Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “lack”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius/SAC-D, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 in October and low values of 33.40 in May, the SMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root-mean-square-errors from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias-adjustment done on the dataset. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season.

SSS↗
NASA NTRS2019

Dynamic RPS Path to Flight

NASA Glenn Research Center has been pursuing the development of dynamic power conversion for several decades. Candidate NASA missions involve mutli-year travel to far away destinations, or to extreme environments where sunlight does not exist. Human-base mission studies also show that power needs would be beyond the capabilities of solar energy conversion, and instead would require nuclear reactor energy sources, for which the thermal energy must be converted to electricity. Dynamic power conversion technology has developed sufficiently to make a sound engineering argument that it is suitable for these NASA missions. Dynamic conversion power sources have yet to be flown in space, and thus suffer a disadvantage owing to their lack of heritage data on flight missions. One of the largest obstacles for adoption is the uncertainty in reliability of a device with moving parts. However, significant progress has been made toward demonstrating the technology capable in all relevant environments, with the necessary long life. Another hurdle for adoption is the lack of mission, which would drive specific requirements, and provide a solid timeline for technology development endpoint. Until a mission is identified, an alternative approach is necessary to advance a dynamic power conversion system towards flight.

Oriti, Sal↗
NASA NTRS2019

Dynamic RPS Path to Flight - Nuclear and Emerging Technologies for Space

NASA Glenn Research Center has been pursuing the development of dynamic power conversion for several decades. Candidate NASA missions involve mutli-year travel to far away destinations, or to extreme environments where sunlight does not exist. Human-base mission studies also show that power needs would be beyond the capabilities of solar energy conversion, and instead would require nuclear reactor energy sources, for which the thermal energy must be converted to electricity. Dynamic power conversion technology has developed sufficiently to make a sound engineering argument that it is suitable for these NASA missions. Dynamic conversion power sources have yet to be flown in space, and thus suffer a disadvantage owing to their lack of heritage data on flight missions. One of the largest obstacles for adoption is the uncertainty in reliability of a device with moving parts. However, significant progress has been made toward demonstrating the technology capable in all relevant environments, with the necessary long life. Another hurdle for adoption is the lack of mission, which would drive specific requirements, and provide a solid timeline for technology development endpoint. Until a mission is identified, an alternative approach is necessary to advance a dynamic power conversion system towards flight.

Oriti, Salvatore↗
NASA NTRS2019

Challenges in Qualification of Thermal Protection Systems in Extreme Entry Environments

Planetary entry vehicles employ ablative TPS materials to shield the aeroshell from entry aeroheating environments. To ensure mission success, it must be demonstrated that the heat shield system, including local features such as seams, does not fail at conditions that are suitably margined beyond those expected in flight. Furthermore, its thermal response must be predictable, with acceptable fidelity, by computational tools used in heat shield design. Mission assurance is accomplished through a combination of ground testing and material response modelling. A material's robustness to failure is verified through arcjet testing while its thermal response is predicted by analytical tools that are verified against experimental data. Due to limitations in flight-like ground testing capability and lack of validated high-fidelity computational models, qualification of heat shield materials is often achieved by piecing together evidence from multiple ground tests and analytical simulations, none of which fully bound the flight conditions and vehicle configuration. Extreme heating environments (>2000 W/sq. cm heat flux and >2 atm pressure), experienced during entries at Venus, Saturn and Ice Giants, further stretch the current testing and modelling capabilities for applicable TPS materials. Fully-dense Carbon Phenolic was the material of choice for these applications; however, since heritage raw materials are no longer available, future uses of re-created Carbon Phenolic will require re-qualification. To address this sustainability challenge, NASA is developing a new dual-layer material based on 3D weaving technology called Heat shield for Extreme Entry Environments (HEEET). Regardless of TPS material, extreme environments pose additional certification challenges beyond what has been typical in recent NASA missions. Scope of this presentation: This presentation will give an overview of challenges faced in verifying TPS performance at extreme heating conditions. Examples include: (1) Bounding aeroheating parameters (heat flux, pressure, shear and enthalpy) in ground facilities. How to certify TPS if environments can't be bounded or aeroheating parameters can't be simultaneously achieved. (2) Higher uncertainties in ground test environments (facility calibration and analytical predictions) at extreme conditions. (3) Testing in flows similar to planetary atmosphere composition (H2/He for Gas and Ice Giants). (4) Test sample size limitations for qualifying seam designs. (5) Lack of computational tools capable of simulating all significant aspects of TPS performance (including initiation and propagation of failures). This presentation will provide recommendations on how the EDL community can address these challenges and mitigate some of the risks involved in flying TPS materials at extreme conditions. Examples include: (1) Dedicated activity to understanding TPS failure modes. Develop computational tools capable of modelling fluid interaction with material's thermostructural response. Validate these tools through failure testing. A better understanding of failure mechanisms may eliminate the need to fully bound all aeroheating parameters in ground testing. (2) Enhancements to current testing facilities to simulate flight-like ablation mechanism (ex. testing in Nitrogen at Ames Interaction Heating Facility to limit oxidation in favor of more sublimation). (3) Improved characterization of test conditions with new diagnostic methods and determination of environment uncertainty through rigorous statistical analysis of available data. (4) Design margin policies that are directly tied to uncertainties in ground test environments and modelling fidelity

Mahzari, Milad↗
NASA NTRS2019

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large-scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 psu in October and low values of 33.40 psu in May, theSMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root mean square difference from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias adjustment done on the data set. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season. Salinity together with temperature is the key parameters that control the state and circulation of the World's oceans. The large‐scale distribution of the sea surface salinity of the Southern Ocean is quantified for the first time using Aquarius, Soil Moisture and Ocean Salinity, and Soil Moisture Active and Passive satellite microwave data. Seasonal and interannual changes in the distribution as affected by the sea ice cover, surface temperature, and precipitation are evaluated. Comparative studies were done, using four different products that are publicly available, in conjunction with in situ observations to gain insights into the true nature of the distribution and how consistently the sea surface salinity is depicted by the different products. There are general consistencies in the products, and discrepancies are attributed to different algorithms, smoothing techniques, and sea ice masking. Aquarius data are shown to have higher accuracy than Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive products in part because of having concurrent scatterometer that provides accurate correction to roughness effects.

Garcia-Eidell, Cynthia↗
NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗
NASA NTRS2019

Reliability Requirements and Research Strategies for Urban Air Mobility Propulsion

An emerging new mission for aeronautics is Urban Air Mobility (UAM), a concept for air transportation around metropolitan areas with passenger-carrying operations. UAM vehicles must be capable of vertical take-off and landing, and this requirement presents unique technical challenges for electric and hybrid-based vertical take-off and landing (eVTOL). A critical challenge for UAM market growth is to gain public acceptance for being as safe as - or safer than - commercial air travel and automotive transportation. There is a lack of data for propulsion systems, components, and the associated thermal management systems for UAM eVTOL propulsion systems. The new mission, new propulsion system concepts, safety criticality of propulsion component performance during vertical take-off and lift operations, and lack of data presents many research challenges and opportunities. NASA has developed and published UAM vehicle concept studies. For a subset of the said concept vehicles, NASA has contracted for a study to identify failure modes and hazards associated with the propulsion systems of the concept vehicles and to perform functional hazard analyses (FHA) and failure modes and effects criticality analyses (FMECA) for each. From the completed study results, it was recommended for NASA to support research toward developing electric/hybrid-electric propulsion components with improved reliability and to explore powertrain architectures that can take advantage of higher reliability components to achieve inherent air-vehicle safety. NASA has started a research effort for UAM propulsion with a focus toward improving safety and reliability. Recent results and research strategy will be discussed toward the goals by means of: 1) improving individual component reliability through advanced materials and design methods, 2) improving the thermal management system, and 3) designing propulsion system architectures to provide inherent UAM vehicle safety.

Krantz, Timothy↗
NASA NTRS2019

Quantifying Water Storage Change and Land Subsidence Induced by Reservoir Impoundment Using GRACE, Landsat, and GPS Data

The construction of hydropower dams is a common strategy to support a country's increasing need for electricity and river water management for industry and agriculture. Although the hydrological and geophysical impacts of water relocation are usually assessed prior to impoundment, their accuracy is generally limited due to the lack of in situ observations, especially in a remote area. This study presents a workflow to quantify the terrestrial water storage change (TWS) and land subsidence induced by a reservoir's water impoundment using multiple satellite observations (GRACE, Landsat), land surface models (CABLE, GLDAS, NCEP, ECMWF), and GPS data. The study site is the Bakun Dam, located in Sarawak, Malaysia, which is the largest hydropower dam in Southeast Asia. Commencing operation in late 2010, the dam induced a change of water mass and lake surface area that was clearly observed by GRACE and Landsat observations, respectively. During the 17-month impounding period (from August 2010 to December 2011), GRACE observed a dramatic increase of approximately 200 mm equivalent water height, while Landsat detected an increased lake extent of around 600 km2. In this paper, a forward model is developed to determine the increased water surface level corresponding to GRACE observations, estimated to be about 120 m. In contrast to GRACE, the TWS derived from land surface models cannot capture the increased TWS, due to the lack of reservoir routing algorithms in the models. In addition, the land subsidence was calculated using the disk load model constructed based on the GRACE-derived lake level and Landsat-derived lake extent; the result is validated with the GPS data from BIN1 station, located at the western coast of Borneo. The commencement stage of the Bakun Dam induces the large-scale land subsidence, which causes the GPS-BIN1 station to subside by ~9 mm, and move toward the Bakun Lake by ~4 mm. Computation of the surface displacements directly from GRACE spherical harmonic coefficient data fails to capture the subsidence feature, mainly due to the truncation error. Overall, this study demonstrates that evaluating GRACE in conjunction with Landsat, LSMs, and GPS data allows the exploitation of the gravity signal at a much smaller spatial scale than its intrinsic resolution. Benefiting from global coverage, the newly developed satellite-based algorithm is a valuable tool for assessing the impacts of reservoir operation on hydrological and geophysical changes from local to regional scales.

Natthachet Tangdamrongsub↗
NASA NTRS2019

Quantifying Water Storage Change and Land Subsidence Induced by Reservoir Impoundment Using GRACE, Landsat, and GPS Data

The construction of hydropower dams is a common strategy to support a country's increasing need for electricityand river water management for industry and agriculture. Although the hydrological and geophysical impacts ofwater relocation are usually assessed prior to impoundment, their accuracy is generally limited due to the lack ofin situ observations, especially in a remote area. This study presents a workflow to quantify the terrestrial waterstorage change (TWS) and land subsidence induced by a reservoir's water impoundment using multiple satelliteobservations (GRACE, Landsat), land surface models (CABLE, GLDAS, NCEP, ECMWF), and GPS data. The studysite is the Bakun Dam, located in Sarawak, Malaysia, which is the largest hydropower dam in Southeast Asia.Commencing operation in late 2010, the dam induced a change of water mass and lake surface area that wasclearly observed by GRACE and Landsat observations, respectively. During the 17-month impounding period(from August 2010 to December 2011), GRACE observed a dramatic increase of approximately 200mmequivalent water height, while Landsat detected an increased lake extent of around 600 km2. In this paper, aforward model is developed to determine the increased water surface level corresponding to GRACE observations,estimated to be about 120 m. In contrast to GRACE, the TWS derived from land surface models cannotcapture the increased TWS, due to the lack of reservoir routing algorithms in the models. In addition, the landsubsidence was calculated using the disk load model constructed based on the GRACE-derived lake level andLandsat-derived lake extent; the result is validated with the GPS data from BIN1 station, located at the westerncoast of Borneo. The commencement stage of the Bakun Dam induces the large-scale land subsidence, whichcauses the GPS-BIN1 station to subside by ~9 mm, and move toward the Bakun Lake by ~4 mm. Computation ofthe surface displacements directly from GRACE spherical harmonic coefficient data fails to capture the subsidencefeature, mainly due to the truncation error. Overall, this study demonstrates that evaluating GRACE inconjunction with Landsat, LSMs, and GPS data allows the exploitation of the gravity signal at a much smallerspatial scale than its intrinsic resolution. Benefiting from global coverage, the newly developed satellite-basedalgorithm is a valuable tool for assessing the impacts of reservoir operation on hydrological and geophysicalchanges from local to regional scales.

Tangdamrongsub, Natthachet↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2020

Biases in the In Situ Measurement of Particulate Organic Carbon and Its Effect on the Calibration and Validation of Ocean Color Sensors

Particulate organic carbon (POC) plays an oversize role, relative to its standing stock in the global carbon (C) cycle. Accurate measurement of POC is central to understanding the ocean C flux and its sensitivity to climate forcing. POC is a standard NASA ocean color data product, which lacks a consensus, quality-assured measurement protocol for satellite validation. Thus, algorithms based on field measurements lacking verified uncertainties have limited applicability towards climate data records. Different sampling and filtration protocols, and blank corrections, introduce biases in the magnitude of POC measured from the field. A significant filter blank attributable to dissolved organic C (DOC) adsorption that, until recently has been seldom corrected for, likely has introduced biases in POC global datasets.

Chaves, Joaquín E.↗
NASA NTRS2016

Building a Robust Commercial Microgravity Economy in Earth's Orbit: Economic Readiness Considerations

The reduced gravity environment of space provides a unique opportunity to further our understanding of various materials phenomena involving the molten, fluidic and gaseous states as well as life science applications where, contrary to earlier beliefs, microgravity induces changes in single cells and simple organisms; not only in large organisms with a complex overall response to gravity (or lack thereof). The potential breadth of commercial opportunities in microgravity thus spans over many verticals of the private sector with applications ranging from fiber optics, high-resolution crystals, microencapsulation, 3D organs to perfume and color dyes. Overall, products manufactured in microgravity hold the promise to have key properties surpassing their best terrestrial counterparts. Commercialization, also known as taking a new technology to market, is a journey in itself where the business, economic, market and technological components must align to generate a successful outcome. A business perspective is very different than technology maturation. In order for a technology to be ready for commercialization, it must not only be mature, but it must also have a compelling business case, and the means to scale up production must be identified and practical. Creating a robust economy in Earths orbit (Fig 1) is especially challenging because of the complexity (high risks, lack of standardization) involved in predicting future growth. This complexity can easily overwhelm the fact that many of the products have an attractive touch of space which aids with branding and marketing.This paper reviews the types of added value that can be extracted from space, with an emphasis on the microgravity environment. In addition, lessons learned from past commercialization efforts will be reviewed. While past efforts have yielded some point successes, they have as a whole failed to precipitate a sustainable LEO based market

Cozmuta, Ioana↗
NASA NTRS2016

Economic Readiness Level Considerations for a Robust LEO Economy

The reduced gravity environment of space provides a unique opportunity to further our understanding of various materials phenomena involving the molten, fluidic and gaseous states as well as life science applications where, contrary to earlier beliefs, microgravity induces changes in single cells and simple organisms; not only in large organisms with a complex overall response to gravity (or lack thereof). The potential breadth of commercial opportunities in microgravity thus spans over many verticals of the private sector with applications ranging from fiber optics, high-resolution crystals, microencapsulation, 3D organs to perfume and color dyes. Overall, products manufactured in microgravity hold the promise to have key properties surpassing their best terrestrial counterparts. Commercialization, also known as taking a new technology to market, is a journey in itself where the business, economic, market and technological components must align to generate a successful outcome. A business perspective is very different than technology maturation, which can be measured with the usual Technology Readiness Level (TRL) approach. In order for a technology to be ready for commercialization, it must not only be mature (high TRL), but it must also have a compelling business case, and the means to scale up production must be identified and practical. Creating a sustainable economy in an emerging market such as microgravity is especially challenging because of the complexity (high risks, lack of standardization) involved in predicting future growth. This complexity can easily overwhelm the fact that many of the products have an attractive touch of space which aids with branding and marketing.This paper builds upon the concept of the verticals of microgravity, capturing not only new lines of investigations but also promising killer apps originating from microgravity to discuss and define the notion of Economic Readiness Level (ERL). To advance in ERL, the technology itself may not necessarily need to mature at all, but the understanding of its economic potential does. Building upon ERL, a model that ultimately leads to the creation of pathways for infusion of private capital and a sustainable commercial microgravity LEO-Earth economy will be discussed.

Cozmuta, Ioana↗
NASA NTRS2020

Lessons Learned from Recent Space Flight Assessments

This paper highlights four space flight assessments, each with an important lesson. The first assessment points to the challenge of material substitution for weight saving. Space Shuttle’s external tank stringers were changed from Al 2024 to Al 2090 without comprehensive analysis. Lack of understanding the low Short-Transverse (S-T) strength associated with Al 2090 led to widespread cracking. The second assessment addresses the failure to understand that time-dependent relationships and loading interactions of multiple disciplines can lead to non-conservative results. An aerodynamic test article of the Orion Multi-Purpose Crew Vehicle (MPCV) Launch Abort System (LAS) was analyzed for worst-case single discipline loads, showing positive structural margins. When disciplines were combined with time dependent loading, the analysis resulted in negative margins. The third assessment highlights the pitfalls of putting blind trust in historical architecture. The MPCV re-entry heat shield adopted the Apollo design approach despite fundamental differences between the two designs (e.g., the underlying substructure design, material differences, scale, and geometry). Manufacturing resulted in cracking along seams of the cured MPCV heat shield and produced lower mechanical properties of the Avcoat witness panels. Subsequently, the design was changed to a block architecture, which introduced unperceived technical, cost, and schedule risk. The fourth assessment shows how the lack of a comprehensive investigation of design modifications and inappropriate Finite Element Analyses (FEA) can lead to trouble. A mass reduction campaign eliminated highly critical internal struts on the ISS Node-1 pressurized module based on inadequate and uncorrelated FEA. High strains were noted and ignored as a modeling aberration. A system level high fidelity FEA discovered large plastic strains in the gussets of all four radial ports. These findings initiated a full-scale test campaign, new test correlated assembly-level FEA, and significant design modifications.

James P Smith↗
NASA NTRSDate not supplied

TPSAS-NF1676L-15315-DND

Some advantages over mechanical joints: - Lower structural weight - Lower manufacturing cost - Improve damage tolerance However: - Lack of failure criteria and material/structural validated models - Lack of an accurate and reliable quality control

Donato Girolamo↗
NASA NTRS2020

Constraining the Twomey effect from satellite observations: Issues and perspectives

The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd,ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions which also comprises rapid adjustments. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd,ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large scale (hundreds of kilometres) ΔNd,ant remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and vertical wind and by droplet sink processes. These operate at scales on the order of 10s of metres at which only localized observations are available and at which no approach exists yet to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are three key uncertainties in determining ΔNd,ant, namely the quantification (i) of the cloud-active aerosol – the cloud condensation nuclei concentrations (CCN) at or above cloud base –, (ii) of Nd, as well as (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data. A fourth uncertainty, the anthropogenic perturbation to CCN concentrations, is also not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: In terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, the non-direct relation to the concentration of aerosols, the impossibility to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilizing colocated polarimeter and lidar instruments, ideally including high spectral resolution lidar capability at two wavelengths to maximize vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity, and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd – to – CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

Twomey effect↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
NASA NTRS2019

ALMA Autocorrelation Spectroscopy of Comets: the HCN/H13CN Ratio in C/2012 S1 (ISON)

The Atacama Large Millimeter/submillimeter Array (ALMA) is a powerful tool for high-resolution mapping of comets, but the main interferometer (comprised of 50×12-m antennas) is insensitive to the largest coma scales due to a lack of very short baselines. In this work, we present a new technique employing ALMA autocorrelation data (obtained simultaneously with the interferometric observations), effectively treating the entire 12-m array as a collection of single-dish telescopes. Using combined autocorrelation spectra from 28 active antennas, we recovered extended HCN coma emission from comet C/2012 S1 (ISON), resulting in a fourteen-fold increase in detected line brightness compared with the interferometer. This resulted in the first detection of rotational emission from H13CN in this comet. Using a detailed coma radiative transfer model accounting for optical depth and non-LTE excitation effects, we obtained an H12CN/H13CN ratio of 88±18, which matches the terrestrial value of 89, consistent with a lack of isotopic fractionation in HCN during comet formation in the protosolar accretion disk. The possibility of future discoveries in extended sources using autocorrelation spectroscopy from the main ALMA array is thus demonstrated.

M A Cordiner↗