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DOE OSTI2021

Survival of Snow in the Melting Layer: Relative Humidity Influence

This study quantifies how far snow can fall into the melting layer (ML) before all snow has melted by examining a combination of in-situ observations from aircraft measurements in Lagrangian spiral descents from above through the ML and descents and ascents into the ML, as well as an extensive database of NOAA surface observer reports during the past 50 years. The airborne data contain information on the particle phase (solid, mixed, or liquid), population size distributions and shapes, along with temperature, relative humidity, and vertical velocity. A wide range of temperatures and ambient relative humidities are used for both the airborne and ground-based data. It is shown that an ice bulb temperature of 0°C, together with the air temperature and pressure (altitude), are good first order predictors of the highest temperature snowflakes can survive in the melting layer before completely melting. Particle size is also important, as is whether the particles are graupel or hail. If the relative humidity is too low, the particles will sublimate completely as they fall into the melting layer. Snow as warm as +7°C is observed from aircraft measurements and surface observations. Snow pellets survive to even warmer temperatures. Relationships are developed to represent the primary findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Summertime Marine Boundary Layer Cloud, Thermodynamic, and Drizzle Morphology over the Eastern North Atlantic: A Four-Year Study

Abstract Summertime remote sensor and in situ data from 2016 to 2019 collected at the ARM Eastern North Atlantic (ENA) Observatory are combined with aircraft measurements from the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign to quantify marine boundary layer (MBL) cloud, thermodynamic, and drizzle morphology in the region. A radar reflectivity–rainfall rate relationship ( Z – R ) is developed from aircraft data and 6-h cloud morphological regimes are identified from ENA data using a k -means algorithm driven by three independent inputs quantifying cloud thickness, drizzle intensity, and cloud field geometric complexity. Four separate MBL structural regimes representing non- or weakly drizzling single-layer stratocumulus, drizzling stratocumulus and cumulus-coupled stratocumulus, deep convection, and broken clouds embedded in northerly flow are identified. Single-layer stratocumulus is indicated when weak subtropical anticyclones are significantly west of the ENA site, and the MBL is cooler and drier than when drizzling and cumulus-coupled stratocumulus and broken clouds are observed. Drizzling and cumulus-coupled stratocumulus clouds are observed on the eastern flank of strong subtropical anticyclones in deep warm moist air masses with wind speeds exceeding 7 m s −1 and strong near-surface wind shear. Broken clouds exhibit strong wind shear near the inversion, while single-layer stratocumulus clouds have lower wind speeds and minimal shear. Net latent heat fluxes in the subcloud layer resulting from a combination of the ocean surface heat flux and evaporating drizzle average near zero over long periods in drizzling and cumulus-coupled stratocumulus. The ECMWF reanalysis version 5 (ERA5) is found to accurately represent single-layer stratocumulus properties, while producing significant discrepancies when drizzling stratocumulus and cumulus-coupled stratocumulus are observed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assessment of Bird Strike Likelihood to Refine Bird Strike Risk Models

In its most basic form, bird strike risk is comprised of a frequency component that reflects the likelihood of a collision and a severity component that reflects the cost (monetary or otherwise) of the incident. The bird strike risk model currently used by United State Department of Agriculture (USDA) Wildlife Services to evaluate the risk posed by individual bird species at airports and establish priorities for management was developed in 2018. The model uses airport-specific data on the number of reported strikes for a species recorded in the Federal Aviation Administration (FAA)’s National Wildlife Strike Database as a measure of frequency and the species’ relative hazard score as a measure of severity. The model was tested against independent data, found to perform well overall, and is being implemented widely across the United States. However, the model has limitations, including that species known to pose risk to aircraft locally, but not present in the strike record database, are not reflected as a major component of risk. Standard bird survey methodology commonly used at airports (e.g. point counts or transects) potentially can be used to complement wildlife strike records to calculate frequency or relative abundance of species. However, these methods generally focus on airport-wide population estimation and often ignore vital information that contributes to the true likelihood of a strike, such as use of runway protection zones and other critical areas, and spatial and temporal overlap with departing or approaching aircraft. As such, a more detailed understanding of space use by birds across landcovers and population fluctuations across the year is needed to accurately estimate the likelihood of bird strikes at airports. In this manuscript, we will review the extant risk model, including a discussion on its limitations. We then discuss approaches for refining our understanding of strike likelihood and briefly touch on needs for estimating probability of strike severity (cost).

bird strike, aircraft collision, damage by wildlif↗

DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗

DOE OSTI2020

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗

DOE OSTI2020

Internal arcing and lightning strike damage in short carbon fiber reinforced thermoplastic composites

Short carbon fiber reinforced thermoplastic composites (S-CFRTP) are currently used in interior parts of aircraft such as electronic circuitry and assemblies, and their usage in aircraft's exterior is also being studied. In this paper, we present a lightning strike study on S-CFRTPs. High performance thermoplastics such as a polyphenylene sulfide (PPS) reinforced with 50% weight (wt.) short carbon fiber (CF), a polyphenyl sulfone (PPSU) reinforced with 25 % wt. Short CF and a acrylonitrile butadiene styrene (ABS) with 20 % wt. short CF were tested against artificial lightning strikes. All samples were prepared using an extrusion compression molding (ECM) technique. A modified component A (100 kA) of lightning waveform, as defined by SAE ARP 5412-B, was applied. High speed imaging, thermography, scanning electron microscopy (SEM) and ultrasonic non-destructive evaluation were performed to understand the effect of lightning strikes on the thermoplastic panels. The results show that electrical conductivity of the composite, thermal stability of the polymers, and CF orientation significantly affected the damage from artificial lightning strikes. PPS composite had the highest resilience against lightning strike damage, retaining 95% and 89% residual flexural strength and modulus, respectively. CF exfoliation due to internal arcing was observed in the carbon fiber-reinforced ABS polymer (CF-ABS) composite. CF exfoliation damage has been reported for the first time.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Intense formation of secondary ultrafine particles from Amazonian vegetation fires and their invigoration of deep clouds and precipitation

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. Here, we find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar datasets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective up- and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size-intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ~ 0.5), and increases in draft intensity with altitude. A model-observational intercomparison for draft properties (core width, intensity, mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (?x) that range from 4 km to 250 m. It is shown that the simulations performed at ?x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.

radar, arm, wind profiler, goamazon↗

DOE OSTI2020

Reconciling Contrasting Relationships Between Relative Dispersion and Volume-Mean Radius of Cloud Droplet Size Distributions

Cloud droplet spectral relative dispersion is critical to parameterizations of cloud radiative properties, warm-rain initiation, and aerosol-cloud interactions in models; however, there is no consistent relationship between relative dispersion and volume-mean radius in literature, which hinders improving relative dispersion parameterization and calls for physical explanation. Here we show, by analyzing aircraft observations of cumulus clouds during Routine AAF [Atmospheric Radiation Measurement (ARM) Aerial Facility] Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations, that the correlation between relative dispersion and volume-mean radius changes from positive to negative as volume-mean radius increases. With the new observation, we postulate that the sign of the correlation is determined by whether or not condensation (evaporation) occurs simultaneously with significant new activation (deactivation). The hypothesis is validated by simulations of both an adiabatic cloud parcel model and a parcel model accounting for entrainment-mixing. A new quantity, first bin strength, is introduced to quantify this new observation. Theoretical analysis of truncated gamma and modified gamma size distributions further supports the hypothesis and reconciles the contrasting relationships between relative dispersion and volume-mean radius, including the results in polluted fog observations. The results could shed new light on the so-called “twilight zone” between cloudy and cloud-free air, which in turn affects evaluation of aerosol-cloud interactions and retrieval of aerosol optical depth.

Lu, Chunsong↗

DOE OSTI2021

Joint CO 2 Mole Fraction and Flux Analysis Confirms Missing Processes in CASA Terrestrial Carbon Uptake Over North America

Terrestrial biosphere models (TBMs) play a key role in the detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. This study uses atmospheric CO 2 mole fraction ([CO 2 ]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the CASA TBM. WRF-Chem is used to simulate [CO 2 ] using biogenic CO 2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO 2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO 2 ] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO 2 ] data show that the CASA ensemble net ecosystem exchange (NEE) of CO 2 is biased high (NEE too positive) and identify the maximum light use efficiency E max a key parameter that drives the spread of the CASA ensemble in summer 2016. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements confirms missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilates observed [CO 2 ]. Flux tower analyses, however, reveal an unrealistic overestimation of nighttime respiration in CT2017 due to the limitation of inversion strategy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Two-Point Mixing, Buoyancy Sorting, and Updraft Dilution in the RACORO Campaign

Aircraft measurements of shallow cumulus cases from the RACORO campaign are studied in a transformed Paluch diagram. Previous studies have interpreted samples in Paluch diagrams in terms of two-point mixing and buoyancy sorting. The two-point mixing behavior is frequently seen in the RACORO observations and can be unambiguously distinguished from buoyancy sorting. The latter is not found in the RACORO observations and is also rare in a large-eddy simulation (LES) of a RACORO case. As opposed to often assumed gradual dilution of updrafts, around half of the instances of two-point mixing show no clear evidence of updraft dilution. We also find substantial spread in the properties of parcels that have adjusted to their level of neutral buoyancy and that a LES with horizontally homogeneous surface underestimates this spread. The inclusion of surface heterogeneity in the LES improves the agreement of its horizonal moisture variations with the aircraft measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Observed Relationships Between Cloud Droplet Effective Radius and Biogenic Gas Concentrations in Summertime Marine Stratocumulus Over the Eastern North Atlantic

Abstract Biogenic gases are a prominent component of the summertime marine boundary layer (MBL) over the Eastern North Atlantic. One of these gases, dimethyl sulfide (DMS), can produce sulfate cloud condensation nuclei (CCN) that, in theory, can brighten clouds through photolysis, and produces a reaction product, methane sulfonic acid (MSA). It is also possible that DMS can interact with sea‐salt or other marine aerosols changing their CCN activation spectrum, which could also modify cloud microphysical structure. Data collected aboard the G1 aircraft during the Aerosol Cloud Experiment Eastern North Atlantic (ACE‐ENA) in well‐mixed and decoupled marine boundary layers (MBLs) were used to examine relationships between the cloud droplet effective radii, , and the concentrations of DMS and MSA in constant cloud liquid water content (LWC) bins. A weak but statistically significant negative correlation was observed between CCN concentration and in most LWC bins, regardless of the source of the CCN, while a weak but statistically significant positive correlation between and DMS was observed. No correlation between the cloud droplet number concentration and DMS was found. The presence of MSA indicated that DMS‐to‐sulfate photolysis was likely occurring, but data sparsity prevented a statistically significant conclusion regarding the relationship between MSA and . Data sparsity in decoupled conditions also prevented statistically significant conclusions. To properly address biogenic gas impacts on cloud microphysics, it is recommended that aircraft data be supplemented by long‐term biogenic gas measurements at the surface in marine locations with appropriate remote and in‐situ cloud sensing capabilities, and the analysis limited to well‐mixed MBL's.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Remote Sensing of Electric Fields Observed Within Winter Precipitation During the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Field Campaign

Aircraft electric fields from NASA's Lightning Instrument Package (LIP) were coupled with other airborne and ground-based, and in situ measurements to understand electrification in winter clouds that did not produce lightning. The measurements were made during seven research flights by a NASA ER-2 during the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Observed total electric field magnitudes were as high as 80 V m -1 and variability in the electric field was observed along the flight path of the ER-2, indicating horizontal and/or vertical inhomogeneity in the cloud's electrical structure. X-band airborne radar data indicated 20-dBZ echo tops above 5 km in regions where electrification exceeded 10 V m -1 . In these regions, 85-GHz brightness temperatures (TB) from an airborne radiometer were lower than 265 K, with the lowest T B (~210 K) associated with ice scattering collocated with the strongest electric fields. In situ microphysical measurements from the NASA P-3 aircraft on February 7 indicated that regions near strong electric field contained supercooled water, rimed ice hydrometeors, ice water p- content as high as 1 g m -3 , liquid water content as high as 0.15 g m -3 , and supersaturation as high as 3.5%. Furthermore, these observations support the role of mixed phase microphysics in the generation of electric fields in clouds. In three case studies, ground based S-band polarimetric radar observed depolarization streaks in differential reflectivity near areas where the strongest electrification was observed. This observation reinforces the utility of depolarization streaks to identify areas of electrification prior to lightning occurrence.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Realism of Lagrangian Large Eddy Simulations Driven by Reanalysis Meteorology: Tracking a Pocket of Open Cells Under a Biomass Burning Aerosol Layer

An approach to drive Lagrangian large eddy simulation (LES) of boundary layer clouds with reanalysis data is presented and evaluated using satellite (Spinning Enhanced Visible and Infrared Imager, SEVIRI) and aircraft (Cloud-Aerosol-Radiation Interactions and Forcing, CLARIFY) measurements. The simulations follow trajectories of the boundary layer flow. They track the formation and evolution of a pocket of open cells (POC) underneath a biomass burning aerosol layer in the free troposphere. The simulations reproduce the evolution of observed stratocumulus cloud morphology, cloud optical depth, and cloud drop effective radius, and capture the timing of the cloud state transition from closed to open cells seen in the satellite imagery on the three considered trajectories. They reproduce a biomass burning aerosol layer identified by the in-situ aircraft measurements above the inversion of the POC. Entrainment of aerosol from the biomass burning layer into the POC is limited to the extent of having no impact on cloud- or boundary layer properties, in agreement with the CLARIFY observations. The two-moment bin microphysics scheme used in the simulations reproduces the in-situ cloud microphysical properties reasonably well. A two-moment bulk microphysics scheme reproduces the satellite observations in the non-precipitating closed-cell state, but overestimates liquid water path and cloud optical depth in the precipitating open-cell state due to insufficient surface precipitation. A boundary layer cold and dry bias occurring in LES can be counteracted by reducing the grid aspect ratio and by tightening the large scale wind speed nudging towards the surface.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Source Levels of In‐Cloud Air in Shallow Cumulus: Consistency Between Paluch Diagram and Lagrangian Particle Tracking

Abstract The Paluch diagram is a widely used tool for interpreting aircraft measurements of shallow cumulus clouds. A prior study conducted by Heus et al. (2008,https://doi.org/10.1175/2008jas2572.1) concluded that the source levels of in‐cloud air inferred from the Paluch diagram exhibit biases, sometimes of several hundred meters, in comparison to those derived from Lagrangian particle tracking. In this short study we revisit this comparison. The results indicate that the upper source levels of in‐cloud air determined from the Lagrangian Particle Tracking and the Paluch diagram are consistent, and the choice of statistical methods is crucial. The significance of this research lies in confirming the reliability of the Paluch analysis, enabling its confident application to aircraft data.

Meteorology & Atmospheric Sciences↗

DOE OSTI2022

The challenges and opportunities of battery-powered flight

Aircraft, and the aviation ecosystem in which they operate, are shaped by complex trades among technical requirements, economics and environmental concerns, all built on a foundation of safety. This Perspective explores the requirements of battery-powered aircraft and the chemistries that hold promise to enable them. The difference between flight and terrestrial needs and chemistries are highlighted. Safe, usable specific energy rather than cost is the major constraint for aviation. We conclude that battery packs suitable for flight with specific energy approaching 600 kilowatt hours per kilogram may be achievable in the next decade given sufficient investment targeted at aeronautical applications.

25 ENERGY STORAGE↗

DOE OSTI2024

Measuring the robustness of predictive probability for early stopping in two-group comparisons

We report physical experiments are often expensive and time-consuming. Test engineers must certify the compatibility of aircraft and their weapon systems before they can be deployed in the field, but the testing required is time consuming, expensive, and resource limited. Adopting Bayesian adaptive designs is a promising way to borrow from the successes seen in the clinical trials domain. The use of predictive probability (PP) to stop testing early and make faster decisions is particularly appealing given the aforementioned constraints. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. Although PP has been thoroughly studied for binary data, there is less work with continuous data, where many reliability studies are interested in certifying the specification limits of components. A simulation study evaluating the robustness of this approach indicates early stopping based on PP is reasonably robust to minor assumption violations, especially when only a few interim analyses are conducted. The simulation study also compares PP to conditional power, showing its relative strengths and weaknesses. A post-hoc analysis exploring whether release requirements of a weapon system from an aircraft are within specification with desired reliability resulted in stopping the experiment early and saving 33% of the experimental runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Thermal Management System for an Electric Machine with Additively Manufactured Hollow Conductors with Integrated Heat Pipes: Preprint

This paper discusses steps taken to size a thermal management system for an aircraft propulsion electric machine containing additively manufactured coils integrated with heat pipes aimed at boosting its specific power. Experimental setups are used to size and characterize heat pipes for the application and 3D thermal FEA is used to determine optimum heat transfer coefficient of convective boundaries. Geometric details of fin-based surface area enhancement required to reach target combined overall heat transfer coefficient (U) and surface area (A) performance (UA) in W/K, is worked out for relevant boundaries and the resulting UA is verified in 3D thermal FEA. Thermal management system's UA (by extension specific power) sensitivity to coolant temperature is explored and temperature distribution plots of optimized machine components are presented and discussed.

additive manufacturing↗