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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Challenges Ahead for Multimessenger Analyses of Gravitational Waves and Kilonova: A Case Study on GW190425

In recent years, there have been significant advances in multimessenger astronomy due to the discovery of the first, and so far only confirmed, gravitational wave event with a simultaneous electromagnetic (EM) counterpart, as well as improvements in numerical simulations, gravitational wave (GW) detectors, and transient astronomy. This has led to the exciting possibility of performing joint analyses of the GW and EM data, providing additional constraints on fundamental properties of the binary progenitor and merger remnant. Here, in this work, we present a new Bayesian framework that allows inference of these properties, while taking into account the systematic modeling uncertainties that arise when mapping from GW binary progenitor properties to photometric light curves. We extend the relative binning method presented in Zackay et al. to include extrinsic GW parameters for fast analysis of the GW signal. The focus of our EM framework is on light curves arising from r-process nucleosynthesis in the ejected material during and after merger, the so-called kilonova, and particularly on black hole–neutron star systems. As a case study, we examine the recent detection of GW190425, where the primary object is consistent with being either a black hole or a neutron star. We show quantitatively how improved mapping between binary progenitor and outflow properties, and/or an increase in EM data quantity and quality are required in order to break degeneracies in the fundamental source parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Development of a Gibbs Energy Minimiser for the MOOSE-based Corrosion Modelling App Yellowjacket and Validation of MSTDB

Nuclear materials are highly complex multiscale, multiphysics systems,and an effective prediction of nuclear reactor performance and safety requires simulation capabilities that tightly couple different physical phenomena. The Idaho National Laboratory’s Multiphysics Object Oriented Simulation Environment (MOOSE) provides the computational foundation for performing such simulations. With the move towards advanced reactors, such as the Molten Salt Reactor (MSR), that employ high temperature fluids compared to conventional reactors, corrosion has become a problem of great interest. A new application called Yellowjacket is currently under development to directly couple thermodynamic equilibrium and kinetics with phase field models in order to model corrosion in MSRs. As part of Yellowjacket, a Gibbs energy minimiser is being developed to perform thermochemical equilibrium calculations for a range of different materials, which is currently in its infancy. This report describes the further progress towards the development of Yellowjacket Gibbs energy minimiser. Ontario Tech University is developing a new Gibbs energy minimiser for Yellowjacket which is the primary contribution of this work. The aim to develop a thermochemistry solver for the MOOSE framework following the same development philosophy and using the same tools and libraries. A special focus is on performance, documentation and SQA. Furthermore through a scope extension partway through the fiscal year a thorough assessment of the MSTDB-TC v1.3 was performed at Ontario Tech University in the context of continuous improvement and quality assurance. The objective of the work was to have an arm’s length review of the database to give confidence that the database is performing as it was intended while assessing its current state to give recommendations to future developments. This assessment involved two parts: A) a quantitative assessment, and B) a qualitative assessment. Part A involved developing an automated test-suite that would compute values from the database using Thermochimica with comparisons to experimental measurements for validation purposes, which gives confidence to the database’s stakeholders that its functioning properly. Part B involved reviewing all binary systems in the database and making a qualitative assessment with two performance indicators: comprehensiveness and overall confidence. It is important to note that the models in the database are empirical, which is to say that the quality of any model is highly dependent on the experimental data used to inform its development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Closure models for the feedback of energetic particles on plasma turbulence

Energetic particles interact with the plasma surrounding them, resonating with certain types of plasma waves to stabilize them while destabilizing others, and changing the character of the background turbulence in ways that have not been fully quantified or understood. Interaction with the turbulent background plasma is key to the acceleration of many types of energetic particles including high-energy cosmic rays, solar energetic particles, and pick-up ions. The acceleration of particles is a process that would ideally be described by a kinetic model, a type of model that follows a probability distribution function (PDF) for all particles in 7-dimensional (x, y, z, v x , v y , v z , t) space. Because of the high dimensionality of a kinetic model, simulations that solve kinetic equations use the largest computational resources currently available, and are yet unable to simulate a realistic number of particles, reach the large scales necessary for astrophysical problems, and use high-precision numerical methods. Two available alternatives to kinetic plasma models have been explored for this problem, with limited success. One is a multi-fluid model produced by a cumulant discarding closure, which evolves coupled equations for the velocity, magnetic field, and internal energy for both the background plasma and the fluid of energetic particles. However, simulations that solve multi-fluid magnetohydrodynamic (MHD) equations are able to include the interaction with energetic particles only in crude ways, typically as an add-on pressure term. The second alternative is to use a hybrid method to couple a fluid description of the background plasma to a kinetic model or a Fokker–Planck model for the energetic particles. These methods are hampered by the physical modeling of the coupling. In this work, we develop a new model, which follows the PDF for all particles; this can be viewed as a step toward physical realism above a multi-fluid MHD model, while also being more computationally efficient than a kinetic model. The equations we develop model both the background plasma and the energetic particles self-consistently. Over the last decade, similar PDF methods have been developed to a high level of sophistication to model reactive flows and turbulent combustion for engineering applications. For treatment of the feedback of the energetic particles on a background plasma, a PDF closure approach should evaluate the mean characteristics, including the density, with better statistical quality than will particle-sampling procedures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Rapid Compression Machine Study of 2-Phenylethanol Autoignition at Low-To-Intermediate Temperatures

To meet the increasing anti-knocking quality demand of boosted spark-ignition engines, fuel additives are considered an effective approach to tailor fuel properties for satisfying the performance requirements. Thus, screening/developing bio-derived fuel additives that are best-suited for advanced spark-ignition engines has become a significant task. 2-Phenylethanol (2-PE) is an attractive candidate that features high research octane number, high octane sensitivity, low vapor pressure, and high energy density. Recognizing that the low temperature autoignition chemistry of 2-PE is not well understood and the need for fundamental experimental data at engine-relevant conditions, rapid compression machine (RCM) experiments are therefore conducted herein to measure ignition delay times (IDTs) of 2-PE in air over a wide range of conditions to fill this fundamental void. These newly acquired IDT data at low-to-intermediated temperatures, equivalence ratios of 0.35–1.5, and compressed pressures of 10–40 bar are then used to validate the 2-PE model developed by Shankar et al. (2017). It is found that this literature model greatly overpredicts the current RCM data. The comparison of experimental and simulated results also provides insights into 2-PE autoignition behaviors at varying conditions. Further chemical kinetic analyses demonstrate that the absence of the O2-addition pathway of β-R. radical in the 2-PE model of Shankar et al. (2017) could account for the model discrepancies observed at low-to-intermediated temperatures.

Fang, Ruozhou (ORCID:0000000348315100)↗
DOE OSTI2023

Origin of surface and subband states at the InAs(111)A surface

The atomic structure of surfaces and interfaces plays a vital role in the electronic quality and properties of quantum devices. The interplay between the surface and confined bulk subband states in terms of their susceptibility has been investigated in relation to crystal defects on an InAs(111)A-(2×2) reconstructed surface, using low-temperature scanning tunneling microscopy and spectroscopy. We measure the two-dimensional quantized subband states arising from the confined potential imposed by downward bending of the conduction band edge. Furthermore, we show evidence of the existence of surface Bloch states within the confined bulk band gap projected on the surface spectrum which have originated from the surface reconstruction. As expected, larger confined bulk band gaps at the surface and conduction band offset are measured to be 0.58 and 0.31 eV, respectively. Here we further show the scattering of these quantum states at different surface defects and demonstrate that surface states are more susceptible to the defect potential when compared with the corresponding subband states. This apparent contrast follows from the length scale at which these defect potentials actively interact on or near the surface. Our observed experimental results are supported by empirical tight-binding simulations for the subband states and first-principles density functional theory simulations for the surface states present on the surface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

E-PVT: enhanced position-velocity-time scheduler for computer-controlled optical finishing with comprehensive considerations of dynamics constraints, continuity and efficiency

Deterministic computer-controlled optical finishing is an essential approach for achieving high-quality optical surfaces. Its determinism and convergence rely heavily on precise and smooth motion control to guide the machine tool over an optical surface to correct residual errors. One widely supported and smooth motion control model is position-velocity-time (PVT), which employs piecewise cubic polynomials to describe positions. Our prior research introduced a PVT-based velocity scheduling method, demonstrating sub-nanometer level convergence in ion beam figuring (IBF) processes. However, three challenges remained. Firstly, this method relies on quadratic programming, resulting in computational intensiveness for dense tool paths. Secondly, the dynamics constraints and velocity and acceleration continuities are not comprehensively considered, limiting the full potential of PVT-based control. Thirdly, no compensation mechanism existed when dynamics constraints are exceeded. In this study, in response to these challenges, we proposed the Enhanced PVT (E-PVT) method, reducing the time complexity from O ( n 3 ) to O ( n ) while fully addressing dynamics constraints and continuities. A novel compensation method utilizing particle swarm optimization was proposed to address situations where dynamics constraints might be exceeded while maintaining the overall processing efficiency. Validation through simulation and experimentation confirmed the improved performance of E-PVT.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-Task with Procter and Gamble (CRADA No. NFE-10-02672)

The purpose of this Cooperative Research and Development Agreement (CRADA) between UT-Battelle, LLC (the “Contractor) and Procter & Gamble Company (the “Participant”) is the development of a research partnership to create new tools, tests and analytical methods to improve the performance, safety and/or environmental quality of chemicals, advanced materials, food products and manufacturing processes. The Participant operates in three global business units: Beauty, Health and Well-Being and Household Care. Some of its worldwide products include Head and Shoulders®, Pantene®, Gillette® razors and personal care products, Crest®, Dawn®, Tide®, Bounty®, Duracell® batteries; and Iams® pet food among others. At its core, however, the Participant is a science driven company. It supports one of the most robust industrial research and development (R&D) programs in the world. The Participant uses this rich foundation of science to drive innovation across all of its product lines. But the innovation process is not confined in-house The Participant pursues an “open innovation” policy, seeking partnerships with scientists and researchers in universities and national laboratories where it can contribute its extensive knowledge assets and collaborate to advance scientific understanding. The research under this multi-task CRADA was directed under the following general task areas and, throughout the duration of this CRADA the work statement was modified to match the needs of the Parties and the direction of the research. (1) Software modeling, simulation and development; (2) Manufacturing Technologies; (3) Supply Chain Optimization, (4) Advanced Materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Joint Analysis of Small-scale Galaxy Clustering and Galaxy–Galaxy Lensing from BOSS Galaxies

We present a joint analysis of galaxy clustering and galaxy–galaxy lensing measurements from BOSS galaxies using a simulation-based emulation method combined with a halo occupation distribution model. Our emulators are constructed with the Aemulus ν simulations, a suite of wνCDM N-body simulations with massive neutrinos as independent particle species. We combine small-scale analysis of clustering from 0.1 to 60.2 h −1 Mpc and lensing from 1.7 to 60.2 h −1 Mpc to perform cosmological constraints. We split the BOSS galaxies into three redshift bins to measure their clustering and employ galaxies from Dark Energy Camera Legacy Survey and Hyper Suprime-Cam as source galaxies to measure lensing separately. We find that the addition of lensing significantly improves the constraining power on $S_8 = σ_8(Ω_m/0.3)^{0.5}$, with a weak improvement for fσ 8 . Our results of fσ 8 indicate tensions of around 1σ−4σ below the results of the cosmic microwave background observations of Planck. For S 8 , our results are also lower than Planck, and the tension can be mitigated when considering possible systematics in lensing measurement. As a by-product, our analysis prefers a nonzero neutrino mass but without strong significance, with the constraining power dominated by the clustering. Given the accuracy and precision of our model and the observational data, it is anticipated that larger and higher-quality spectroscopic data sets will improve the constraints on this fundamental property in the near future.

Gao, Wenhao [Shanghai Jiao Tong University (China)↗
DOE OSTI2023

Towards a wavefront-preservation X-ray crystal monochromator for high-repetition-rate FELs

The wavefront preservation of coherent X-ray free-electron laser beams is pushing the requirement on the quality and performance of X-ray optics to an unprecedented level. The Strehl ratio can be used to quantify this requirement. In this paper, the criteria for thermal deformation of the X-ray optics are formulated, especially for crystal monochromators. To preserve the X-ray wavefront, the standard deviation of the height error should be sub-nm for mirrors and less than 25 pm for crystal monochromators. Cryocooled silicon crystals combined with two techniques can be used to achieve this level of performance for monochromator crystals: (1) using a focusing element to compensate the second-order component of the thermal deformation; (2) introducing a cooling pad between the cooling block and silicon crystal and optimizing the effective cooling temperature. Each of these techniques allows the thermal deformation in standard deviation of the height error to be reduced by an order of magnitude. As an example, for the LCLS-II-HE Dynamic X-ray Scattering instrument, the criteria on thermal deformation of a high-heat-load monochromator crystal can be achieved for a 100 W SASE FEL beam. Wavefront propagation simulations confirm that the reflected beam intensity profile is satisfactory on both the peak power density and focused beam size.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

PWR loading pattern optimization with reinforcement learning

The core loading pattern optimization problem belongs to the class of combinatorial optimization problem and has been studied since the dawn of commercial nuclear energy industry. It is characterized by multiple objectives and constraints, with a very high number of candidate patterns, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors to perform fuel cycle reload design. Nevertheless, hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art core reload patterns, we aim to create a method as scalable as possible, that agrees with the designer's goal of performance and safety. To help in this task Deep Reinforcement Learning (DRL), in particular Proximal Policy Optimization is leveraged. DRL has recently experienced a strong impetus from its successes applied to games, sometimes even reaching 'super-human' performances. This paper lays out the foundation of this method and proposes to study the behavior of several hyper-parameters that influence the DRL algorithm. The algorithm is highly dependent on multiple factors such as an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen and the number of samples collected before a policy update, but also the shape of the objective function derived for the core design. Experimental results also demonstrate the effectiveness of the method in finding high-quality solutions from scratch within a reasonable amount of time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Coupled wave-current modeling for hydrodynamic load analysis of macroalgae cultivation farms

Large-scale cultivation of macroalgae is one of the most promising biofuel sources that could reduce our consumption of fossil fuels and would be particularly economically competitive when grown for the co-production of additional goods such as food and textiles. Current production of biofuel from algal biomass is limited by labor costs and lack of data on potential impacts of cultivation and harvesting on marine and coastal environments as well as a comprehensive characterization of the impact of environmental conditions on macroalgal feedstock and their bioproduct and biofuel yield and quality. Awarded by the United States (U.S.) Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program, which seeks to enable the U.S. as a global leader in the production of marine biomass, the Marine Biological Laboratory (MBL) has been developing a test system for tropical seaweed cultivation in the Gulf of Mexico and the Caribbean. An integral step in designing macroalgae cultivation farms is to adequately characterize the hydrodynamic climate at potential growth sites. In regions like the Gulf of Mexico and the Caribbean, which are highly exposed to tropical cyclones, it is critical to not only consider the day-to-day hydrodynamic conditions but to also assess the risk of extreme sea states to ensure the survivability of future macroalgae farms. Under these considerations, the Pacific Northwest National Laboratory (PNNL) is providing modeling support for MBL to inform the design and siting of the farm systems that have been proposed for their ARPA-E MARINER project. This report describes the development of a high-resolution coupled storm surge and wave model to simulate the hydrodynamics and wave climate at proposed macroalgae cultivation sites selected by MBL in Florida and Puerto Rico. Model results, including model validation, water level, current distributions, and sea states, are discussed for both selected sites in Florida and Puerto Rico coast. These simulations provide an accurate insight into the hydrodynamic conditions that a macroalgae farm is likely to experience during its operational lifetime, including current information to support fine-scale hydrodynamic load modeling, risk analysis and system design.

09 BIOMASS FUELS↗
DOE OSTI2024

A general Bayesian algorithm for the autonomous alignment of beamlines

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Computationally-Aided Design of a Small-Scale Radioactive Waste Glass Melter

To provide mission support to the Hanford Waste Immobilization and Treatment Plant for the vitrification of legacy nuclear tank waste, a reduced-scale vitrification pilot system is being designed to process simulated and actual radioactive tank wastes. The tank waste will be separated into high-level waste (HLW) and low-activity waste (LAW) fractions, where the majority by mass (~90%) is LAW and by activity (>95%) is HLW. The first tank waste to be processed at the WTP will be LAW. Since Hanford tank waste and the resultant melter feed compositions are known to vary widely, pilot-scale operations are essential to identify potential problems, provide needed data, confirm assumptions, and determine impacts to full-scale melters and off-gas systems from these different feeds. In addition, the reduced-scale melter system must be capable of quickly providing results to operations without the typically high costs of radioactive operations and provide an engineering platform in close proximity to local operations staff. The objective is to produce similar process conditions to those encountered in the full-scale LAW melters and minimize the volume of radioactive waste necessary for the evaluations. To this end, melter design features are being explored to increase production without significantly increasing surface area, melter glass volumes or compromising data quality. In support of these objectives, a set of computational fluid dynamic models have been developed to provide insight into the flow patterns within these small melters and evaluate design features to increase production without significantly increasing glass inventory. Computed velocities were evaluated at the interface between the cold cap and the molten glass pool and within the bulk glass pool. A method was developed to correlate results to an estimated melt rate and down-select design features that produce the highest gains.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Computationally-Aided Design of a Small-Scale Radioactive Waste Glass Melter

To provide mission support to the Hanford Waste Immobilization and Treatment Plant for the vitrification of legacy nuclear tank waste, a reduced-scale vitrification pilot system is being designed to process simulated and actual radioactive tank wastes. The tank waste will be separated into high-level waste (HLW) and low-activity waste (LAW) fractions, where the majority by mass (~90%) is LAW and by activity (>95%) is HLW. The first tank waste to be processed at the WTP will be LAW. Since Hanford tank waste and the resultant melter feed compositions are known to vary widely, pilot-scale operations are essential to identify potential problems, provide needed data, confirm assumptions, and determine impacts to full-scale melters and off-gas systems from these different feeds. In addition, the reduced-scale melter system must be capable of quickly providing results to operations without the typically high costs of radioactive operations and provide an engineering platform in close proximity to local operations staff. The objective is to produce similar process conditions to those encountered in the full-scale LAW melters and minimize the volume of radioactive waste necessary for the evaluations. To this end, melter design features are being explored to increase production without significantly increasing surface area, melter glass volumes or compromising data quality. In support of these objectives, a set of computational fluid dynamic models have been developed to provide insight into the flow patterns within these small melters and evaluate design features to increase production without significantly increasing glass inventory. Computed velocities were evaluated at the interface between the cold cap and the molten glass pool and within the bulk glass pool. A method was developed to correlate results to an estimated melt rate and down-select design features that produce the highest gains.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗
DOE OSTI2022

Real-time equation-of-motion CC cumulant and CC Green’s function simulations of photoemission spectra of water and water dimer

Newly developed coupled-cluster (CC) methods enable simulations of ionization potentials and spectral functions of molecular systems in a wide range of energy scales ranging from core-binding to valence. This paper discusses the results obtained with the real-time equation-of-motion CC cumulant (RT-EOM-CC) approach and CC Green’s function (CCGF) approaches in applications to the water and water dimer molecules. Here, we compare the ionization potentials obtained with these methods for the valence region with the results obtained with the coupled-cluster with singles, doubles, and perturbative triples formulation as a difference of energies for N and N – 1 electron systems. All methods show good agreement with each other. They also agree well with the experiment with errors usually below 0.1 eV for the ionization potentials. We also analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods employing single and double excitations, as a function of the monomer OH bond length and the proton transfer coordinate in the dimer. Finally, we analyze the impact of the basis set effects on the quality of calculated ionization potentials and find that the basis set effects are less pronounced for the augmented-type sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Investigation of Solid Particle Reactors for Nonoxidative Dehydrogenation of Ethane: Toward Solar Thermal Ethylene Production

Concentrating solar power plants can generate renewable heat at temperatures well above those of most industrial processes. Ceramic particles irradiated with concentrated sunlight can store high-quality sensible heat and transfer this to power generation systems. These concepts and materials hold great potential to also enable thermal processes in the chemical industry, but effective strategies for transferring heat from thermal energy storage media into chemical reactors are still under development. This present work evaluated the thermal and chemical compatibility of various solid particle media (including quartz, bauxite, and alumina particles) integrated directly into tube reactors and the subsequent effects on reactor performance for the nonoxidative dehydrogenation of ethane reaction. Empty tube reactors without loaded particles (representing conventional ethane cracking coils) showed significant heat transfer limitations as the tube diameter was scaled. The incorporation of media into the reactor significantly aided heat transfer to the gaseous ethane reactant and increased its conversion by as much as 10% at similar space velocities. Despite direct contact with hydrocarbon gases, alumina and quartz media showed negligible coke formation. Even during reaction in 100% ethane feed gas at 825 °C, the average selectivity of the coke product was only 0.57% when using the quartz media. These materials further demonstrated excellent thermal stability during subsequent reoxidation in air at 800 °C, which simulated the reheating of particles in a circulating particle solar receiver. Conversely, high rates of coke formation, with a product selectivity of 27.5%, were observed on sintered bauxite particles during the reaction, likely promoted by transition metal constituents. These particles fractured upon reoxidation due to exotherms generated from coke combustion. In conclusion, while the use of cofed steam could mitigate attrition of redox-active particles, the ability of inert metal oxide particles to efficiently transfer heat to concentrated ethane reactant gas while suppressing side reactions or degradation suggests that these media could effectively couple solar thermal plants to reactors for next-generation production of ethylene and other critical chemicals.

Hydrocarbons↗
DOE OSTI2021

Cell-Edge Detection via Selective Cooperation and Generalized Canonical Correlation

Improving the uplink quality of service for users located around the boundaries between cells is a key challenge in cellular systems. Existing approaches relying on power control throttle the rates of cell-center users, while multi-user detection requires accurate channel estimates for the cell-edge users, which is another challenge due to their low received signal-to-noise ratio (SNR). Utilizing the fact that cell-edge user signals are weak but common (received at roughly equal power) at different base stations (BSs), this paper establishes a connection between cell-edge user detection and generalized canonical correlation analysis (GCCA). It puts forth a GCCA-based method that leverages selective BS cooperation to recover the cell-edge user signal subspace even at low SNR. The cell-edge user signals can then be extracted from the resulting mixture via algebraic signal processing techniques. The paper includes theoretical analysis showing why GCCA recovers the correct subspace containing the cell-edge user signals under mild conditions. The proposed method can also identify the number of cell-edge users in the system, i.e., the common subspace dimension. Simulations reveal significant performance improvement relative to various multiuser detection techniques. Cell-edge detection performance is further studied as a function of how many / which BSs are selected, and it is shown that using the closest three BS is always the best choice.

base station cooperation↗