressurized-water reactor (PWR) conditions and introduces typical sensor performance and anomalies. The method employs data partitioning, linear regression for preprocessing, and a K-means clustering algorithm for anomaly detection, achieving over 95% precision in identifying anomalies. A parametric study using Monte Carlo Sampling on the anomaly detection algorithm’s input values reveals critical factors such as window size’s impact on accuracy and computational time. Utilizing the Risk Analysis Virtual Environment (RAVEN) tools, a Pareto optimal frontier is determined to balance accuracy and execution time. Sensitivity and uncertainty analysis highlight window size as a critical factor. While further refinement is necessary for practical application, these techniques show promise for enhancing nuclear system monitoring and data management.
System and Component Development of Particle-Based Pumped Thermal Energy Storage
Authors: Ma Zhiwen , McTigue Josh , Gifford Jeffrey , Hirschey Jason , Jeong Shin Young , Shah Munjal Purnkant , Martinek Janna 
Published: September 2024

Abstract
Reliable power supply from variable renewable resources requires energy storage at various scales to overcome resource intermittency. Long-duration energy storage (LDES, 10–100 hours) can improve dispatchability and grid reliability with increasing levels of renewable power supply. Thermal energy storage (TES) has siting flexibility and the ability to store a large capacity of energy, and thus has the potential to meet the LDES need and provide charging and discharging durations beyond the economic capacity of conventional batteries. TES technology has evolved from concentrating solar thermal power (CSP) generation and is recognized as an economic large-scale energy storage method. A standalone electric-thermal energy storage system supporting renewable integration of wind and solar power without CSP solar field can firm renewable generation and boost overall grid resilience and security.
An Ultra-Short-Term Power Prediction Method for Wind Farms in Northwest China Based on Federated Learning
Authors: Rong Wu , Lin Xiaojie , Cong Feiyun , Zhong Wei 
Published: September 2024

Abstract
Wind power is highly favored due to its minimal environmental impact, great economic benefits, and mature technology. China is developing large-scale wind farms in its northwestern region as a significant part of its Carbon Neutrality strategy. Accurate power prediction for large-scale wind farms is one of the keys to the safe operation of China’s future power grid. Farms of diverse scale and a variety of start-up time have to overcome communication barriers. These factors collectively present challenges to achieving precise power predictions for wind farms. To address this, this paper presents a universal ultra-short-term prediction method for multiple wind farms using an encoder-decoder network based on federated learning. The prediction power sequences are separated into trend and fluctuation components to be predicted separately. While considering the data privacy of different farm owners, this approach overcomes the low accuracy and lack of model universality in power predictions for various wind farms. In experiments, this method achieves an average prediction accuracy of 87.41% in the twelve federated farms, about 5.9% improvement compared to conventional wind power prediction methods. This approach also addresses the challenges of small-sample predictions in newly established wind farms, reaching an 86% average accuracy in five non-federated farms.