bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Simulation of the Effect of Magnetic Fields on Soot Formation in Laminar Non-Premixed Flames
Authors: Chukwuemeka Edison E. , Schoegl Ingmar M. 
Published: August 2021

Abstract
Characteristics of non-premixed flames such as flame height and lift-off height are affected by the presence of magnetic fields due to the paramagnetic properties of some combustion species. However, it is unknown whether magnetic fields can be used to reduce the emission of pollutants in non-premixed flames.
In general, pollutant emissions are reduced in combustion systems if the mixing of combustion species is enhanced during the process. Since paramagnetic combustion species such as O2, O, OH, HO2, etc have a preferential motion direction in the presence of magnetic fields, there is a potential to harness this effect of mixing by imposing a magnetic field on the flame.
This study seeks to provide some insights on the effect of magnetic field on pollutants generated in a laminar non-premixed flame numerically. The non-premixed flame is simulated using a detailed chemical mechanism for propane-air combustion and a modified Moss-Brookes soot model. To simulate the effect of magnetism on the paramagnetic chemical species, the species paramagnetic susceptibility is computed using the Curie relation. The non-premixed flame is placed at three different locations within the magnetic field.
The computation predicted that the amount of average pollutants reduction is dependent on the location of the flames within the magnetic fields with respect to magnetic gradients. The mass weighted average of the soot volume fraction over the computational domain decreased when the non-premixed flame is located at certain locations within the magnetic field of the solenoid with respect to the absence of the magnetic fields, but increases in other locations.
A Digital Twin Environment Designed for the Implementation of Real Time Monitoring Tool
Authors: Pezzini Paolo , Bonilla Harry , Johnson Grant , Reinhart Zachary , Bryden Kenneth Mark 
Published: August 2021

Abstract
Real time models and digital twin environments represent a new frontier that allow the development of supplemental data analytics of measurable and unmeasurable parameters for a variety of power plant configurations. Performance prediction, monitoring of degradation effects, and a faster recognition of anomalous events during power plant load following operations and/or due to cyber-attacks can be easily detected with the support of digital twin environments. In the research work described in this article, a digital twin environment was developed to capture the dynamics of a micro compressor-turbine system modified for hybrid configuration at the Department of Energy’s National Energy Technology Laboratory (NETL). The innovative approach for the development of the digital twin environment was based on creating a compressor-turbine physics-based model using a stateless methodology generally utilized for microservices architectures. The advantage of using this approach was represented by modeling individual or a group of power plant components on distributed computational resources such as clouds in a lightweight and interchangeable manner.
Supplemental data analytics were performed using an online system identification tool developed in previous work and applied to an unmeasurable parameter only available in the digital twin system. This work demonstrated the ability to train a recursive algorithm to predict a supplemental parameter for faster anomaly detection or for replacing the physics-based model during design or monitoring of operational systems. The thermodynamic compressor-turbine net power was the unmeasurable parameter only available in the digital twin model, which was predicted with the online system identification tool. Results showed that the online system identification algorithm predicted the dynamic response of the thermodynamic net power based on a set of experimental data points at nominal operating conditions with an absolute mean percentage error of ∼0.644%.
Simulation Research on Explosives Detection System Based on D-D Sealed Neutron Generator
Authors: Gao Ya-Dong , He De-Dong , Gong Ke , Shi Guang-Yu , Chen Si-Yuan , Zhu Chen-Xi , Jing Shi-Wei 
Published: August 2021

Abstract
A prompt gamma neutron activation analysis (PGNAA) system based on a deuterium-deuterium (D-D) sealed neutron generator was designed using the MOCA code for explosive detection. The system is mainly composed of four parts: D-D sealed neutron generator, moderator, shielding, and Lutetium Yttrium OxyorthoSilicate (LYSO) scintillation detectors. Polyethylene (PE) was selected as the moderator and the optimal thickness was 7cm. Lead, PE, and boron-containing polyethylene were used as shielding materials. In the optimized model, the LYSO detector is used to measure eighteen materials, such as wood, melamine, glucose, and nylon, and so on. Firstly, the nitrogen characteristic peak of 10.8 MeV was analyzed to determine whether the material contained nitrogen. Then, the ratio of characteristic peak counts of C/O and O/N were calculated to distinguish explosives from nitrogen containing materials. Finally, dinitrobenzene, nitroglycerin, TNT, and ammonium nitrate can be separated from nitrogenous substances by a discriminant algorithm. The final device can be used to detect the chemical composition of the threat substances, and the maximum dose rate of the system meets the limits of international protection standards.
The Sandia National Laboratories Natural Circulation Cooler
Authors: Middleton Bobby D. , Brady Patrick V. , Lawles Serafina 
Published: August 2021

Abstract
Sandia National Laboratories (SNL) is developing a cooling technology concept — the Sandia National Laboratories Natural Circulation Cooler (SNLNCC) — that has potential to greatly improve the economic viability of hybrid cooling for power plants. The SNLNCC is a patented technology that holds promise for improved dry heat rejection capabilities when compared to currently available technologies. The cooler itself is a dry heat rejection device, but is conceptualized here as a heat exchanger used in conjunction with a wet cooling tower, creating a hybrid cooling system for a thermoelectric power plant.
The SNLNCC seeks to improve on currently available technologies by replacing the two-phase refrigerant currently used with either a supercritical fluid — such as supercritical CO2 (sCO2) — or a zeotropic mixture of refrigerants. In both cases, the heat being rejected by the water to the SNLNCC would be transferred over a range of temperatures, instead of at a single temperature as it is in a thermosyphon. This has the potential to improve the economics of dry heat rejection performance in three ways: decreasing the minimum temperature to which the water can be cooled, increasing the temperature to which air can be heated, and increasing the fraction of the year during which dry cooling is economically viable. This paper describes the experimental basis and the current state of the SNLNCC.
Stator Leakage Monitoring System in Water-Cooled Generators: Problems and Solutions
Authors: Bauer Thomas , Svoboda Matthias 
Published: August 2021

Abstract
An electrical generator is one of the most efficient large-scale machines. It converts mechanical to electrical energy with an efficiency coefficient of approximately 99%. The remaining 1% can mainly be contributed to heat losses. Direct cooling is only necessary for larger turbogenerators with more than 250 MVA where the cooling media is introduced via hollow conductors within the stator bars. Turbogenerators of approximately up to 700 MVA nowadays use exclusively hydrogen (H2) gas as a cooling media. Even larger turbogenerators have to introduce direct water cooling.
The water chemistry of the stator cooling water is typically of neutral pH and has a conductivity of less than 0.1 μS/cm. Two zones of the oxygen (O2) concentration have been established through the last 50 years, one at low dissolved O2 concentration with less than 20 ppb, the other with high concentrations of more than 2 ppm. The latter has to continuously inject CO2 free air to ensure to always keep the oxygen concentration above 2 ppm.
The first part of this publication shows several incidents with the air injection system in different Nuclear Power Plants in the US, resulting in unfavorable stator cooling water chemistry. This led to a reduced cooling efficiency, resulting in several chemical online cleanings being necessary.
The second part of this work presents a technical solution to overcome the issues associated with the reduced stator cooling. It continuously injects and monitors the air injected into the system. Additionally, it also measures the hydrogen leakage rate.
The Introduction and Analysis of the World’s First High-Temperature Retrofit Project on a Subcritical Coal-Fired Power Unit
Authors: Feng Weizhong , Li Li 
Published: August 2021

Abstract
Global warming concerns have pushed coal-fired power plants to develop innovative solutions which reduce CO2 emissions by increasing efficiency. While new ultra-supercritical units are built with extremely high efficiency, with Pingshan II approaching 50% LHV[1], subcritical units with much lower efficiency are a major source of installed capacity.
The typical annual average net efficiency of subcritical units in China is about 37% LHV, and some are lower than 35% LHV. Since the total subcritical capacity in China is about 350GW and accounts for over one third of its total coal-fired power capacity, shutting all subcritical units down is not practical.
Finding existing coal-fired plants a cost-effective solution which successfully combines advanced flexibility with high efficiency and low emissions, all while extending service lives, has challenged energy engineers worldwide. However, the (now proven) benefits a high temperature upgrade offers, compared to new construction options, made this an achievement worth pursuing.
After many years of substantial incremental improvements to best-in-class technology, this first-of-its-kind subcritical high temperature retrofit successfully proves that a technically and economically feasible solution exists. It increases the main and reheat steam temperatures from 538°C (1000°F) to 600°C (1112°F), and the plant cycle and turbine internal efficiencies are greatly improved.
This upgrade’s greatest efficiency gains occur at low loads, which is important as fossil plants respond to renewable energy’s increased grid contributions. These are combined with best-in-class flexibility, energy-savings, and technological advances, i.e., flue gas heat recovery technology and generalized regeneration technologies [4].
This project, the world’s first high-temperature subcritical coal-fired power plant retrofit, was initiated in April 2017 and finished in August 2019. Performance reports created by Siemens and GE record unit net efficiency at rated conditions improved from 38.6% to 43.5% LHV. The boiler’s lowest stable combustion load with operational SCR, without oil-firing support, was reduced from 55% to 19%. Substitution or upgrading of high-temperature components extended the lifetime of the unit by more than 30 years. At a third of the cost of new construction, this project set a high-water-mark for retrofitting subcritical units, and meets or supports the requisite attributes for Coal FIRST, Coal Plant of the Future, proposed by the United States Department of Energy (DOE) in 2019 [2].
Modeling and Validation of Hydro Cascade Operation Considering Price Uncertainty
Authors: Libsig Maxime , Raycheva Elena , Garrison Jared B. , Hug Gabriela 
Published: August 2021

Abstract
Most studies involving the use of hydropower in an electric power system tend to consider the point of view of the system operator even though under liberalized markets in Europe, the operation of hydro units is set by the owner to maximize their profits. Such studies also often neglect uncertainties related to hydropower operation and instead assume perfect knowledge of the system conditions over the simulation horizon. This paper presents a methodology to overcome the aforementioned limitations. We optimize the operational choices of a hydropower cascade owner with multiple linked hydro assets and the ability to participate in several energy and reserve markets while also accounting for the impact of market price uncertainties on the owner’s operating decisions. The versatile optimization model created includes a detailed representation of any selected hydro cascade’s topology, constraints to reflect the machinery characteristics, and a rolling horizon approach to account for the price uncertainties in the daily operating schedule. The model is first validated using historical data for a hydro cascade in Switzerland and a perfect-knowledge approach. Next, price uncertainty is added to improve the historical simulation results and find a trade-off between accuracy and computational time.
Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks
Authors: Ziaei Dorsa , Goudarzi Navid 
Published: August 2021

Abstract
Onshore/offshore wind turbines play a vital role in addressing the increasing worldwide energy demand. Enhancing the wind power harnessing capability of turbines and extending the life expectancy of their components support further reductions in the final cost of wind energy. Data-driven techniques can complement existing physics-based approaches for complex problems such as wind farm wake modeling. In this paper, a deep learning model is developed to predict the local short-term wind characteristics. A data pre-processing pipeline that includes data cleaning and normalizing steps is developed to generate the training dataset. Time-series forecasting models based on long-short-term-memory (LSTM) and convLSTM are developed and trained for local short-term wind forecasting. The model is validated through experiments on three-year data from the National Renewable Energy Laboratory (NREL) database. The conducted experiments showed favorable performance based on root mean square error (RMSE) and R2 test scores. The R2 values for predicting 1-minute, 30-minute, and 1 hour, wind characteristics for both LSTM and convLSTM were above 0.92. The results are in agreement with the literature. They also demonstrate the effectiveness of the developed models for short-term wind forecasting compared to similar ones.
A CapsNet-Based Fault Diagnosis Method for a Digital Twin of a Wind Turbine Gearbox
Authors: Zhao Hao , Hu Weifei , Liu Zhenyu , Tan Jianrong 
Published: August 2021

Abstract
Accurate fault diagnosis of complex energy systems, such as wind turbines, is essential to avoid catastrophic accidents and ensure a stable power source. However, accurate fault diagnoses under dynamic operating conditions and various failure mechanisms are major challenges for wind turbines nowadays. Here we present a CapsNet-based deep learning scheme for data-driven fault diagnosis used in a digital twin of a wind turbine gearbox. The CapsNet model can extract the multi-dimensional features and rich spatial information from the gearbox monitoring data by an artificial neural network named the CapsNet. Through the dynamic routing algorithm between capsules, the network structure and parameters of the CapsNet model can be adjusted effectively to realize an accurate and robust classification of the operational conditions of a wind turbine gearbox, including front box stuck (single fault) and high-speed shaft bearing damage & planetary gear damage (coupling faults). Two gearbox datasets are used to verify the performance of the CapsNet model. The experimental results show that the accuracy of this proposed method is up to 98%, which proves the accuracy of CapsNet model in the case study when this model performed three-state classification (health, stuck, and coupled damage). Compared with state-of-the-art fault diagnosis methods reported in the literature, the CapsNet model has a competitive advantage, especially in the ability to diagnose coupling faults, high-speed shaft bearing damage & planetary gear damage in our case study. CapsNet has at least 2.4 percentage points higher than any other measure in our experiment. In addition, the proposed method can automatically extract features from the original monitoring data, and do not rely on expert experience or signal processing related knowledge, which provides a new avenue for constructing an accurate and efficient digital twin of wind turbine gearboxes.
History of geological investigations of Mount Diablo, Contra Costa County, California
Authors: Bartow Gregory W. 
Published: August 2021

ABSTRACT
Over the past 150 years, Mount Diablo has served as a window into the evolving understanding of California geology. In the 1800s, geologists mapped this easily accessible peak located less than 100 km (62 miles) from the rapidly growing city of San Francisco and the geology departments at the University of California at Berkeley and Stanford University. Later, the mountain served as a focal point for investigating San Francisco Bay area tectonics. The structural interpretation of the up-thrusting mechanisms has evolved from a simple compressional system involving a few local faults to a more complex multifault and multiphase mountain-building theory. The stratigraphic interpretation and understanding have been advanced from a general description of the lithologies and fossils to a detailed description using sequence stratigraphy to define paleogeographic settings and depositional regimes.
Effectiveness of technology-based interventions in detection, prevention, monitoring and treatment of patients at risk or diagnosed with mild cognitive impairment: protocol for a systematic review
Authors: Martínez-Soldevila Jordi , Pastells-Peiró Roland , Climent-Sanz Carolina , Piñol-Ripoll Gerard , Rocaspana-García Mariona , Gea-Sánchez Montserrat 
Published: August 2021

IntroductionThe gradual changes over the decades in the longevity and ageing of European society as a whole can be directly related to the prolonged decline in the birth rate and increase in the life expectancy. According to the WHO, there is an increased risk of dementia or other cognitive disorders as the population ages, which have a major impact on public health. Mild cognitive impairment (MCI) is described as a greater than expected cognitive decline for an individual’s age and level of education, but that does not significantly interfere with activities of daily living. Patients with MCI exhibit a higher risk of dementia compared with others in the same age group, but without a cognitive decline, have impaired walking and a 50% greater risk of falling.The urban lifestyle and advent of smartphones, mobility and immediate access to all information via the internet, including health information, has led to a totally disruptive change in most general aspects.This systematic review protocol is aimed at evaluating the effectiveness of technology-based interventions in the detection, prevention, monitoring and treatment of patients at risk or diagnosed with MCI.Methods and analysisThis review protocol follows the recommendations of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols reporting guidelines. The search will be performed on MEDLINE (PubMed), CENTRAL, CINAHL Plus, ISI Web of Science and Scopus databases from 2010 to 2020. Studies of interventions either randomised clinical trials or pre–post non-randomised quasi-experimental designs, published in English and Spanish will be included. Articles that provide relevant information on the use of technology and its effectiveness in interventions that assess improvements in early detection, prevention, follow-up and treatment of the patients at risk or diagnosed with MCI will be included.Ethics and disseminationEthics committee approval not required. The results will be disseminated in publications and congresses.
Do we need to change catheter-related bloodstream infection surveillance in the Netherlands? A qualitative study among infection prevention professionals
Authors: Verberk Janneke DM , van der Kooi Tjallie II , Derde Lennie PG , Bonten Marc JM , de Greeff Sabine C , van Mourik Maaike SM 
Published: August 2021

ObjectivesCatheter-related bloodstream infections (CRBSI) are a common healthcare-associated infection and therefore targeted by surveillance programmes in many countries. Concerns, however, have been voiced regarding the reliability and construct validity of CRBSI surveillance and the connection with the current diagnostic procedures. The aim of this study was to explore the experiences of infection control practitioners (ICPs) and medical professionals with the current CRBSI surveillance in the Netherlands and their suggestions for improvement.DesignQualitative study using focus group discussions (FGDs) with ICPs and medical professionals separately, followed by semistructured interviews to investigate whether the points raised in the FGDs were recognised and confirmed by the interviewees. Analyses were performed using thematic analyses.SettingBasic, teaching and academic hospitals in the Netherlands.Participants24 ICPs and 9 medical professionals.ResultsMain themes derived from experiences with current surveillance were (1) ICPs’ doubt regarding the yield of surveillance given the low incidence of CRBSI, the high workload and IT problems; (2) the experienced lack of leadership and responsibility for recording information needed for surveillance and (3) difficulties with applying and interpreting the CRBSI definition. Suggestions were made to simplify the surveillance protocol, expand the follow-up and surveillance to homecare settings, simplify the definition and customise it for specific patient groups. Participants reported hoping for and counting on automatisation solutions to support future surveillance.ConclusionsThis study reveals several problems with the feasibility and acceptance of the current CRBSI surveillance and proposes several suggestions for improvement. This provides valuable input for future surveillance activities, thereby taking into account automation possibilities.