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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Processing and mechanical characterization of short carbon fiber-reinforced epoxy composites for material extrusion additive manufacturing

Fiber-reinforced polymer composites have been extensively utilized in recent years as feedstock materials for material extrusion additive manufacturing (AM) processes to improve strength, stiffness, and functionality of printed parts over unfilled printed polymers. However, the widespread adoption of AM of fiber-reinforced polymer composites requires a deeper understanding of the process-structure-property relationships in printed components, and such relationships are not well understood yet. Fiber length is critically important to the mechanical performance of short fiber composites, but very few studies to-date have focused on how the fiber length distribution (FLD) evolves during processing of composite feedstocks and how this evolution affects printing behavior and mechanical properties in 3D-printed composites. Here, FLD is measured for carbon fiber reinforced epoxy composites over a wide range of ink compositions and shear mixing times, and the distributions are fit with a Weibull-type distribution function. The effects of FLD on the tradeoff between ink processability, ink rheology, printing behavior and mechanical properties are investigated. Furthermore, the effects of printing parameters (nozzle size and print speed) on mechanical anisotropy and fiber orientation distribution (FOD) in printed composites are explored. Mechanical properties of printed composites are characterized via 3 pt-flexural testing, and microstructure is investigated using optical and scanning electron microscopy (SEM), and x-ray computed tomography. Finally, the fitted Weibull parameters are fed into a composite model that incorporates FLD and FOD, and model predictions are found to be in excellent agreement with experimental observations.

3D printing↗
DOE OSTI2024

Why Do Oceanic Nonlinearities Contribute Only Weakly to Extreme El Niño Events?

Abstract Extreme El Niño events have outsized global impacts and control the El Niño Southern Oscillation (ENSO) warm/cold phases asymmetries. Yet, a consensus regarding the relative contributions of atmospheric and oceanic nonlinearities to their genesis remains elusive. Here, we isolate the contribution of oceanic nonlinearities by conducting paired experiments forced with opposite wind stress anomalies in an oceanic general circulation model, which realistically simulates extreme El Niño events and oceanic nonlinearities thought to contribute to ENSO skewness (Tropical Instability Waves (TIWs), Nonlinear Dynamical Heating (NDH)). Our findings indicate a weak contribution of oceanic nonlinearities to extreme El Niño events in the eastern Pacific, owing to compensatory effects between lateral (NDH and TIWs) and vertical processes. These results hold across different vertical mixing schemes and modifications of the upper‐ocean heat budget mixed layer criterion. Our study reinforces previous research underscoring the pivotal role of atmospheric nonlinearities in shaping extreme El Niño events.

Geology↗
DOE OSTI2021

Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (N CCN ) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy's Southern Great Plains (SGP) site to illustrate the dependence of N CCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (N accu ; median value 805 cm -3 ) and relatively high aerosol hygroscopicity (κ; median value ~0.25) values that result in the higher CCN activation and relatively high N CCN (median value 258–1578 cm -3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher N accu (~35%), and N CCN (230–1721 cm -3 at a SS of 0.1%–1.0%) with the lowest κ (~0.17). In contrast, relatively high nucleation mode particles (N nucl ; ~20%) and low N CCN (128–1553 cm -3 at a SS of 0.1%–1.0%) with higher κ (~0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better N CCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the N CCN prediction uncertainty at lower SS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Lifetime measurements of states of S 35 , S 36 , S 37 , and S 38 using the AGATA γ -ray tracking spectrometer

Lifetimes or lifetime limits of a small number of excited states of the sulfur isotopes with mass numbers A = 35 , 36, 37, and 38 have been measured using the differential recoil-distance method. The isotopes of sulfur were populated in binary grazing reactions initiated by a beam of 36 S ions of energy 225 MeV incident on a thin 208 Pb target which was mounted in the Cologne plunger apparatus. The combination of the PRISMA magnetic spectrometer and an early implementation of the AGATA γ-ray tracking array was used to detect γ rays in coincidence with projectile-like nuclear species. Lifetime measurements of populated states were measured within the range from about 1 to 100 ps. The number of states for which lifetime measurements or lifetime limits were possible was limited by statistics. For 35 S, the lifetime was determined for the first 1/2 + state at 1572 keV; the result is compared with a previous published lifetime value. The lifetime of the 3 - state of 36 S at 4193 keV was determined and compared with earlier measurements. No previous lifetime information exists for the (6 + ) state at 6690 keV; a lifetime measurement with large associated error was made in the present work. For 37 S, the states for which lifetime limits were established were those at 646 keV with J π = 3/2 - and at 2776 keV with J π = 11/2 - ; there are no previously published lifetime values for excited states of 37 S. Finally, a lifetime limit was established for the J π =(6 + ) state of 38 S at 3675 keV; no lifetime information exists for this state in the literature. Measured lifetime values were compared with the results of state-of-the-art shell-model calculations based on the PSDPF, SDPF-U, and FSU effective interactions. In addition, nuclear magnetic-dipole and electric-quadrupole moments, branching ratios, mixing ratios, and electromagnetic transition rates, where available, have been compared with shell-model values. The current work suffers from poor statistics; nevertheless, lifetime values and limits have been possible, allowing a useful discussion of the ability of state-of-the-art shell-model calculations to reproduce the experimental results.

electromagnetic transitions↗
DOE OSTI2020

Bond Synergy Model for Bond Energies in Alloy Oxides

In this work we introduce a metal-oxide bond-energy model for alloy oxides based on pure-phase bond energies and bond synergy factors that describe the effect of alloying on the bond energy between cations and oxygen, an important quantity to understand the formation of alloy oxides and their composition. This model is parameterized for binary cation-alloy oxides using density-functional theory energies and is shown to be directly transferable to multi-component alloy oxides. We parameterized the model for alloy oxide energies with metal cations that form the basis of corrosion resistant alloys, including Fe, Ni, Cr, Mo, Mn, W, Co, and Ru. We find that isoelectronic solutes allow quantification of pure-phase bond energies in oxides and the calculated bond energy values give sensible results compared to common experience, including the role of Cr as the passive-layer former in Fe–Ni–Cr alloys for corrosion applications. Additionally, the bond synergy factors give insights into the mutual strengthening and weakening effects of alloying on cation-oxygen bonds and can be related to enthalpy of mixing and charge neutrality constraints. We demonstrate how charge neutrality can be identified and achieved by the oxidation states that the different cations assume depending on alloy composition and the presence of defects.

Electrochemistry↗
DOE OSTI2021

Degradation of Platinum-Cobalt Alloy PEMFC Cathode Catalysts in Catalyst-Ionomer Inks

In this paper we report on studies of the effects of catalyst-ionomer ink composition: ionomer equivalent weight (EW), ink solvent, and ink mixing on a commercial PtCo alloy PEMFC cathode catalyst. X-ray absorption spectroscopy (XAS) and wide-angle X-ray scattering (WAXS) are utilized to determine catalyst atomic structure, catalyst crystallite composition, and extent of loss of Co into the ionomer-solvent phase. Three different n-propanol to water weight ratios (7:3, 5:5, and 3:7) and two different ionomers (3 M 800 EW and Nafion 1000 EW) were studied. Cobalt loss from the PtCo particles was found to increase with increasing water content in the inks and to be more extensive for the 800 EW ionomer inks, causing loss of contraction of the PtCo lattice and formation of a Pt shell-PtCo alloy core intraparticle structure.

25 ENERGY STORAGE↗
DOE OSTI2024

Tuning Catalytically Active Single Sites in Nonstoichiometric, Mixed Metal Oxides for Oxygen Electrocatalysis (Final Technical Report)

The objective of the proposed work is to employ controlled synthesis, advanced characterization, detailed electrochemical testing and theoretical calculations to develop a framework that would guide the design of robust, non-stoichiometric mixed metal oxides for oxygen electrocatalysis. In this research plan, we focus around the idea of tuning the metal ion composition and environment to create single atom centers with the utmost electrocatalytic activity. We hypothesized that tuning the cationic sites in nonstoichiometric mixed metal oxides will lead to single 4d/5d metal surface sites with optimal catalytic activity for low temperature oxygen reduction (ORR) and oxygen evolution (OER) at solid/liquid interfaces. We will focus on different crystal structures of these oxides including Ruddlesden-Popper (R-P) oxides, simple perovskites and pyrochlores due to their flexibility in accommodating different metal cation dopants, and the fact that they represent variations in the cationic arrangements in non-stoichiometric mixed metal oxide structures, which will lead to an understanding of how bulk crystal structure effects catalytic activity and stability of these systems.

08 HYDROGEN↗
DOE OSTI2020

Boron-Based Neutron Scintillator Screens for Neutron Imaging

In digital neutron imaging, the neutron scintillator screen is a limiting factor of spatial resolution and neutron capture efficiency and must be improved to enhance the capabilities of digital neutron imaging systems. Commonly used neutron scintillators are based on 6 LiF and gadolinium oxysulfide neutron converters. This work explores boron-based neutron scintillators because 10 B has a neutron absorption cross-section four times greater than 6Li, less energetic daughter products than Gd and 6 Li, and lower γ-ray sensitivity than Gd. These factors all suggest that, although borated neutron scintillators may not produce as much light as 6 Li-based screens, they may offer improved neutron statistics and spatial resolution. This work conducts a parametric study to determine the effects of various boron neutron converters, scintillator and converter particle sizes, converter-to-scintillator mix ratio, substrate materials, and sensor construction on image quality. The best performing boron-based scintillator screens demonstrated an improvement in neutron detection efficiency when compared with a common 6 LiF/ZnS scintillator, with a 125% increase in thermal neutron detection efficiency and 67% increase in epithermal neutron detection efficiency. The spatial resolution of high-resolution borated scintillators was measured, and the neutron tomography of a test object was successfully performed using some of the boron-based screens that exhibited the highest spatial resolution. For some applications, boron-based scintillators can be utilized to increase the performance of a digital neutron imaging system by reducing acquisition times and improving neutron statistics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Evidence for Evolved Stellar Binary Mergers in Observed B-type Blue Supergiants

Blue supergiants are the brightest stars in their host galaxies, and yet their evolutionary status has been a long-standing problem in stellar astrophysics. In this pioneering work, we present a large sample of 59 early B-type supergiants in the Large Magellanic Cloud with newly derived stellar parameters and identify the signatures of stars born from binary mergers among them. We simulate novel 1D merger models of binaries consisting of post main-sequence giants with helium-rich cores (primaries) and main-sequence companions (secondaries), and consider the effects of interaction of the secondary with the core of the primary along with the mixing induced by the merger in the envelope. Thereafter, the evolution of the newborn 17–43 M ⊙ stars is followed until core-carbon depletion, close to their final pre-explosion stage. Unlike stars born alone with comparable masses, stars born from mergers of evolved binaries are blue throughout their core helium-burning phase and replicate the surface gravities and Hertzsprung–Russell diagram positions of most of our sample, thus indicating that B-type supergiants structurally resemble stars born from such mergers. Moreover, the large nitrogen-to-carbon and nitrogen-to-oxygen number ratios, coupled with helium enhancements exhibited by at least half our data sample, is uniquely reproduced by our merger models. Collectively, these findings provide compelling evidence toward the important role of binary mergers in producing the currently observed population of blue supergiants in our Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High methylation potential of mercury complexed with mixed thiolate ligands by Geobacter sulfurreducens PCA

We report some thiols, such as cysteine (CYS) at moderate concentrations (10–500 µM), can enhance methylmercury (MeHg) formation by Geobacter sulfurreducens PCA, whereas others such as dithiol 2,3-dimercaptopropanesulfonate (DMPS) and 2,3-dimercaptosuccinic acid (DMSA) abolish mercury [Hg(II)] methylation. Little is known, however, about whether Hg(II) methylation could be enhanced or inhibited by the presence of mixed thiol ligands at low concentrations observed in the environment. Surprisingly we found that mixing CYS (1 µM) with DMPS (0.025–0.5 µM) or DMSA (0.025–1 µM) substantially increased MeHg production by 1.5–3.5-fold, compared to the no-thiol control, whereas complexation with a single DMPS, or DMSA, or CYS (1 µM) strongly inhibited Hg(II) methylation. Pre-equilibration between Hg(II) and thiols before the addition of cells was necessary to observe enhanced methylation. Spectroscopic analyses indicated the formation of mixed or heteroleptic coordinated Hg(II)-S 3 /S 4 complexes, which likely facilitated exchange of Hg(II) with cells and its uptake and internal transfer to the HgcAB proteins required for methylation. These results suggest that the effects of thiols on Hg(II) methylation were more complex than previously thought (using a single thiol) and thus underscore the importance of understanding how mixed thiols and their interactions with Hg(II) may ultimately influence MeHg production in the natural environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Willingness to Receive mHealth Services Among Patients with Diabetes on Chronic Follow-up in Public Hospitals in Eastern Ethiopia: Multicenter Mixed-Method Study

Background: Management of diabetes requires a long-term care strategy, including support for adherence to a healthy lifestyle and treatment. Exploring the willingness of patients with diabetes to receive mHealth services is essential for designing efficient and effective services. This study aimedto determine willingness to receive mHealth services and associated factors, as well as explore the barriers to receive mHealth services among patients with diabetes. Methods: A multicenter mixed-method study was employed from September 1 to November 30, 2022. For the quantitative part, a total of 365 patients with diabetes receiving chronic follow-up at three public hospitals were enrolled. Data were gathered using structured questionnaires administered by interviewers, entered into Epi-data version 4.6, and analyzed using Stata version 17. A binary and multivariable logistic regression model was computed to identify the associated factors. For qualitative, eight key informants and seven in-depth interviews were conducted. After verbatim transcription and translation, the data were thematically analyzed using ATLAS.ti V. 7.5. Results: Overall, 77.3% had access to a mobile phone, and 74.5% of them were willing to receive mHealth services. Higher odds of willingness to receive mHealth services were reported among patients with an age below 35 years [AOR = 4.11 (1.15– 14.71)], attended formal education [AOR = 2.63 (1.19– 5.77)], without comorbidity [AOR = 3.6 (1.54– 8.41)], < 1-hour travel to reach a health facility [AOR = 3.57 (1.03– 12.36)], answered unknown calls [AOR = 2.3 (1.04– 5.13)], and were satisfied with health-care provider service [AOR = 2.44 (1.04– 5.72)]. In the qualitative part, infrastructure, health facilities, socioeconomic factors, and patients’ behavioral factors were major identified barriers to receiving mHealth services. Conclusion: In this study, the willingness to receive mHealth services for those who have access to mobile phones increased. Additionally, the study highlighted common barriers to receiving mHealth services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Role of Inlet Boundary Conditions on Fuel-Air Mixing at Supercritical Conditions

Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.

02 PETROLEUM↗
DOE OSTI2020

An experimental investigation of flame and autoignition behavior of propane

We report autoignition delay time data are one important means to develop, quantify, and validate fundamental understanding of combustion chemistry at low temperatures (T<1200 K). However, low-temperature chemistry often has higher uncertainties and scatter in the experimental data compared with high-temperature ignition data (T>1200 K). In this study, autoignition properties of propane and oxygen mixtures were investigated using the University of Michigan rapid compression facility in order to understand the effects of ignition regimes on low-temperature ignition data. For the first time for propane, autoignition delay times were determined from pressure histories, and autoignition characteristics were simultaneously recorded using highspeed imaging of the test section through a transparent end-wall. Propane mixtures with fuel-to-O 2 equivalence ratios of φ = 0.25 and φ = 0.5 and O 2 -to-inert gas molar ratios of 1:3.76 were studied over the pressure range of 8.9 to 11.3 atm and the temperature range of 930 – 1070 K. The results showed homogeneous or strong autoignition occurred for all φ = 0.25 experiments, and inhomogeneous or mixed autoignition occurred for all φ = 0.5 experiments. While a limited temperature range is covered in the study, importantly the data span predicted transitions in autoignition behavior, allowing validation of autoignition regime hypotheses. Specifically, the results agree well with strong-autoignition limits proposed based on the Sankaran Criterion. The autoignition delay time data at the strong-ignition conditions are in excellent agreement with predictions using a well-validated detailed reaction mechanism from the literature and a zerodimensional modeling assumption. However, the experimental data at the mixed autoignition conditions were systematically faster than the model predictions, particularly at lower temperatures (T< ~970 K). The results are an important addition to the growing body of data in the literature that show mixed autoignition phenomena are important sources of the higher scatter observed in the low-temperature autoignition data for propane and other fuels. The results are discussed in terms of different methods to capture the effects of pre-autoignition heat release associated with mixed autoignition conditions and thereby address some of the discrepancies between kinetic modeling and experimental measurements.

42 ENGINEERING↗
DOE OSTI2024

Asynchronicity in opposed-piston RCMs: Does it matter?

Rapid Compression Machines (RCMs) are widely utilized to study combustion phenomena at engine-relevant conditions, and significant efforts are typically made to create a quiescent environment, particularly for investigations of autoignition chemistry. Opposed-piston configurations can be advantageous due to shorter compression times and reduced surface area to volume ratios. Each side must be actuated simultaneously, but this can be challenging in practice. These devices, like most RCMs, utilize hydraulics for actuation, speed control and arrestation of the piston at the end of the stroke; there is no mechanical control or linkage of the two piston trajectories. To quantify the magnitudes and effects of piston asynchronous behavior, this work employs both detailed experimental measurements and, for the first time, high-fidelity, Direct Numerical Simulation (DNS). The boundary conditions are carefully considered applying insight from high-resolution linear variable differential transformer (LVDT) measurements of the piston trajectory and a zero-dimensional kinematics model of the piston-shaft assembly. Sufficient resolution in the piston crevice region is used. The complicated fluid dynamical behavior that can evolve during piston compression and the ensuing delay processes due to offset timings from t offset = 0-10 ms is elucidated. It is found that near t offset = 6 ms and beyond, the boundary layer on the face of the first-seating piston can be sufficiently perturbed, due initially to reemergence of gas from the crevice of the firstseating piston, so that the adiabatic core can become degraded at long ignition delay times. Substantial mixing of colder gas into the interior of the reaction chamber can alter the measurements, similar to effects previously observed for improper piston crevice configuration. In conclusion, experimental techniques to mitigate asynchronous behavior are discussed and demonstrated.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order–disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

Chemistry↗
DOE OSTI2023

On the Role of Macrophysics and Microphysics in Km-Scale Simulations of Mixed-Phase Clouds During Cold Air Outbreaks

Regional atmospheric models struggle to maintain supercooled liquid in mixed-phase clouds during polar cold-air outbreaks (CAOs). Previous studies focused on the parameterization of aerosol, microphysics and turbulence to understand the origin of this widespread model bias. This study investigates the role of sub-grid cloud fraction parameterizations (CFP) in the simulation of mixed-phase cloud. Km-scale simulations are performed for a large number of CAO cases over Norway, for which continuous ground observations were collected at one site over 6 months. We use a novel analysis that attributes the cloud-radiative errors to deficiencies in specific cloud regimes. We show that the CFP matters for cloud-radiative effects in CAOs, but that it is probably not the primary cause of the lack of liquid water in simulated mixed-phase clouds. Of all the CFP sensitivities explored in this study, the prognostic representation of both liquid and ice shows most promise in increasing the liquid water path. A newly proposed hybrid CFP with prognostic frozen and diagnostic liquid cloud fraction reproduces some of the benefits of the prognostic scheme at reduced cost and complexity. All simulations appear to produce too large precipitation particles. As a result, reducing the snow deposition rate increases the liquid water path and improves the precipitation size distributions, while simulations are less sensitive to reduced riming rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Traffic Prediction for Merging Coordination Control in Mixed Traffic Scenarios

Connected and autonomous vehicles (CAVs) have the potential to bring in safety, mobility, and energy benefits to transportation. The control decisions of CAVs are usually determined for a look-ahead horizon based on previewed traffic information. This requires an effective prediction of future traffic conditions and its integration with the CAV control framework. However, the short-term traffic prediction using information from connectivity is a challenging research topic, especially for mixed traffic scenarios. This work focuses on the development of a traffic prediction framework for a merging coordination controller. The previously developed merging controller coordinates the merging sequence and travel speed of CAVs to maximize the energy efficiency and overall mobility. In mixed traffic scenarios, the controller receives information regarding the position of all the vehicles traveling inside a control zone and controls the desired speed of all CAVs. The controller has no control on the human-driven vehicles. The merging controller does not have direct information or an explicit prediction on the behaviors of human-driven vehicles. Aiming to improve the performance of the merging controller in various mixed traffic conditions, a traffic prediction algorithm is developed and evaluated in this work. The performance of this traffic prediction algorithm is investigated for various penetration rates of connectivity for a single-lane secondary road merging to a single-lane primary road. The results show that compared to a constant speed assumption of human-driven vehicles, the proposed traffic prediction algorithm is able to reduce the prediction error of the arrival time of human-driven vehicles at the merging zone by more than 50%.

Shao, Yunli↗