bluuebunny/crossref_metadata_2025 huggingface dataset:
Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency
Authors: Gribbestad Magnus , Hassan Muhammad Umair , Hameed Ibrahim A. , Sundli Kelvin
Published: January 2021

Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.
Connection between Inverse Engineering and Optimal Control in Shortcuts to Adiabaticity
Authors: Zhang Qi , Chen Xi , Guéry-Odelin David
Published: January 2021

We consider fast high-fidelity quantum control by using a shortcut to adiabaticity (STA) technique and optimal control theory (OCT). Three specific examples, including expansion of cold atoms from the harmonic trap, atomic transport by moving harmonic trap, and spin dynamics in the presence of dissipation, are explicitly detailed. Using OCT as a qualitative guide, we demonstrate how STA protocols designed from inverse engineering method can approach with very high precision optimal solutions built about physical constraints, by a proper choice of the interpolation function and with a very reduced number of adjustable parameters.
Transport Efficiency of Continuous-Time Quantum Walks on Graphs
Authors: Razzoli Luca , Paris Matteo G. A. , Bordone Paolo
Published: January 2021

Continuous-time quantum walk describes the propagation of a quantum particle (or an excitation) evolving continuously in time on a graph. As such, it provides a natural framework for modeling transport processes, e.g., in light-harvesting systems. In particular, the transport properties strongly depend on the initial state and specific features of the graph under investigation. In this paper, we address the role of graph topology, and investigate the transport properties of graphs with different regularity, symmetry, and connectivity. We neglect disorder and decoherence, and assume a single trap vertex that is accountable for the loss processes. In particular, for each graph, we analytically determine the subspace of states having maximum transport efficiency. Our results provide a set of benchmarks for environment-assisted quantum transport, and suggest that connectivity is a poor indicator for transport efficiency. Indeed, we observe some specific correlations between transport efficiency and connectivity for certain graphs, but, in general, they are uncorrelated.
Robust Multiple Regression
Authors: Scott David , Wang Zhipeng
Published: January 2021

As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors under some assumptions. As there are many robust algorithms available, running several and looking for interesting differences is a sensible strategy for understanding the nature of the outliers.
Entropy Generation Analysis of Hybrid Nanomaterial through Porous Space with Variable Characteristics
Authors: Sadiq Muhammad Adil , Haider Farwa , Hayat Tasawar
Published: January 2021

Salient features of hybrid nanofluid (MoS2-SiO2/water) for Darcy–Forchheimer–Brinkman porous space with variable characteristics is examined. Heat transfer analysis subject to viscous dissipation, nonlinear thermal radiation, and heat generation/absorption is carried out. Disturbance inflow is created by an exponentially stretching curved sheet. Relevant equations are simplified by employing boundary layer theory. Adequate transformations lead to a set of dimensionless equations. Velocity, temperature, and entropy generation rate are analyzed graphically. Comparative results are obtained for hybrid (MoS2-SiO2/water) and nanofluid (MoS2-water and SiO2-water). Physical quantities are analyzed through numerical data.
Projection of E-Learning in Higher Education: A Study of Its Scientific Production in Web of Science
Authors: López-Belmonte Jesús , Segura-Robles Adrián , Moreno-Guerrero Antonio-José , Parra-González María-Elena
Published: January 2021

E-learning is conceived as a purely virtual training approach. Different learning styles have been proliferated in recent years, especially now, due to the impact of COVID-19 in the educational field. The aim of this study is to discover the evolution of e-learning in higher education (ELHI) in scientific literature indexed on the Web of Science. Co-word analysis and bibliometric analysis was performed. A total matrix of 1261 documents was analyzed through SciMAT software. The results revealed that studies on ELHI are written in English and presented by conference papers. The main source of publication for the conferences is EDULEARN proceedings, while the journal source is Procedia-Social and Behavioral Sciences. Spain is the country with the highest volume of production. It is concluded that research on ELHI use does not have an established line of research, due to its recent creation and the lack of related research. The bibliometric analysis specifies that the research is oriented towards knowing the level of acceptance and application of the pedagogical method in the teaching and learning processes.
Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research
Authors: Bazionis Ioannis K. , Georgilakis Pavlos S.
Published: January 2021

The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy. However, the wind’s stochastic and unpredictable behavior has created several challenges to the operation and stability of energy systems. Forecasting models have been developed and excessively used in recent decades in order to deal with these challenges. Deterministic forecasting models have been the main focus of researchers and are still being developed in order to improve their accuracy. Furthermore, in recent years, in order to observe and study the uncertainty of forecasts, probabilistic forecasting models have been developed in order to give a wider view of the possible prediction outcomes. Advanced probabilistic and deterministic forecasting models could be used in order to facilitate the energy systems operation and energy markets management. This paper introduces an overview of state-of-the-art wind power deterministic and probabilistic models, developing a comparative evaluation between the different models reviewed, identifying their advantages and disadvantages, classifying and analyzing current and future research directions in this area.
Distributed Event-Triggered Voltage Control with Limited Re-Active Power in Low Voltage Distribution Networks
Authors: Luo Yanyu , Chen Minyou , Kang Wenfa , Sun Xiaoluo
Published: January 2021

A high proportion of photovoltaic (PV) connections to a low-voltage distribution network (LVDN) causes serious voltage problems. In order to ensure voltage stability for renewable energy networks, we propose a distributed reactive voltage control strategy that is event-triggered. The voltage information of the PV nodes is transmitted to the upper layer of the communication network, where the agent calculates the output set value of the PV inverter. The underlying control strategy is based on the voltage sensitivity matrix, and the upper-level control strategy is based on an event-triggered consensus protocol. This strategy can accommodate the requirements for multi-time modeling and control. We verified the convergence of the event-triggered control algorithm using numerical analysis and proved the reduction of the communication times. We conducted case studies and simulation experiments to verify the effectiveness of our proposed voltage control strategies.
Translating Sentimental Statements Using Deep Learning Techniques
Authors: Huang Yin-Fu , Li Yi-Hao
Published: January 2021

Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.
An Improved Stopband Dual-Band Filter Using Quad-Mode Stub-Loaded Resonators
Authors: Weng Min-Hang , Huang Chun-Yueh , Dai Shi-Wei , Yang Ru-Yuan
Published: January 2021

In this paper, we present a dual-band microstrip bandpass filter (BPF) with an improved stopband, which was constructed by a quad-mode stub-loaded resonator (SLR). Since the SLR is able to produce multiresonance within a single unit, the area saving is significant. The proposed quad-mode SLR was implemented by two stubs allocated at symmetry places, thus the even-/odd-mode can be applied to analysis the resonance. Moreover, to shift the spurious passband, the step impedance structure was applied to shift the harmonic resonance to the higher frequency. Design procedure for high performance dual-band BPF is proposed, and filter examples were designed for wireless local area network (WLAN) of 2.4/5.2 GHz. The measured insertion losses, return losses and fractional bandwidths (FBW) are 1.43 dB, 10 dB, and 14.8% at 2.4 GHz and 1.34 dB, 10 dB, and 12.9% at 5.2 GHz. Moreover, by applying two quarter-wavelength stubs on the input/output ports, the passband selectivity with an isolation of 40 dB can be achieved. The simulation and measurement have a close match, verifying the design concept.
Compact Continuous Time Common-Mode Feedback Circuit for Low-Power, Area-Constrained Neural Recording Amplifiers
Authors: Kwak Joon Young , Park Sung-Yun
Published: January 2021

A continuous-time common-mode feedback (CMFB) circuit for low-power, area-constrained neural recording amplifiers is proposed. The proposed CMFB circuit is compact; it can be realized by simply replacing passive components with transistors in a low-noise folded cascode operational transconductance amplifier (FC-OTA) that is one of the most widely adopted OTAs for neural recording amplifiers. The proposed CMFB also consumes no additional power, i.e., no separate CMFB amplifier is required, thus, it fits well to low-power, area-constrained multichannel neural recording amplifiers. The proposed CMFB is analyzed in the implementation of a fully differential AC-coupled neural recording amplifier and compared with that of an identical neural recording amplifier using a conventional differential difference amplifier-based CMFB in 0.18 μm CMOS technology post-layout simulations. The AC-coupled neural recording amplifier with the proposed CMFB occupies ~37% less area and consumes ~11% smaller power, providing 2.67× larger output common mode (CM) range without CM bandwidth sacrifice in the comparison.
Economic, Energy, and Environmental Analysis of PV with Battery Storage for Italian Households
Authors: Lazzeroni Paolo , Mariuzzo Ivan , Quercio Michele , Repetto Maurizio
Published: January 2021

The use of renewable energy sources is one way to decarbonize current energy consumption. In this context, photovoltaic (PV) technology plays a direct fundamental role since it can convert sun irradiance into electricity to be used for supplying electric loads for households. Despite the huge availability of the solar resource, the intermittence of PV production may reduce its exploitation. This problem can be solved by the introduction of storage systems, such as batteries, storing electricity when PV overproduction occurs and acting as a source when PV generation is absent. Consequently, increase in self-sufficiency and self-consumption can be expected in residential end users, paving the way for more sustainable energy systems. In this paper, an economic, energy, and environmental analysis of PV systems (without and with batteries) for the household is performed for the whole of Italy, by means of a Geographical Information Systems (GIS) approach. A model to simulate energy balance and to manage batteries is defined for households to assess the profitability of such systems under an Italian regulation framework. Concerning results, indicators are provided at a national scale using GIS tools to highlight areas where investments are more profitable, boosting the CO2 emission reduction.
Empirical Models Applied to Distributed Energy Resources—An Analysis in the Light of Regulatory Aspects
Authors: Deotti Lucas , Silva Júnior Ivo , Honório Leonardo , Marcato André
Published: January 2021

According to the International Electrotechnical Commission (IEC) 61853 standard, the power rating of photovoltaic (PV) modules must be done on a measurement matrix that broadly covers the ranges of operating conditions encountered in the field. These results are becoming more frequent in recent module datasheets. This paper investigates the effectiveness of applying existing empirical PV performance models while using a matrix with 18 operational records, similar to the 22 that were recommended by IEC 61853, as an alternative to the thousands of records that are conventionally used to determine their coefficients. A review of fifteen empirical models is presented and the procedures for determining their coefficients are discussed. In order to validate them, they were applied to data from fourteen PV modules, which remained installed outdoors for about one year, in three locations with distinct climate types. Although the uncertainties that were obtained with the proposed approach, as compared to the conventional one, are about 1% higher for xSi and CdTe modules, and somewhat higher for mSi and CIGS modules, the total uncertainties were only around 5%, a value that is quite adequate for evaluating module performance. Moreover, these uncertainties were from two to five times smaller than those that were obtained by the method that was recommended by IEC 61853 for this purpose.
Multi-Information Fusion-Based Hierarchical Power Generation-Side Protection System
Authors: Yin Xianggen , Wang Yikai , Qiao Jian , Xu Wen , Yin Xin , Jiang Lin , Xi Wei
Published: January 2021

With renewable power sources and new topology structures being widely introduced into the power system, the current local information-based power generation-side protection cannot fully guarantee the protection performance and the safety coordination with the power grid. This paper proposes an improved hierarchical protection system on the power generation-side. The proposed system takes advantage of the fusion of multi-information provided by the system, station and local layers. The system layer provides the information such as system voltage control and power regulation demand, so that the generation-side protection and control system can adapt to the system operation mode and power regulation demand. The station layer realizes the coordination of the protection principle and action strategy among the related units through information interaction with the power grid, the automatic control system and the local layer protection. The local layer introduces the condition monitoring information and more abundant protection information to enhance the protection performance and master the generation units’ safety condition. To illustrate the hierarchical protection system construction method and actual application mode, the multi-information fusion-based comprehensive local layer protection method and the multi-generators information fusion-based hierarchical protection method are taken as examples. Case analysis shows that the proposed methods can reflect more slight internal fault forms and can adaptively determine the protection action characteristic and tripping strategy according to the system operating conditions and other generators’ fault conditions, which effectively improve the protection sensitivity and coordination capability. To provide reference and inspiration for follow-up research, the hierarchical protection system construction mode, communication technology and research approaches of new protection methods are further pointed out.
Energy Evaluation of a PV-Based Test Facility for Assessing Future Self-Sufficient Buildings
Authors: Amato Angela , Bilardo Matteo , Fabrizio Enrico , Serra Valentina , Spertino Filippo
Published: January 2021

In recent years, investigations on advanced technological solutions aiming to achieve high-energy performance in buildings have been carried out by research centers and universities, in accordance with the reduction in buildings’ energy consumption required by European Union. However, even if the research and design of new technological solutions makes it possible to achieve the regulatory objectives, a building’s performance during operation deviates from simulations. To deepen this topic, interesting studies have focused on testing these solutions on full-scale facilities used for real-life activities. In this context, a test facility will be built in the university campus of Politecnico di Torino (Italy). The facility has been designed to be an all-electric nearly Zero Energy Building (nZEB), where heating and cooling demand will be fulfilled by an air-source heat pump and photovoltaic generators will meet the energy demand. In this paper, the facility energy performance is evaluated through a dynamic simulation model. To improve energy self-sufficiency, the integration of lithium-ion batteries in a HVAC system is investigated and their storage size is optimized. Moreover, the facility has been divided into three units equipped with independent electric systems with the aim of estimating the benefits of local energy sharing. The simulation results clarify that the facility meets the expected energy performance, and that it is consistent with a typical European nZEB. The results also demonstrate that the local use of photovoltaic energy can be enhanced thanks to batteries and local energy sharing, achieving a greater independence from the external electrical grid. Furthermore, the analysis of the impact of the local energy sharing makes the case study of particular interest, as it represents a simplified approach to the energy community concept. Thus, the results clarify the academic potential for this facility, in terms of both research and didactic purposes.
A Modular Multilevel Converter with an Advanced PWM Control Technique for Grid-Tied Photovoltaic System
Authors: Haq Safa , Biswas Shuvra Prokash , Hosain Md. Kamal , Rahman Md. Ashib , Islam Md. Rabiul , Jahan Sumaya
Published: January 2021

Due to global warming and shortage of fossil fuels, the grid-connected solar photovoltaic (PV) system has gained significant popularity all over the world. The modular multilevel cascaded (MMC) inverter is the natural choice for step-up transformer and line filter less direct medium voltage grid integration of solar PV systems. However, power quality and loss are the important issues while connecting the PV system to the medium voltage grid through MMC inverter. Modulation technique is the key to maintain output power quality, e.g., total harmonic distortion (THD) and to ensure low switching and conduction losses. In this paper, an advanced modulation technique named “triangle saturated common mode pulse width modulation (TSCMPWM)” control is proposed for a 3-phase 5-level MMC inverter-based grid-tied PV system. Compared to traditional modulation techniques, the proposed TSCMPWM control offers the lowest voltage THD as well as lower inverter power losses. Performance of the proposed modulation technique is evaluated in MATLAB/Simulink environment and tested with a reduced scale prototype test platform. Both simulation and experimental results show the effectiveness of the proposed modulation technique.
The Impact of Forecasting Jumps on Forecasting Electricity Prices
Authors: Kostrzewski Maciej , Kostrzewska Jadwiga
Published: January 2021

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.
A Thermodynamic Analysis of Heavy Hydrocarbons Reforming for Solid Oxide Fuel Cell Application as a Part of Hybrid Energy Systems
Authors: Kaczmarczyk Robert , Gurgul Sebastian
Published: January 2021

A thermodynamical analysis of steam reforming of Associated Petroleum Gas (APG) was conducted in the presented research. The reforming process of heavy hydrocarbons for small scale power generation is a complex issue. One of the main issues is that a set of undesired chemical reactions deposit solid carbon and, consequently, block the reactor’s catalytic property. The experimental investigation is crucial to design an APG reforming reactor. However, a numerical simulation is a key tool to design a safe operating condition. Designing the next generation of reactors requires a complex coupling of mathematical models, kinetics, and thermodynamic analysis. In practice, the thermodynamic analysis should be applied in each control volume to assure realistic results. This is not easy to apply in practice since both thermodynamic analysis and CFD modeling can be time-consuming. In this paper, the authors suggest using a mathematical formalism called Parametric Equation Formalism to calculate the equilibrium composition. The novelty lies in the mathematical approach in which any complex system at equilibrium can be reduced to the problem of solving one non-linear equation at a time. This approach allows implementing a thermodynamic analysis easily into CFD models to assure the reasonability of obtained results and can be used for research and development of solid oxide fuel cells as a part of hybrid energy systems.
Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast
Authors: Donadio Lorenzo , Fang Jiannong , Porté-Agel Fernando
Published: January 2021

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.
Towards Fossil Free Cities—A Supermarket, Greenhouse & Dwelling Integrated Energy System as an Alternative to District Heating: Amsterdam Case Study
Authors: ten Caat Nick , Graamans Luuk , Tenpierik Martin , van den Dobbelsteen Andy
Published: January 2021

The municipality of Amsterdam has set stringent carbon emission reduction targets: 55% by 2030 and 95% by 2050 for the entire metropolitan area. One of the key strategies to achieve these goals entails a disconnection of all households from the natural gas supply by 2040 and connecting them to the existing city-wide heat grid. This paper aims to demonstrate the value of considering local energy potentials at the city block level by exploring the potential of a rooftop greenhouse solar collector as a renewable alternative to centralized district heating. An existing supermarket and an ATES component complete this local energy synergy. The thermal energy balance of the three urban functions were determined and integrated into hourly energy profiles to locate and quantify the simultaneous and mismatched discrepancies between energy excess and demand. The excess thermal energy extracted from one 850 m2 greenhouse can sustain up to 47 dwellings, provided it is kept under specific interior climate set points. Carbon accounting was applied to evaluate the system performance of the business-as-usual situation, the district heating option and the local system. The avoided emissions due to the substitution of natural gas by solar thermal energy do not outweigh the additional emissions consequential to the fossil-based electricity consumption of the greenhouse’s crop growing lights, but when the daily photoperiod is reduced from 16 h to 12 h, the system performs equally to the business-as-usual situation. Deactivating growth lighting completely does make this local energy solution carbon competitive with district heating. This study points out that rooftop greenhouses applied as solar collectors can be a suitable alternative energy solution to conventional district heating, but the absence of growing lights will lead to diminished agricultural yields.
Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions
Authors: Sun Hao , Jiang Bo , You Heze , Yang Bojian , Wang Xueyuan , Wei Xuezhe , Dai Haifeng
Published: January 2021

The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.
Sorption-Enhanced Water-Gas Shift Reaction for Synthesis Gas Production from Pure CO: Investigation of Sorption Parameters and Reactor Configurations
Authors: Stadler Tabea J. , Barbig Philipp , Kiehl Julian , Schulz Rafael , Klövekorn Thomas , Pfeifer Peter
Published: January 2021

A sorption-enhanced water-gas shift (SEWGS) system providing CO2-free synthesis gas (CO + H2) for jet fuel production from pure CO was studied. The water-gas shift (WGS) reaction was catalyzed by a commercial Cu/ZnO/Al2O3 catalyst and carried out with in-situ CO2 removal on a 20 wt% potassium-promoted hydrotalcite-derived sorbent. Catalyst activity was investigated in a fixed bed tubular reactor. Different sorbent materials and treatments were characterized by CO2 chemisorption among other analysis methods to choose a suitable sorbent. Cyclic breakthrough tests in an isothermal packed bed microchannel reactor (PBMR) were performed at significantly lower modified residence times than those reported in literature. A parameter study gave an insight into the effect of pressure, adsorption feed composition, desorption conditions, as well as reactor configuration on breakthrough delay and adsorbed amount of CO2. Special attention was paid to the steam content. The significance of water during adsorption as well as desorption confirmed the existence of different adsorption sites. Various reactor packing concepts showed that the interaction of relatively fast reaction and relatively slow adsorption kinetics plays a key role in the SEWGS process design at low residence time conditions.
Modeling of Heavy-Oil Flow with Regard to Their Rheological Properties
Authors: Beloglazov Ilia , Morenov Valentin , Leusheva Ekaterina , Gudmestad Ove T.
Published: January 2021

With the depletion of traditional energy resources, the share of heavy-oil production has been increasing recently. According to some estimates, their reserves account for 80% of the world’s oil resources. Costs for extraction of heavy oil and natural bitumen are 3–4 times higher than the costs of extracting light oil, which is due not only to higher density and viscosity indicators but also to insufficient development of equipment and technologies for the extraction, transportation, and processing of such oils. Currently, a single pipeline system is used to pump both light and heavy oil. Therefore, it is necessary to take into account the features of the heavy-oil pumping mode. This paper presents mathematical models of heavy-oil flow in oil-field pipelines. The rheological properties of several heavy-oil samples were determined by experiments. The dependencies obtained were used as input data for a simulation model using computational fluid dynamics (CFD) methods. The modeling condition investigates the range of shear rates up to 300 s−1. At the same time, results up to 30 s−1 are considered in the developed computational models. The methodology of the research is, thus, based on a CFD approach with experimental confirmation of the results obtained. The proposed rheological flow model for heavy oil reflects the dynamics of the internal structural transformation during petroleum transportation. The validity of the model is confirmed by a comparison between the theoretical and the obtained experimental results. The results of the conducted research can be considered during the selection of heavy-oil treatment techniques for its efficient transportation.
Decision Support System of Innovative High-Temperature Latent Heat Storage for Waste Heat Recovery in the Energy-Intensive Industry
Authors: Royo Patricia , Acevedo Luis , Arnal Álvaro J. , Diaz-Ramírez Maryori , García-Armingol Tatiana , Ferreira Victor J. , Ferreira Germán , López-Sabirón Ana M.
Published: January 2021

Reductions in energy consumption, carbon footprint, equipment size, and cost are key objectives for the forthcoming energy-intensive industries roadmaps. In this sense, solutions such as waste heat recovery, which can be replicated into different sectors (e.g., ceramics, concrete, glass, steel, aluminium, pulp, and paper) are highly promoted. In this line, latent heat thermal energy storage (TES) contributes as an innovative technology solution to improve the overall system efficiency by recovering and storing industrial waste heat. To this end, phase-change material (PCM) selection is assisted through a decision-support system (DSS). A simplified tool based on the MATLAB® model, based on correlations among the most relevant system parameters, was developed to prove the feasibility of a cross-sectorial approach. The research work conducted a parametric analysis to assess the techno-economic performance of the PCM-TES solution under different working conditions and sectors. Additionally, a multicriteria assessment was performed comparing the tool outputs from metal alloys and inorganic hydrated PCM salts. Overall, the inorganic PCMs presented higher net economic and energy savings (up to 25,000 €/yr; 480 MWh/yr), while metal alloys involved promising results, shorter cycles, and competitive economic ratios; its commercial development is still limited.
Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China
Authors: Guo Yahui , Zeng Jing , Wu Wenxiang , Hu Shunqiang , Liu Guangxu , Wu Linsheng , Bryant Christopher Robin
Published: January 2021

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.
The Zoo of Modes of Convection in Liquids Vibrated along the Direction of the Temperature Gradient
Authors: Crewdson Georgie , Lappa Marcello
Published: January 2021

Thermovibrational flow can be seen as a variant of standard thermogravitational convection where steady gravity is replaced by a time-periodic acceleration. As in the parent phenomena, this type of thermal flow is extremely sensitive to the relative directions of the acceleration and the prevailing temperature gradient. Starting from the realization that the overwhelming majority of research has focused on circumstances where the directions of vibrations and of the imposed temperature difference are perpendicular, we concentrate on the companion case in which they are parallel. The increased complexity of this situation essentially stems from the properties that are inherited from the corresponding case with steady gravity, i.e., the standard Rayleigh–Bénard convection. The need to overcome a threshold to induce convection from an initial quiescent state, together with the opposite tendency of acceleration to damp fluid motion when its sign is reversed, causes a variety of possible solutions that can display synchronous, non-synchronous, time-periodic, and multi-frequency responses. Assuming a square cavity as a reference case and a fluid with Pr = 15, we tackle the problem in a numerical framework based on the solution of the governing time-dependent and non-linear equations considering different amplitudes and frequencies of the applied vibrations. The corresponding vibrational Rayleigh number spans the interval from Raω = 104 to Raω = 106. It is shown that a kaleidoscope of possible variants exist whose nature and variety calls for the simultaneous analysis of their temporal and spatial behavior, thermofluid-dynamic (TFD) distortions, and the Nusselt number, in synergy with existing theories on the effect of periodic accelerations on fluid systems.
First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Critical Points in Coupled Nonlinear Systems. I: Mathematical Framework
Authors: Cacuci Dan Gabriel
Published: January 2021

This work presents the novel first-order comprehensive adjoint sensitivity analysis methodology for critical points (1st-CASAM-CP), which enables the exact and efficient computation of the first-order sensitivities of responses defined at critical points (maxima, minima, saddle points) of coupled nonlinear models of physical systems characterized by imprecisely known parameters underlying the models, boundaries, and interfaces between the coupled systems. Responses defined at critical points are important in many applications, including system optimization, safety analyses and licensing. For the design and licensing of nuclear reactors, such essentially important responses include the maximum temperatures of the fuel and cladding in hot channels. The 1st-CASAM-CP presented in this work makes it possible to determine, using a single large-scale “adjoint” computation, the first-order sensitivities of the magnitude of a response defined at a critical point of a function in the phase-space of the systems’ independent variables. In addition, the 1st-CASAM-CP enables the computation of the sensitivities of the location in phase-space of the critical point at which the respective response is located: one “adjoint” computation is required for each component of the respective critical point in the phase-space of independent variables. By enabling the exact and efficient computation of the sensitivities of responses and of their critical locations to imprecisely known model parameters, boundaries, and interfaces, the 1st-CASAM-CP significantly extends the practicality of analyzing crucially important responses for large-scale systems involving many uncertain parameters, interfaces, and boundaries.
First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Critical Points in Coupled Nonlinear Systems. II: Application to a Nuclear Reactor Thermal-Hydraulics Safety Benchmark
Authors: Cacuci Dan Gabriel
Published: January 2021

Responses defined at critical points are particularly important for reactor safety analyses and licensing (e.g., the maximum fuel and/or clad temperature). The novel mathematical framework of the first-order comprehensive adjoint sensitivity analysis methodology for critical points (1st-CASAM-CP) is applied in this work to develop a reactor safety thermal-hydraulics benchmark model which admits exact closed-form expressions for the adjoint functions and for the first-order sensitivities of responses defined at critical points (maxima, minima, saddle points) in physical systems characterized by imprecisely known parameters, external and internal boundaries. This benchmark model is designed for verifying the capabilities and accuracies of computational tools for modeling numerically thermal-hydraulics systems. The unique and extensive capabilities of the 1st-CASAM-CP methodology are demonstrated in this work by considering two responses of paramount importance in reactor safety, namely, (i) the maximum rod surface temperature, which occurs at the imprecisely known interface between the subsystem that models the heat conduction inside the heated rod and the subsystem modeling the heat convection process surrounding the rod; and (ii) the maximum temperature inside the heated rod, which has a critical point with two components, one located at a precisely known boundary of the subsystem that models the heat conduction inside the heated rod, while the other component depends on an imprecisely known boundary (i.e., the rod length). The exact analytical expressions developed in this work for the sensitivities of the maximum internal rod temperature and maximum rod surface temperature, as well as for the sensitivities of the locations where these respective maxima occur, provide exact benchmarks for verifying the accuracy of thermal-hydraulics computational tools. The sensitivities of such responses and of their critical points with respect to model parameters enable the quantification of uncertainties induced by uncertainties stemming from the system’s parameters and boundaries in the respective responses and their underlying critical points.
Coherent Streamwise Vortex Structure of a Three-Dimensional Wall Jet
Authors: Namgyal Lhendup , Hall Joseph W.
Published: January 2021

The dynamics of the coherent structures in a turbulent three-dimensional wall jet with an exit Reynolds number of 250,000 were investigated using the Snapshot Proper Orthogonal Decomposition (POD). A low-dimensional reconstruction using the first 10 POD modes indicates that the turbulent flow is dominated by streamwise vortex structures that grow in size and relative strength, and that are often accompanied by strong lateral sweeps of fluid across the wall. This causes an increase in the bulging and distortions of streamwise velocity contours as the flow evolves downstream. The instantaneous streamwise vorticity computed from the reconstructed instantaneous velocities has a high level of vorticity associated with these outer streamwise vortex structures, but often has a persistent pair of counter-rotating regions located close to the wall on either side of the jet centerline. A model of the coherent structures in the wall jet is presented. In this model, streamwise vortex structures are produced in the near-field by the breakdown of vortex rings formed at the jet outlet. Separate structures are associated with the near-wall streamwise vorticity. As the flow evolves downstream, the inner near-wall structures tilt outward, while the outer streamwise structures amalgamate to form larger streamwise asymmetric structures. In all cases, these streamwise vortex structures tend to cause large lateral velocity sweeps in the intermediate and far-field regions of the three-dimensional wall jet. Further, these structures meander laterally across the jet, causing a strongly intermittent jet flow.
By-Products of Agri-Food Industry as Tannin-Rich Sources: A Review of Tannins’ Biological Activities and Their Potential for Valorization
Authors: Fraga-Corral María , Otero Paz , Echave Javier , Garcia-Oliveira Paula , Carpena Maria , Jarboui Amira , Nuñez-Estevez Bernabé , Simal-Gandara Jesus , Prieto Miguel A.
Published: January 2021

During recent decades, consumers have been continuously moving towards the substitution of synthetic ingredients of the food industry by natural products, obtained from vegetal, animal or microbial sources. Additionally, a circular economy has been proposed as the most efficient production system since it allows for reducing and reutilizing different wastes. Current agriculture is responsible for producing high quantities of organic agricultural waste (e.g., discarded fruits and vegetables, peels, leaves, seeds or forestall residues), that usually ends up underutilized and accumulated, causing environmental problems. Interestingly, these agri-food by-products are potential sources of valuable bioactive molecules such as tannins. Tannins are phenolic compounds, secondary metabolites of plants widespread in terrestrial and aquatic natural environments. As they can be found in plenty of plants and herbs, they have been traditionally used for medicinal and other purposes, such as the leather industry. This fact is explained by the fact that they exert plenty of different biological activities and, thus, they entail a great potential to be used in the food, nutraceutical and pharmaceutical industry. Consequently, this review article is directed towards the description of the biological activities exerted by tannins as they could be further extracted from by-products of the agri-food industry to produce high-added-value products.