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Laminar Non-Premixed Flames of Gaseous Fuel Aboard the International Space Station
From late 2017 to early 2022, six independent studies with flames of gaseous fuels were conducted on the International Space Station (ISS) in the U.S. combustion research facility. An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh, downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners, yielding a nominally one-dimensional flame structure. The objectives and selected findings of each investigation will be briefly discussed after a short review of the advantages of studying combustion in microgravity, earlier ISS research, and the experimental hardware and its operation.
Laminar Non-Premixed Flames of Gaseous Fuel Aboard the International Space Station
From late 2017 to early 2022, six independent studies with flames of gaseous fuels were conducted on the International Space Station (ISS) in the U.S. combustion research facility. An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh, downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners, yielding a nominally one-dimensional flame structure. The objectives and selected findings of each investigation will be briefly discussed after a short review of the advantages of studying combustion in microgravity, earlier ISS research, and the experimental hardware and its operation.
ACME: Advanced Combustion via Microgravity Experiments
From Nov. 2017 to Feb. 2022, the ACME project’s six independent investigations of laminar, non-premixed flames of gaseous fuels were conducted on the International Space Station (ISS) in the Combustion Integrated Rack (CIR). An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners yielding a nominally one-dimensional flame structure. The ACME research will be represented through images and text.
Processing Noisy Vibration Responses of Dynamic Structural Flight Data: Single or Multichannel Approach?
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A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging
Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.
A symmetrical quasi-classical windowing model for the molecular dynamics treatment of non-adiabatic processes involving many electronic states
In the previous work of Cotton and Miller [J. Chem. Phys. 145, 144108 (2016)], an improved symmetrical quasi-classical (SQC) windowing model for the molecular dynamics treatment of electronically non-adiabatic processes was developed in order to extend the original SQC approach to the regime of weak-coupling between the electronic states. Furthermore, the improved SQC model—based on triangular-shaped window functions—handled the weak-coupling limit as intended and, as a bonus, was shown to be universally superior to the original square/histogram SQC windowing model over all coupling regimes, but only for treating systems of two electronic states, as no higher-dimensional generalization was evident. This paper, therefore, provides a generalized version for treating an arbitrary number of electronic states. By construction, the benefits of the two-state triangle model—seamless treatment of weak-coupling and improved accuracy in all coupling regimes—carry over to the generalized version. Far more significant, however, is that the new model provides vastly improved windowing statistics in higher dimensions, enabling the SQC simulation of electronically non-adiabatic processes involving many more relevant electronic states than was previously practical. Capabilities are demonstrated with respect to a 24 pigment trimer model of the Fenna-Matthews-Olson light-harvesting complex, as well as treating similar 48- and 96-electronic state model problems, illustrating the scaling properties of the new method.
Dynamic Model of Basic Oxygen Steelmaking Process Based on Multi-zone Reaction Kinetics: Model Derivation and Validation
A multi-zone kinetic model coupled with a dynamic slag generation model was developed for the simulation of hot metal and slag composition during the basic oxygen furnace (BOF) operation. The three reaction zones (i) jet impact zone, (ii) slag–bulk metal zone, (iii) slag–metal–gas emulsion zone were considered for the calculation of overall refining kinetics. In the rate equations, the transient rate parameters were mathematically described as a function of process variables. A micro and macroscopic rate calculation methodology (micro-kinetics and macro-kinetics) were developed to estimate the total refining contributed by the recirculating metal droplets through the slag–metal emulsion zone. The micro-kinetics involves developing the rate equation for individual droplets in the emulsion. The mathematical models for the size distribution of initial droplets, kinetics of simultaneous refining of elements, the residence time in the emulsion, and dynamic interfacial area change were established in the micro-kinetic model. In the macro-kinetics calculation, a droplet generation model was employed and the total amount of refining by emulsion was calculated by summing the refining from the entire population of returning droplets. A dynamic Fe{sub t}O generation model based on oxygen mass balance was developed and coupled with the multi-zone kinetic model. The effect of post-combustion on the evolution of slag and metal composition was investigated. The model was applied to a 200-ton top blowing converter and the simulated value of metal and slag was found to be in good agreement with the measured data. The post-combustion ratio was found to be an important factor in controlling Fe{sub t}O content in the slag and the kinetics of Mn and P in a BOF process.
The dynamics of the spatial attributes of movements in the process of formation of images of gymnastic exercises
Dynamics of spatial attributes of movements in process of formation of images of gymnastic exercises
AR/D image processing system
General Dynamics has developed advanced hardware, software, and algorithms for use with the Tomahawk cruise missile and other unmanned vehicles. We have applied this technology to the problem of locating and determining the orientation of the docking port of a target vehicle with respect to an approaching spacecraft. The system described in this presentation utilizes a multi-processor based computer to digitize and process television imagery and extract parameters such as range to the target vehicle, approach, velocity, and pitch and yaw angles. The processor is based on the Inmos T-800 Transputer and is configured as a loosely coupled array. Each processor operates asynchronously and has its own local memory. This allows additional processors to be easily added if additional processing power is required for more complex tasks. Total system throughput is approximately 100 MIPS (scalar) and 60 MFLOPS and can be expanded as desired. The algorithm implemented on the system uses a unique adaptive thresholding technique to locate the target vehicle and determine the approximate position of the docking port. A target pattern surrounding the port is than analyzed in the imagery to determine the range and orientation of the target. This information is passed to an autopilot which uses it to perform course and speed corrections. Future upgrades to the processor are described which will enhance its capabilities for a variety of missions.
Investigating the Effects of Mixing Dynamics on Twin-Screw Granule Quality Attributes via the Development of a Physics-Based Process Map
Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism.
Nonlinear processes upon two-photon interband picosecond excitation of PbWO{sub 4} crystal
A new experimental method is proposed to study the dynamics of nonlinear processes occurring upon two-photon interband picosecond excitation of a lead tungstate crystal and upon its excitation by cw probe radiation in a temporal range from several nanoseconds to several seconds. The method is applied to the case of crystal excitation by a sequence of 25 high-power picosecond pulses with a wavelength of 523.5 nm and 633-nm cw probe radiation. Measuring the probe beam transmittance during crystal excitation, one can investigate the influence of two-photon interband absorption and the thermal nonlinearity of the refractive index on the dynamics of nonlinear processes in a wide range of times (from several nanoseconds to several seconds). The time resolution of the measuring system makes it possible to distinguish fast and slow nonlinear processes of electronic or thermal nature, including the generation of a thermal lens and thermal diffusion. An alternative method is proposed to study the dynamics of induced absorption transformation and, therefore, the dynamics of the development of nonlinear rocesses upon degenerate two-photon excitation of the crystal in the absence of external probe radiation. (nonlinear-optical phenomena)
A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex
Using quantum algorithms to simulate complex physical processes and correlations in quantum matter has been a major direction of quantum computing research, towards the promise of a quantum advantage over classical approaches. In this work we develop a generalized quantum algorithm to simulate any dynamical process represented by either the operator sum representation or the Lindblad master equation. We then demonstrate the quantum algorithm by simulating the dynamics of the Fenna-Matthews-Olson (FMO) complex on the IBM QASM quantum simulator. This work represents a first demonstration of a quantum algorithm for open quantum dynamics with a moderately sophisticated dynamical process involving a realistic biological structure. We discuss the complexity of the quantum algorithm relative to the classical method for the same purpose, presenting a decisive query complexity advantage of the quantum approach based on the unique property of quantum measurement.
A dynamic intron retention program enriched in RNA processing genes regulates gene expression during terminal erythropoiesis
Differentiating erythroblasts execute a dynamic alternative splicing program shown here to include extensive and diverse intron retention (IR) events. Cluster analysis revealed hundreds of developmentallydynamic introns that exhibit increased IR in mature erythroblasts, and are enriched in functions related to RNA processing such as SF3B1 spliceosomal factor. Distinct, developmentally-stable IR clusters are enriched in metal-ion binding functions and include mitoferrin genes SLC25A37 and SLC25A28 that are critical for iron homeostasis. Some IR transcripts are abundant, e.g. comprising ~50% of highly-expressed SLC25A37 and SF3B1 transcripts in late erythroblasts, and thereby limiting functional mRNA levels. IR transcripts tested were predominantly nuclearlocalized. Splice site strength correlated with IR among stable but not dynamic intron clusters, indicating distinct regulation of dynamically-increased IR in late erythroblasts. Retained introns were preferentially associated with alternative exons with premature termination codons (PTCs). High IR was observed in disease-causing genes including SF3B1 and the RNA binding protein FUS. Comparative studies demonstrated that the intron retention program in erythroblasts shares features with other tissues but ultimately is unique to erythropoiesis. Finally, we conclude that IR is a multi-dimensional set of processes that post-transcriptionally regulate diverse gene groups during normal erythropoiesis, misregulation of which could be responsible for human disease.
A Coupled Plasma Dynamics and Gas Flow Model for Semiconductor Processing
A continuum modeling approach by self-consistently coupling plasma dynamics and gas flow will be presented for the analysis of high density plasma reactors. Experimental data shows that gas flow distribution affects the etch rate uniformity even at low pressures (6-20 mTorr) and flow rates (20-70 sccm). This study will investigate the effects of gas flow and gas energy on bulk plasma densities and temperatures using a continuum model. The model solves multidimensional equations of mass balance for neutrals and ions, gas momentum, separate energy equations for electrons and neutrals and Maxwell's equations for power coupling. A test case of N2 plasma in a 300mm TCP etch reactor, for which hybrid model and Langmuir probe data are available, is chosen for this analysis. Our preliminary results show that modeling gas flow and energy improves the predictions of electron density and its spatial variation in the reactor when compared with the experimental data. The aim of this study is to identify the operating conditions for the TCP reactor when a self-consistent modeling of gas flow is important.
WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes
Computational fluid dynamics (CFD)-based wear predictions are computationally expensive to evaluate, even with a high-performance computing infrastructure. Thus, it is difficult to provide accurate local wear predictions in a timely manner. Data-driven approaches provide a more computationally efficient way to approximate the CFD wear predictions without running the actual CFD wear models. In this paper, a machine learning (ML) approach, termed WearGP, is presented to approximate the 3D local wear predictions, using numerical wear predictions from steady-state CFD simulations as training and testing datasets. The proposed framework is built on Gaussian process (GP) and utilized to predict wear in a much shorter time. The WearGP framework can be segmented into three stages. At the first stage, the training dataset is built by using a number of CFD simulations in the order of O (10 2 ). At the second stage, the data cleansing and data mining process are performed, where the nodal wear solutions are extracted from the solution database to build a training dataset. At the third stage, the wear predictions are made, using trained GP processes. Two CFD case studies including 3D slurry pump impeller and casing are used to demonstrate the WearGP framework, in which 144 training and 40 testing data points are used to train and test the proposed method, respectively. The numerical accuracy, computational effiency and effectiveness between the WearGP framework and CFD wear model for both slurry pump impellers and casings are compared. It is shown that the WearGP framework can achieve highly accurate results that are comparable with the CFD results, with a relatively small size training dataset, with a computational time reduction in an order of 10 5 to 10 6 .
Quantum process identification: a method for characterizing non-markovian quantum dynamics
Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system's environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.
Suppression of Void Nucleation in High-Purity Aluminum via Dynamic Recrystallization
The process of ductile fracture in metals often begins with void nucleation at second-phase particles and inclusions. Previous studies of rupture in high-purity face-centered-cubic metals, primarily aluminum (Al), concluded that second-phase particles are necessary for cavitation. A recent study of tantalum (Ta), a body-centered-cubic metal, demonstrated that voids nucleate readily at deformation-induced dislocation boundaries. These same features form in Al during plastic deformation. This study investigates why void nucleation was not previously observed at dislocation boundaries in Al. Here, we demonstrate that void nucleation is impeded in Al by room-temperature dynamic recrystallization (DRX), which erases these boundaries before voids can nucleate at them. If dislocation cells reform after DRX and before specimen separation by necking, voids nucleation is observed. These results indicate that dislocation substructures likely plays an important role in ductile rupture.