Turbulent Channel Flow with Spatially-Dependent Viscosity: A Numerical Study

In the present study, we examine in detail the effect of spatially dependent viscosity on wall-bounded flow. For this purpose, Direct Numerical Simulations (DNS) are performed considering a channel flow with a viscosity change along the streamwise direction. The DNS were performed using Nek5000, a computational fluid dynamic code developed at Argonne National Laboratory. The channel is divided in three different regions: in the first one, the flow is at a constant Reynolds number of Re = 5000; in the second region, the Reynolds number is imposed to linearly increase as viscosity decreases through a ramp; finally, in the third region the flow is again at a constant Reynolds number, this time at Re = 10000. Since the temperature field is not evaluated, the proposed set up is a simplification of a heated channel. Nevertheless, the outcomes of this study may be valuable for future works considering variable-viscosity effects, especially for cooling and heating applications. Four test cases with different ramp inclinations were analyzed. The results from the present study were compared with a correlation available in the literature for the friction Reynolds number as a function of the Reynolds number. We observe that in all cases the ramp does not cause an immediate change in the characteristics of turbulent structures and a delay is in fact observed in both wall shear and friction. Finally, in order to characterize and understand these effects, streaks from the viscous region and turbulence statistics for the turbulent kinetic energy budget terms are analyzed.

Use of X-Ray Fluorescence Microscopy for Studies on Research Models of Hepatocellular Carcinoma

Introduction: TheraSphere® microspheres containing yttrium 90Y are among many radioembolization agents used clinically to reduce liver tumor burden, and their effects on cancer volume reduction are well-established. At the same time, concerns about off target tissue injury often limit their use. Deeper investigation into tissue distribution and long-term impact of these microspheres could inform us about additional ways to use them in practice.Methods: Healthy rat liver and rabbit liver tumor samples from animals treated with TheraSpheres were sectioned and their elemental maps were generated by X-ray fluorescence microscopy (XFM) at the Advanced Photon Source (APS) synchrotron at Argonne National Laboratory (ANL).Results: Elemental imaging allowed us to identify the presence and distribution of TheraSpheres in animal tissues without the need for additional sample manipulation or staining. Ionizing radiation produced by 90Y radioactive contaminants present in these microspheres makes processing TheraSphere treated samples complex. Accumulation of microspheres in macrophages was observed.Conclusions: This is the first study that used XFM to evaluate the location of microspheres and radionuclides in animal liver and tumor samples introduced through radioembolization. XFM has shown promise in expanding our understanding of radioembolization and could be used for investigation of human patient samples in the future.

Electric Vehicle Battery Simulation: How Electrode Porosity and Thickness Impact Cost and Performance

Lithium-ion batteries almost exclusively power today’s electric vehicles (EVs). Cutting battery costs is crucial to the promotion of EVs. This paper aims to develop potential solutions to lower the cost and improve battery performance by investigating its design variables: positive electrode porosity and thickness. The open-access lithium-ion battery design and cost model (BatPac) from the Argonne National Laboratory of the United States Department of Energy, has been used for the analyses. Six pouch battery systems with different positive materials are compared in this study (LMO, LFP, NMC 532/LMO, NMC 622, NMC 811, and NCA). Despite their higher positive active material price, nickel-rich batteries (NMC 622, NMC 811, and NCA) present a cheaper total pack cost per kilowatt-hour than other batteries. The higher thickness and lower porosity can reduce the battery cost, enhance the specific energy, lower the battery mass but increase the performance instability. The reliability of the results in this study is proven by comparing estimated and actual commercial EV battery parameters. In addition to the positive electrode thickness and porosity, six other factors that affect the battery’s cost and performance have been discussed. They include energy storage, negative electrode porosity, separator thickness and porosity, and negative and positive current collector thickness.

A New RANS Correction to Account for Varying Viscosity Effects

It has previously been shown that by increasing the Reynolds number across a channel by spatially varying the viscosity does not cause an immediate change in the size of turbulent structures and a delay is in fact observed in both wall shear and friction Reynolds number (Coppo Leite, V, & Merzari, E., Proceedings of the ASME 2020 FEDSM, p. V003T05A019). Furthermore, it is also shown that depending on the length in which the flow condition changes, turbulence bursts are observed in the turbulence field. For the present work we propose a new version of the standard Reynolds Averaged Navier Stokes (RANS) k–τ model that includes some modifications in the production term in order to account for these effects. The new proposed model may be useful for many engineering applications as turbulent flows featuring temperature gradients and high heat transfer rates are often seen in heat exchangers, combustion chambers and nuclear reactors. In these applications, thermal and viscous properties of the working fluid are important design parameters that depend on temperature; hence it is likely to observe strong gradients on these scalars’ fields. To accomplish our goal, the modifications for the k–τ model are implemented and tested for a channel flow with spatial varying viscosity in the streamwise direction. The numerical simulations are performed using Nek5000, a spectral-element code developed at Argonne National Laboratory (ANL). Finally, the results considering a turbulence channel using the proposed model are compared against data obtained using Direct Numerical Simulations from the earlier work.

Large-scale dendritic spine extraction and analysis through petascale computing

The synapse is a central player in the nervous system serving as the key structure that permits the relay of electrical and chemical signals from one neuron to another. The anatomy of the synapse contains important information about the signals and the strength of signal it transmits. Because of their small size, however, electron microscopy (EM) is the only method capable of directly visualizing synapse morphology and remains the gold standard for studying synapse morphology. Historically, EM has been limited to small fields of view and often only in 2D, but recent advances in automated serial EM (i.e. connectomics) have enabled collecting large EM volumes that capture significant fractions of neurons and the different classes of synapses they receive (i.e. shaft, spine, soma, axon). However, even with recent advances in automatic segmentation methods, extracting neuronal and synaptic profiles from these connectomics datasets are difficult to scale over large EM volumes. Without methods that speed up automatic segmentation over large volumes, the full potential of utilizing these new EM methods to advance studies related to synapse morphologies will never be fully realized. To solve this problem, we describe our work to leverage Argonne leadership-scale supercomputers for segmentation of a 0.6 terabyte dataset using state of the art machine learning-based segmentation methods on a significant fraction of the 11.69 petaFLOPs supercomputer Theta at Argonne National Laboratory. We describe an iterative pipeline that couples human and machine feedback to produce accurate segmentation results in time frames that will make connectomics a more routine method for exploring how synapse biology changes across a number of biological conditions. Finally, we demonstrate how dendritic spines can be algorithmically extracted from the segmentation dataset for analysis of spine morphologies. Advancing this effort at large compute scale is expected to yield benefits in turnaround time for segmentation of individual datasets, accelerating the path to biology results and providing population-level insight into how thousands of synapses originate from different neurons; we expect to also reap benefits in terms of greater accuracy from the more compute-intensive algorithms these systems enable.

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

High-energy micrometre-scale pixel direct conversion X-ray detector

The objective of this work was to fabricate and characterize a new X-ray imaging detector with micrometre-scale pixel dimensions (7.8 µm) and high detection efficiency for hard X-ray energies above 20 keV. A key technology component consists of a monolithic hybrid detector built by direct deposition of an amorphous selenium film on a custom designed CMOS readout integrated circuit. Characterization was carried out at the synchrotron beamline 1-BM-B at the Advanced Photon Source of Argonne National Laboratory. The direct conversion detector demonstrated micrometre-scale spatial resolution with a 63 keV modulation transfer function of 10% at Nyquist frequency. In addition, spatial resolving power down to 8 µm was determined by imaging a transmission bar target at 21 keV. X-ray signal linearity, responsivity and lag were also characterized in the same energy range. Finally, phase contrast edge enhancement was observed in a phase object placed in the beam path. This amorphous selenium/CMOS detector technology can address gaps in commercially available X-ray detectors which limit their usefulness for existing synchrotron applications at energies greater than 50 keV; for example, phase contrast tomography and high-resolution imaging of nanoscale lattice distortions in bulk crystalline materials using Bragg coherent diffraction imaging. The technology will also facilitate the creation of novel synchrotron imaging applications for X-ray energies at or above 20 keV.

Analysis and Visualisation of Research Trends in Hydrogen Fuel: A General Review

Hydrogen fuel is one of the clean fuels that can replace non-renewable energy sources. Engineering innovations in extraction and distribution are the key challenges to this alternative fuel[1].The bibliometric analysis had been conducted to understand the active authors, organizations, journals, and countries involved in the research domain of “Hydrogen fuel”[2], [3]. All published articles related to “Hydrogen fuel” from “Scopus”, were analyzed using the VOS viewer to develop analysis tables and visualization maps.This article had set the objective to consolidate the scientific literature regarding the “Hydrogen fuel”and also to find out the trends related to the same.The most active journals in this research domain were identified as International Journal of Hydrogen Energyand Journal of Power Sources.The most active country was the United States of America. The leading organizations in this research domain were the Russian Academy of Sciences of Russia and Argonne National Laboratory of the United States of America.The most active authorswere Tomasov A.A and Jacobson M.Z.

Evaluating the Ecosystem Service Benefits of Native Vegetation Management at Solar Energy Facilities

<p>Concomitant with the increase in solar photovoltaic (PV) energy development over the past decade has been the increasing emphasis on land sharing strategies that maximize the land use efficiency of solar energy developments.  Many of these strategies focus on improving the compatibility of solar energy development with other co-located land uses (e.g., agriculture) and by improving several ecosystem services that could have natural, societal, and industrial benefits. One such land opportunity is the restoration and management of native grassland vegetation beneath ground-mounted PV solar energy facilities, which has the potential to restore native habitat to conserve biodiversity and restore previously altered ecosystem services (e.g., natural pollination services). This presentation will discuss various assessment and modeling approaches to evaluate the scale and magnitude of the ecosystem services provided by different vegetation management strategies at solar PV energy development sites. This work demonstrates how multifunctional land uses in energy systems represents a win-win solution for energy and the environment by optimizing energy-food-ecology synergies. This work was conducted by Argonne National Laboratory for the U.S. Department of Energy Solar Energy Technologies Office under Contract No. DE-AC02-06CH11357.</p>