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This simplified model provides solutions for the current-voltage characteristics of a sheath in a dense flowing plasma when surface chemistry contributes secondary ions. The problem is motivated by the recent discovery that strong transient signals in industrial ion current sensors are caused by chemical reactions with carbon in the steel being cut or welded by oxyfuel processes. The one-dimensional model considers a quasi-uniform dense plasma flowing towards and stagnating on an absorbing surface, above which there is a source of secondary ions. Because the secondary ions are formed directly in the plasma sheath, they have strong impacts on the current-voltage characteristic. With ionic Reynolds number,R, and integral length scale,α, secondary ion formation rate, Ω, and length scale,β, saturation currents are simplyR+βΩ untilβ≪ 1, at which point, new electrons cannot escape the sheath, and secondary ions have no effect. Floating potential,ϕ_∞, scales like$\exp ({\varphi }_{\infty })\propto R^{-3/4}$, and secondary ions have little impact unlessβ²Ω > 1. Even then, floating potential is only weakly affected by secondary ion formation. The integral length scale,α, is not found to strongly affect the results.

 

A three-dimensional computational model is presented in this paper that illustrates the detailed electrical characteristics, and the current–voltage (i–v) relationship throughout the preheating process of premixed methane-oxygen oxyfuel cutting flame subject to electric bias voltages. As such, the equations describing combustion, electrochemical transport for charged species, and potential are solved through a commercially available finite volume computational fluid dynamics (CFD) code. The reactions of the methane-oxygen (CH4–O2) flame were combined with a reduced mechanism, and additional ionization reactions that generate three chemi-ions, H3O+, HCO+, and e−, to describe the chemistry of ions in flames. The electrical characteristics such as ion migrations and ion distributions are investigated for a range of electric potential, V ∈ [−5 V, +5 V]. Since the physical flame is comprised of twelve Bunsen-like conical flames, inclusion of the third dimension imparts the resolution of fluid mechanics and the interaction among the individual cones. It was concluded that charged “sheaths” are formed at both torch and workpiece surfaces, subsequently forming three distinct regimes in the i–v relationship. The i–v characteristics obtained from this study have been compared to the previous experimental and two-dimensional computational model for premixed flame. In this way, the overall model generates a better understanding of the physical behavior of the oxyfuel-cutting flames, along with more validated i–v characteristics. Such understanding might provide critical information toward achieving an autonomous oxyfuel-cutting process.

 

Recent use of ion currents as a sensing strategy in the mechanized oxyfuel cutting process motivated a series of studies which revealed that the steel work piece contributes secondary ions in addition to the primary ions classically identified in the oxyfuel flame. In this work, we present a computational model that has linked carbon-related chemi-ions as a source of secondary ions in preheating stage of oxyfuel cutting process subject to electric bias voltages. The flames' response to the electric field at different positive and negative polarities manifested a better understanding of the physical behavior of current-voltage (i-v) relationship. While copper surface exhibits stable and repeatable i-v characteristics, sporadically enhanced current was observed in positive saturation regime for steel surface, and this is believed to be due to the presence of secondary chemi-ions. To this extent, a source term of gaseous carbon has been assigned to mimic the ‘work surface’ reactions. The hypothesis is that since carbon is an important element, it will be diffusing out of the steel surface and evaporate into the flame.

 

Spoke-type PMSMs were designed with commercial permanent magnets and theoretically designed hexaferrite: Nd-Fe-B (NdFe35, G1NH), Alnico (8B, 8H, 9), and La-CoSrM hexaferrite (NMF-15G). It was found that coercivity (Hc) plays a crucial role in determining motor performance. The ANSYS Maxwell software was used to characterize the designed motor performance. Commercial RE-free Alnico 9 holds a 10.5 MGOe of (BH)max, much higher than a 5.5 MGOe of RE-free Alnico 8B/8H and SrM (SrFe12O19) hexaferrite magnets. However, the Alnico 9 motor performance is not better than the other Alnico 8B/8H and hexaferrite motors. The spoke-type PMSM with our theoretically designed SrM hexaferrite simulated motor performance. A motor performs best when the Hc/Br ratio equals one with a high Hc. For instance, the motor torque and peak power increase to 189 Nm and 178 kW, respectively, as the Hc increases to 4.86 kOe from 2.43 kOe. However, the motor performance is not significantly changed with a fixed Hc and various Br. It was found that regardless of (BH)max, coercivity (Hc) plays a dominant role in motor performance. 

A three-dimensional (3D) computational model is presented in this paper that illustrates the detailed electrical characteristics, and the current-voltage (i-v) relationship throughout the preheating process of premixed methane-oxygen oxyfuel cutting flame subject to electric bias voltages. As such, the equations describing combustion, electrochemical transport for charged species, and potential are solved through a commercially available finite-volume Computational Fluid Dynamics (CFD) code. The reactions of the methane-oxygen (CH4 – O2) flame were combined with a reduced mechanism, and additional ionization reactions that generate three chemi-ions, H3O+, HCO+, and e−, to describe the chemistry of ions in flames. The electrical characteristics such as ion migrations and ion distributions are investigated for a range of electric potential, V ∈ [−5V, +5V]. Since the physical flame is comprised of twelve Bunsen-like conical flame, inclusion of the third dimension imparts the resolution of fluid mechanics and the interaction among the individual cones. It was concluded that charged ‘sheaths’ are formed at both torch and workpiece surfaces, subsequently forming three distinct regimes in the i-v relationship. The i-v characteristics obtained out of the current study have been compared to the previous experimental and two-dimensional (2D) computational model for premixed flame. In this way, the overall model generates a better understanding of the physical behavior of the oxyfuel cutting flames, along with a more validated i-v characteristics. Such understanding might provide critical information towards achieving an autonomous oxyfuel cutting process.

 

The Neural Engineering Data Consortium at Temple University has been providing key data resources to support the development of deep learning technology for electroencephalography (EEG) applications [1-4] since 2012. We currently have over 1,700 subscribers to our resources and have been providing data, software and documentation from our web site [5] since 2012. In this poster, we introduce additions to our resources that have been developed within the past year to facilitate software development and big data machine learning research. Major resources released in 2019 include: ● Data: The most current release of our open source EEG data is v1.2.0 of TUH EEG and includes the addition of 3,874 sessions and 1,960 patients from mid-2015 through 2016. ● Software: We have recently released a package, PyStream, that demonstrates how to correctly read an EDF file and access samples of the signal. This software demonstrates how to properly decode channels based on their labels and how to implement montages. Most existing open source packages to read EDF files do not directly address the problem of channel labels [6]. ● Documentation: We have released two documents that describe our file formats and data representations: (1) electrodes and channels [6]: describes how to map channel labels to physical locations of the electrodes, and includes a description of every channel label appearing in the corpus; (2) annotation standards [7]: describes our annotation file format and how to decode the data structures used to represent the annotations. Additional significant updates to our resources include: ● NEDC TUH EEG Seizure (v1.6.0): This release includes the expansion of the training dataset from 4,597 files to 4,702. Calibration sequences have been manually annotated and added to our existing documentation. Numerous corrections were made to existing annotations based on user feedback. ● IBM TUSZ Pre-Processed Data (v1.0.0): A preprocessed version of the TUH Seizure Detection Corpus using two methods [8], both of which use an FFT sliding window approach (STFT). In the first method, FFT log magnitudes are used. In the second method, the FFT values are normalized across frequency buckets and correlation coefficients are calculated. The eigenvalues are calculated from this correlation matrix. The eigenvalues and correlation matrix's upper triangle are used to generate feature. ● NEDC TUH EEG Artifact Corpus (v1.0.0): This corpus was developed to support modeling of non-seizure signals for problems such as seizure detection. We have been using the data to build better background models. Five artifact events have been labeled: (1) eye movements (EYEM), (2) chewing (CHEW), (3) shivering (SHIV), (4) electrode pop, electrostatic artifacts, and lead artifacts (ELPP), and (5) muscle artifacts (MUSC). The data is cross-referenced to TUH EEG v1.1.0 so you can match patient numbers, sessions, etc. ● NEDC Eval EEG (v1.3.0): In this release of our standardized scoring software, the False Positive Rate (FPR) definition of the Time-Aligned Event Scoring (TAES) metric has been updated [9]. The standard definition is the number of false positives divided by the number of false positives plus the number of true negatives: #FP / (#FP + #TN). We also recently introduced the ability to download our data from an anonymous rsync server. The rsync command [10] effectively synchronizes both a remote directory and a local directory and copies the selected folder from the server to the desktop. It is available as part of most, if not all, Linux and Mac distributions (unfortunately, there is not an acceptable port of this command for Windows). To use the rsync command to download the content from our website, both a username and password are needed. An automated registration process on our website grants both. An example of a typical rsync command to access our data on our website is: rsync -auxv nedc_tuh_eeg@www.isip.piconepress.com:~/data/tuh_eeg/ Rsync is a more robust option for downloading data. We have also experimented with Google Drive and Dropbox, but these types of technology are not suitable for such large amounts of data. All of the resources described in this poster are open source and freely available at https://www.isip.piconepress.com/projects/tuh_eeg/downloads/. We will demonstrate how to access and utilize these resources during the poster presentation and collect community feedback on the most needed additions to enable significant advances in machine learning performance. 

This is part of a larger effort bringing together a diverse design team, to create curriculum integrating computer coding and middle-school general-mathematics. The goal was to enhance the instruction of students that have been traditionally underserved in mathematics by using computer science ideas found in coding to complement, reinforce, and build on mathematics ideas in a meaningful way. The development of modules was guided by the principles of Design-Based Research and Realistic Mathematics Education instructional design heuristics, in particular by drawing on the notion of guided reinvention through emergent models. Here we present the design principles that emerged from the first half of the effort with the hopes of informing other projects that integrate coding and mathematics learning.