Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictions Of A Turbulent Flow Inside A Sharp U-Curve Duct For A Turbine Blade Cooling Passage

The internal cooling problem has been an issue of importance for the extending turbine blade life. This paper provides significant discovery of the improvement of simulation techniques for the flows inside a sharp U-curve duct. The finite volume difference method incorporated with the higher-order bounded interpolation scheme has been employed in the present study. For the purpose of comparison, the predictions with different turbulence models are also given. The results obtained through this research show that non-linear low-Re k-ω model and linear model produce the quite different secondary flow patterns. It is shown that the present non-linear model produces satisfactory predictions of the flow development inside the sharp U-curve duct. NOMENCLATURE c , a weighted average coefficients ( ) 9 ~ 1 = i C i turbulence modeling constants in Eq. (3) D hydraulic diameter k turbulence kinetic energy P mean pressure k P generation rate of j i u u c R radius of curvature of U-curve duct Re flow Reynolds number ( ) ν / D W b ≡ T Re local Reynolds number , , ω β R R Rk model constants ij S deformation tensor U mean velocity in cross duct direction i U mean velocity tensor u velocity fluctuation in crossduct direction j i u u Reynolds stress tensor W mean velocity in streamwise direction w velocity fluctuation in streamwise direction b W streamwise bulk velocity x crossduct direction y direction normal to the duct symmetry plane z streamwise direction Greek Symbols β α α , , * 0 0 model constants ij δ Kronecker delta ω specific dissipation rate ij Ω vorticity tensor t , μ μ laminar and turbulence viscosity ν kinematic viscosity Φ transport variable Superscript W Q, representation of QUICK and WACEB schemes, respectively INTRODUCTION To increase the efficiency and the power of modern power plant and aircraft gas turbines, designers are continually trying to raise the maximum turbine inlet temperature. Over the last decade the temperature has risen from 1500K to 1750K in some highperformance units. With this increase of the temperature only 25 percent can be attributed to improved alloys. New materials, such as ceramics, could help increase this maximum temperature even more in the future. However, most of the recent improvements in inlet temperature come from better cooling of the blades and greater Proceedings of the International Gas Turbine Congress 2003 Tokyo November 2-7, 2003
An overview on biogas generation from anaerobic digestion of food waste

ABSTRACT Food waste is an inevitable type of waste in every city, and its treatment technology evolves with time. Due to the high organic content and high biodegradability of food waste, anaerobic digestion becomes a commonly accepted treatment method to deal with it. This review article summarizes key factors for anaerobic digestion and provides useful information for successful anaerobic digestions. Reasonable temperature and pH are essential for a successful and productive anaerobic digestion process. A good inoculum to substrate ratio triggers a profitable food waste digestion. Good mixing and small particle sizes are important factors too. In addition, the pros and cons of different reactors to food waste digestion are highlighted. Moreover, co-digestion of food waste with animal manures, sewage sludge, and green waste were introduced.
Appendix A. THE DE RHAM-WITT COMPLEX AND ADDITIVE CHOW GROUPS OVER A FIELD: THE CHARACTERISTIC 2 CASE

A.1. Let (WSΩA)S denote the big de Rham Witt complex from [Hes15], where A is a ring and S is running through all truncation sets. It comes with the maps d, Fn, Vn, restriction, and multiplication. For m ≥ 1 we set WmΩA := W{1,. ,m}ΩA and for a fixed prime p we denote the p-typical de Rham-Witt complex by WnΩ • A := W{1,p,. ,pn−1}ΩA. Note that in general d ◦ d is not zero in WSΩA. Denote by WSΩA/Z the Witt complex from [Hes15, Rem 4.8] (with k = W(Z)); it is the initial object in the category of Witt complexes with W(Z)-linear differential. Note that WSΩA/Z is always a dga, in particular we have d◦d = 0. Furthermore, if A contains a field, then WSΩA/Z = WSΩ • A see [Hes15, Rem 4.2, c)]; if A is an Fp-algebra, then the p-typical de Rham-Witt complex is the one from Bloch-Deligne-Illusie. Also note in case A is an Fp-algebra or a Q-algebra, then we have a decomposition
The devil is in the details: Reporting and transparent research practices in sports medicine and orthopedic clinical trials

Introduction: While transparent reporting of clinical trials is essential to assess the risk of bias and translate research findings into clinical practice, earlier studies have shown that deficiencies are common. This study examined current clinical trial reporting and transparent research practices in sports medicine and orthopedics. Methods: The sample included clinical trials published in the top 25% of sports medicine and orthopedics journals over eight months. Two independent reviewers assessed pre-registration, open data and criteria related to scientific rigor, the study sample, and data analysis. Results: The sample included 163 clinical trials from 27 journals. While the majority of trials mentioned rigor criteria, essential details were often missing. Sixty percent (confidence interval [CI] 53-68%) of trials reported sample size calculations, but only 32% (CI 25-39%) justified the expected effect size. Few trials indicated the blinding status of all main stakeholders (4%; CI 1-7%). Only 18% (CI 12-24%) included information on randomization type, method, and concealed allocation. Most trials reported participants' sex/gender (95%; CI 92-98%) and information on inclusion and exclusion criteria (78%; CI 72-84%). Only 20% (CI 14-26%) of trials were pre-registered. No trials deposited data in open repositories. Conclusions: These results will aid the sports medicine and orthopedics community in developing tailored interventions to improve reporting. While authors typically mention blinding, randomization and other factors, essential details are often missing. Greater acceptance of open science practices, like pre-registration and open data, is needed. These practices have been widely encouraged, we discuss systemic interventions that may improve clinical trial reporting. Registration: https://doi.org/10.17605/OSF.IO/9648H
Evaluation of a city-wide school-located influenza vaccination program in Oakland, California with respect to race and ethnicity: A matched cohort study

Objectives: To evaluate the effectiveness of city-wide school-located influenza vaccination by race/ethnicity from 2014-2018. Methods: We used multivariate matching to pair schools in the intervention district in Oakland, CA with schools in West Contra Costa County, CA, a comparison district. We estimated difference-in-differences (DIDs) in caregiver-reported influenza vaccination coverage and laboratory-confirmed influenza hospitalization incidence. Results: Differences in influenza vaccination coverage in the intervention vs. comparison site were larger among White and Latino students than Asian/Pacific Islander (API), Black, and multiracial students. Concerns about vaccine effectiveness or safety were more common among Black and multiracial caregivers; logistical barriers to vaccination were more common among White, API, and Latinos. In both sites, hospitalization in 2017-18 was higher in Blacks vs. other races/ethnicities. All-age influenza hospitalization incidence was lower in the intervention site vs. comparison site among White/API individuals in 2016-17 and 2017-18 and Black older adults in 2017-18, but not in other groups. Conclusions: SLIV was associated with higher vaccination coverage and lower influenza hospitalization, but associations varied by race/ethnicity. SLIV alone may be insufficient to ensure equitable health outcomes for influenza.
Requirements-Driven Automotive Electrical/Electronic Architecture: A Survey and Prospective Trends

The automotive E/E architecture has undergone a paradigm shift in the past century. Particularly, the new requirements of automated driving are severely challenging the existing architecture, which has led to ongoing revolutionary innovation in E/E architecture. In this paper, we reviewed the evolution of E/E architecture and outlined the requirements-driven developments. We illustrated the state-of-the-art E/E architecture, including network topology, standards, simulator and software platform. We also discussed the next generation of E/E architecture from the perspective of different OEMs and suppliers. The analysis shows that software-defined, hierarchical, reconfigurable and customized E/E architecture is universally accepted. Furthermore, the automotive industry has been experiencing several transitions related to OEMs and suppliers. With the emergence and maturation of automated driving, we analyzed the new requirements on E/E architecture and proposed prospective development trends.
Intelligent Traffic Flow Prediction Using Optimized GRU Model

Facilitating citizens with accurate traffic flow prediction increases the quality of life. Roadside sensors and devices are used to capture live streams of huge data and the Internet of Things (IoT) is becoming popular for the deployment of effective Intelligent Transportation Systems (ITS). Traffic flow prediction from the live datastreams require building a data-driven model. This is a challenging task and has attracted researchers for better interpretation of the traffic characteristics. The core problem in traffic prediction is modeling a diversity of traffic trends and unpredictable flow variations with temporal dependencies. Initially, statistical and shallow neural network models were applied to some extent. Recently, deep learning has come up with proven and promising outcomes. Gated Recurrent Unit (GRU) is a variation of recurrent neural networks used effectively for traffic flow prediction. Like other deep networks, GRU uses hyperparameters and a sliding window time-steps mechanism to prepare and tune the model. Better tuning for hyperparameters and search for optimal window size is a tedious process. In this research work, we present an algorithm for hyperparameters tuning along with sliding window steps optimization. Results obtained on a real-time public traffic dataset show a higher capability of the proposed method to reduce the error and an average gain of the optimized model over the untuned network is 4.5%. Furthermore, we apply the optimal hyperparameters obtained in the experiment to other deep learning models and present that our approach improves prediction accuracy and stability.
Autonomous System Concept of Multiple-Receiver Inductive Coupling Wireless Power Transfer for Output Power Stabilization Against Cross-Interference Among Receivers and Resonance Frequency Tolerance

Multiple-receiver resonant inductive coupling wireless power transfer (RIC-WPT) technique is emerging as a promising charging method for multiple household appliances, mobile devices, and wearable devices. However, this technique often suffers from output power fluctuation owing to the cross-interference among receivers as well as the manufacturing and aging tolerance of the resonant frequency (i.e., resonant frequency tolerance). This article proposes an autonomous RIC-WPT system concept to solve this difficulty. The proposed concept addresses the cross-interference and resonant frequency tolerance by incorporating the following two functions. First, the amplitude of the transmitter current is controlled to have a constant amplitude. Second, the phase of each receiver current is controlled to be orthogonal to that of the transmitter current by using an active reactance compensator installed in each receiver. The experiment of a two-receiver RIC-WPT system successfully verified that the two functions of the proposed system concept can stabilize the output voltage against the cross-interference and resonant frequency tolerance.
Shape Parameters Design for Improving Energy Efficiency of IPM Traction Motor for EV

In this paper, a traction motor for maximum power 76kW electrical vehicle (EV) was designed, and the design results were verified through experiments. One of the important design objectives of the traction motor for EV is to improve energy efficiency. The energy efficiency of a traction motor affects the mileage of electric vehicles (EVs). Among the design factors influencing energy efficiency, shape parameters include torque per rotor volume (TRV), torque density (TD) and shape ratio (SR). These shape parameters have an impact on the motor's phase back EMF, inductance, winding resistance and core loss resistance, eventually affecting the energy efficiency in the entire operating range of the motor. In this paper, a New European Driving Cycle (NEDC) was applied to calculate the energy efficiency of the traction motor for EV. Changes in motor parameters such as phase back electromotive force (EMF), inductance, winding resistance and core loss resistance were identified according to variations in shape parameters. In addition, the motor characteristics according to the changes of these parameters were confirmed, and shape parameters to improve energy efficiency were selected. Since the traction motor designed in this paper adopted a water cooling method, thermal analysis was performed by reflecting the water cooling method in the thermal equivalent circuit (TEC). In order to confirm the reliability of the analysis method and design, a load test was conducted on the entire operating range to calculate energy efficiency, and a temperature test was performed to compare the test results with the analysis results.
Artificial Noise Assisted In-Band Full-Duplex Secure Channel Estimation

This paper proposes a novel secure channel estimation technique to provide security against leakage of the channel estimates to any malicious user by utilizing artificial noise (AN) along with full-duplex (FD) transmissions. AN overcomes the drawback of FD transmission, where any strategically located eavesdropper can minimize the interference signal received from the FD receiver. The proposed secure channel estimation technique comprises three stages, where the first stage is responsible for the estimation of the residual self-interference (SI) channel. The second stage acquires rough channel estimates to design AN orthogonal to the channel between legitimate transmitter-receiver for the next training stage. In the third stage, both legitimate nodes transmit orthogonal AN signals along with the known training signals using FD transmissions. For power allocation, we have presented a novel local adaptive power allocation algorithm at each legitimate node to allocate the powers to the training signals, and AN signals while ensuring equivocation at the eavesdropper. We provide the mean square error (MSE) to indicate the performance achieved by the respective nodes. We have also provided the bit error rate (BER) simulation analysis to indicate the secure communication achieved by securing the channel estimation process. The presented simulation analysis indicates that the eavesdropper is unable to decode the transmitted information while the legitimate receiver has robustly decoded the transmitted information.
Rotation-Aware 3D Vehicle Detection From Point Cloud

Three-dimensional vehicle detection using LiDAR point clouds is important for the stability of autonomous driving. It can provide high-quality three-dimensional information for most obstacles. Although efficient algorithms based on a Bird’s Eye View (BEV) feature map have been developed, many research issues still remain; in particular, the existing methods show a limited accuracy in estimating the rotation angle of a 3D object. In this paper, to improve the accuracy of rotation angle estimation, we propose a rotation-aware 3D vehicle detector that extracts distinguishable features from the proposals with various angles of rotations. Experiments are conducted on KITTI dataset and Waymo Open dataset. Our approach improves detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency.
Digital Predistortion of Millimeter-Wave Hybrid Beamforming Transmitters Using Observation Receivers With Low-Bit Resolution Analog-to-Digital Converter

This paper proposes a new digital predistortion (DPD) scheme for linearizing millimeter-wave hybrid beamforming transmitters using observation receivers with low-bit resolution analog-to-digital converters (ADCs). To train the DPD function required to compensate for the distortions exhibited by a given sub-array (also called main sub-array), an error signal is produced by out-of-phase combining the main sub-array transmitted signal and the one generated by another sub-array (also called auxiliary sub-array) using anti-beamforming modules. The error signal is then frequency down-converted and digitized using a low-bit resolution ADC. Proof-of-concept validation experiments are conducted by applying the proposed DPD system to linearize an off-the-shelf hybrid-beamforming array comprised of four 64-element sub-arrays, operating at 28 GHz and driven with up to 800 MHz orthogonal frequency-division multiplexing modulated signals. Using the proposed DPD scheme, an observation receiver with a 4-bit ADC was sufficient to improve the adjacent channel power ratio by 10 dB and the error vector magnitude was reduced from 5.8% to 1.6%. These results are similar to those obtained using an observation receiver with 16-bit ADC.
On the Electromagnetic Performance Prediction of Turbo Synchronous Condensers Based on Wound-Field Flux Switching Machine Design

The revival of the old synchronous condenser (SC) technology is presently occurring. Hence, this study is proposed on the electromagnetic design and evaluation of turbo wound-field flux switching machines (WF-FSMs) for SC operation, for the first time. Unlike classical wound-field rotating machines, WF-FSMs do not need brushless exciters while possessing a robust topology. A design process based on the frozen permeability technique in static 2-D finite element analysis script-based package, which enhances both iterative and analytic adjudications, is proposed to accurately predict the SC operation. To attain the specific design requirements (∼300 MVAR, 16 kV), a global-optimized model (GOM) of the SC machine is obtained through a stochastic optimization process involving multiple objectives. Based on the GOM, simple torque ripple minimization techniques are implemented, from which a final benchmark design is selected. The proposed SC analytical design procedure is then tested and the expected V-curves clearly obtained. The whole design approach is further verified by experimenting on a small-scale prototype, which when synchronized to the grid, confirms the novelty and feasibility of the proposed SC design and operation.
Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for Microgrids

Future generation power grids will require the introduction and deployment of distributed energy resources to meet modern-day load requirements. Consequently, we expect to see a rise in microgrids (MGs) existing as part of the main grid (grid-connected) or independent (islanded). Contained in these microgrids are a combination of energy resources such as solar, wind and fossil fuels coupled with storage devices, electric vehicles and smart devices supporting the prosumer operation. However, the addition of renewable energy resources would mean fluctuations in energy supply which would cause power system instability if not managed effectively. Hence, to maximize the management flexibility of MGs, the concept of microgrid software definition is introduced. A concept that can be looked at as giving the microgrid an operating system to improve operation response and event detection by maintaining a global view of the network. This paper therefore critically analyses what this entails by presenting an architecture for Software-Defined Microgrids (SDMGs) and discussing the management opportunities that softwarization of the MG introduces. We also highlight the design requirements and associated challenges in implementing and deploying SDMGs.
Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system.
Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning

The rise of Artificial Intelligence (AI) enables enterprises to manage large amounts of data in order to derive predictions about future performance and to gain meaningful insights. In this context, descriptive and predictive analytics has gained a significant research attention; however, prescriptive analytics has just started to emerge as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time. Although machine learning for decision making has been identified as one of the most important applications of AI, up to now, prescriptive analytics is mainly addressed with domain-specific optimization models. On the other hand, existing literature lacks generalized prescriptive analytics models capable of being dynamically adapted according to the human preferences. Reinforcement Learning, as the third machine learning paradigm alongside supervised learning and unsupervised learning, has the potential to deal with the dynamic, uncertain and time-variant environments, the huge states space of sequential decision making processes, as well as the incomplete knowledge. In this paper, we propose a human-augmented prescriptive analytics approach using Interactive Multi-Objective Reinforcement Learning (IMORL) in order to cope with the complexity of real-life environments and the need for optimized human-machine collaboration. The decision making process is modelled in a generalized way in order to assure scalability and applicability in a wide range of problems and applications. We deployed the proposed approach in a stock market case study in order to evaluate the proactive trading decisions that will lead to the maximum return and the minimum risk that the user’s experience and the available data can yield in combination.