Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “precondition”

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.

409 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Damage Accelerates Ice Shelf Instability and Mass Loss in Amundsen Sea Embayment

Pine Island Glacier and Thwaites Glacier in the Amundsen Sea Embayment are among the fastest changing outlet glaciers in West Antarctica with large consequences for global sea level. Yet, assessing how much and how fast both glaciers will weaken if these changes continue remains a major uncertainty as many of the processes that control their ice shelf weakening and grounding line retreat are not well understood. Here, we combine multisource satellite imagery with modeling to uncover the rapid development of damage areas in the shear zones of Pine Island and Thwaites ice shelves. These damage areas consist of highly crevassed areas and open fractures and are first signs that the shear zones of both ice shelves have structurally weakened over the past decade. Idealized model results reveal moreover that the damage initiates a feedback process where initial ice shelf weakening triggers the development of damage in their shear zones, which results in further speedup, shearing, and weakening, hence promoting additional damage development. This damage feedback potentially preconditions these ice shelves for disintegration and enhances grounding line retreat. The results of this study suggest that damage feedback processes are key to future ice shelf stability, grounding line retreat, and sea level contributions from Antarctica. Moreover, they underline the need for incorporating these feedback processes, which are currently not accounted for in most ice sheet models, to improve sea level rise projections.

glaciology↗
NASA NTRS2021

Southern Ocean Heat Storage, Reemergence, and Winter Sea Ice Decline Induced by Summertime Winds

The observational record shows a substantial 40-yr upward trend in summertime westerly winds over the Southern Ocean, as characterized by the southern annular mode (SAM) index. Enhanced summertime westerly winds have been linked to cold summertime sea surface temperature (SST) anomalies. Previous studies have suggested that Ekman transport or upwelling is responsible for this seasonal cooling. Here, another process is presented in which enhanced vertical mixing, driven by summertime wind anomalies, moves heat downward, cooling the sea surface and simultaneously warming the subsurface waters. The anomalously cold SSTs draw heat from the atmosphere into the ocean, leading to increased depth-integrated ocean heat content. The subsurface heat is returned to the surface mixed layer during the autumn and winter as the mixed layer deepens, leading to anomalously warm SSTs and potentially reducing sea ice cover. Observational analyses and numerical experiments support our proposed mechanism, showing that enhanced vertical mixing produces subsurface warming and cools the surface mixed layer. Nevertheless, the dominant driver of surface cooling remains uncertain; the relative importance of advective and mixing contributions to the surface cooling is model dependent. Modeling results suggest that sea ice volume is more sensitive to summertime winds than sea ice extent, implying that enhanced summertime westerly winds may lead to thinner sea ice in the following winter, if not lesser ice extent. Thus, strong summertime winds could precondition the sea ice cover for a rapid retreat in the following melt season.

Southern Ocean↗
NASA NTRSDate not supplied

Towards Real-time Global Assessment of Post-fire Debris Flow Hazard

As the risk of wildfires increases worldwide, burned steeplands are vulnerable to the secondary hazard of widespread sediment mobilization through debris flows. Following an initial burn, sediment and soil previously restrained by vegetation are no longer consolidated, allowing for easy mobilization into channels and along steep hillslopes through runoff. Sufficiently powerful rainfall incorporates entrained material into turbulent flows and serves as the primary trigger for debris flow initiation. There is thus an ongoing need to establish the relationship between rainfall and debris flow initiation based on a variety of spatiotemporal preconditions. Previous work establishes regional and local thresholds to constrain the effect of rainfall in recently burned areas, but no empirical or numerical solution has worldwide application. Building from regionally-based efforts in the U.S., this work considers how remote sensing data can be applied to better approximate the post-fire debris flow hazards worldwide using freely available global datasets and software. Our work assesses the utility of remote sensing resources for analyzing burn characteristics, topography, rainfall intensity/duration, and, thus, debris flow initiation. Early results show that global observations are sufficient to delineate background rainfall rates from storms likely to cause debris flows across a variety of burn severity and topographic conditions. However, the dearth of publicly-available post-fire debris flow inventories globally limit the ability to test how the model framework performs within different climatologic and morphologic areas. This work will present preliminary analysis over the Western United States and demonstrate the feasibility of a global, near-real time model to provide situational awareness of potential hazards within recently burned areas worldwide. Future work will also consider how global or regional precipitation forecasts may increase the lead time for improved early warning of these hazards.

Elijah Orland↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS2022

Test Methods and Analysis of Anomalous Charging Currents in Polymer Tantalum Capacitors

Due to dry environments, anomalous charging currents (ACC) in polymer tantalum capacitors (PTC) could cause malfunctions or failures in space systems. Currently, there is no standard metric to assess this effect, and factors affecting ACC are not well understood. This paper discusses the benefits and drawbacks of different methods used to reveal ACC including constant voltage ramp and constant current stress and suggest a power surge testing (PST) as a procedure for screening and qualification of PTCs for space applications. The suggested test is similar to the surge current testing that is currently used for MnO2 tantalum capacitors but assures dissipation of high power in the part throughout testing. Using various types of capacitors, the reproducibility of test results for different manufacturing lots of PTCs and from sample to sample within a lot were estimated. The impact of moisture content, test temperature, stress voltages, and preconditioning were assessed. Thermal effects associated with ACC and the possibility of catastrophic failures were studied experimentally using an IR camera and calculated at adiabatic heating conditions. Possible mechanisms of the phenomenon are discussed and recommendations for testing to avoid failures related to ACC are suggested.

Alexander A.Teverovsky↗
NASA NTRS2022

Power Surge Testing for Polymer Tantalum Capacitors

Due to dry environments, anomalous charging currents (ACC) in polymer tantalum capacitors (PTC) might cause malfunctions and failures in space systems. Currently, there is no standard metrics to assess this effect and factors affecting ACC are not well understood. This paper discusses benefits and drawbacks of different methods used to reveal ACC and suggest a power surge testing (PST) as a procedure for screening and qualification of PTCs. The suggested test is similar to the surge current testing that is currently used for MnO2 tantalum capacitors but assures dissipation of high power in the part during the whole period of testing. Using various types of capacitors, the reproducibility of test results for different lots of PTCs and from sample to sample in a lot were estimated. The impact of moisture content, test temperature, stress voltages, and preconditioning is assessed. Thermal effects associated with ACC and the possibility of catastrophic failures were studied experimentally using an IR camera and calculated at adiabatic heating conditions. Possible mechanisms of the phenomenon are discussed and recommendations for testing to avoid failures related to ACC are suggested.

Alexander A Teverovsky↗
NASA NTRSDate not supplied

Revisiting the Causes of the US Midwest Great Flood of 1993

The floods that occurred in the Midwest during the summer of 1993 remain one of the greatest flooding events in US history. While a considerable amount of work has already been done addressing its causes there still remain uncertainties as to the relative roles of forcing from the tropics and the extra-tropics as well as the extended nature of the excessive rainfall that lasted throughout much of the late spring and summer of that year. Here we use a novel “replay” technique in which the GEOS AGCM is constrained to remain close to the MERRA-2 reanalysis over specified regions of the globe to isolate the causes of the excessive rain. We show the main rainfall event in the upper Midwest in July (extending into August) was the result of the development of a large amplitude Rossby wave (and associated pronounced moisture influx from the Gulf of Mexico) apparently forced by anomalous transient eddies in the western North Pacific. Rainfall that fell in the Midwest during the two months prior to that, while not as intense, acted to precondition the soil, with forcing from the tropics contributing to the May precipitation, while the excessive Midwest rain that fell during June was part of a record hemispheric-wide precipitation signal that appears to be linked to an unusually cold north polar stratosphere. The nature of the stratospheric impact and the possible delayed influence of the Pinatubo eruption is currently being assessed.

Midwest↗
NASA NTRS2021

Lila: Optimal Dispatching in Probabilistic Temporal Networks using Monte Carlo Tree Search

Executing a Probabilistic Simple Temporal Network (PSTN) amounts at scheduling, i.e. \textit{dispatch}, a set of events under time uncertainty. This constitutes a NP-hard online optimization problem. The right execution time must be dynamically assigned to each event of the PSTN such that the temporal constraints are met, whereas activity durations are progressively observed as the execution unfolds. We propose a dispatching algorithm based on Monte Carlo Tree Search, called Lila, with the following characteristics: (i) it is an anytime algorithm, both offline and online, proven asymptotically optimal; (ii) it returns the current probability of success, either before or at any moment during operations; (iii) it handles any possible continuous or discrete, even non-parametric, probability distributions, as well as inter-dependencies between random variables, exogenous and endogenous uncertainty; and (iv) can be easily extended to handle probabilistic external events, PSTNs with resources, PSTNs with cutoff times and precondition chains, etc. Lila is universal in the sense that it can handle any dispatching protocol, simply by specifying it to the algorithm. It has the unlimited flexibility offered by the simulation paradigm, whilst it asymptotically converges to optimal decisions and/or robustness approximations.

Chien, Steve A.↗
NASA NTRS2022

Understanding Structure in Line-Driven Stellar Winds Using Ultraviolet Spectropolarimetry in the Time Domain

The most massive stars are thought to lose a significant fraction of their mass in a steady wind during the main-sequence and blue supergiant phases. This in turn sets the stage for their further evolution and eventual supernova, and preconditions the surrounding medium for all following events, with consequences for ISM energization, chemical enrichment, and dust formation. Understanding these processes requires accurate observational constraints on the mass-loss rates of the most luminous stars, which can also be used to test theories of stellar wind driving. In the past, mass-loss rates have been characterized via collisional emission processes such as optical Hα and free-free radio emission, but these so-called “density squared” diagnostics require correction in the presence of widespread clumping. Recent observational and theoretical evidence points to the likelihood of a ubiquitously high level of such clumping in hot-star winds, but quantifying its effects requires a deeper understanding of the complex dynamics of radiatively driven winds and their stochastic instabilities. Furthermore, large-scale structures initiating in surface anisotropies and propagating throughout the wind can also affect wind driving and alter mass-loss diagnostics. Time series spectroscopy of high resonance-line opacity in the UV, capable of high resolution and high signal-to-noise, are required to better understand these complex dynamics, and more accurately determine mass-loss rates. The proposed Polstar mission (Scowen et al. 2022, this volume) provides the necessary resolution at the Sobolev (∼10 km s −1 ) or sound-speed (∼20 km s −1 ) scale, for over three dozen bright galactic massive stars with signal-to noise an order of magnitude above that of the celebrated MEGA campaign (Massa et al. 1995) of the International Ultraviolet Explorer (IUE), via continuous observations that track propagating structures through the winds in real time. Supporting geometric constraints are provided by the polarimetric capabilities present in all the datasets of such a mission.

Polstar – NASA MIDEX↗
NASA NTRS2022

The Polstar High Resolution Spectropolarimetry MIDEX Mission

The Polstar mission will provide a space-borne 60 cm spectropolarimeter operating at ultraviolet (UV) wavelengths, capturing all four Stokes parameters (intensity, two linear polarization components, and circular polarization). Polstar’s capabilities are designed to meet its goal of determining how circumstellar gas flows alter and inform massive star evolution, affect the stellar remnant population, and stir and enrich the interstellar medium (ISM). These will be achieved by investigating the dynamical geometries in the winds and disks of hot stars, the composition and magnetic alignment of interstellar dust, and the star-forming accretion disks of UV-bright stars at an important transition boundary. Together these areas map out a kind of two-way interface between massive stars and their effect on our galaxy, wherein the stellar winds enrich the ISM with metals and kinetic energy, preconditioning their environment and the stellar endpoints prior to undergoing supernova. The ISM dust in turn reveals the composition and magnetic environment leading to new star formation, and the accretion disks of Herbig Ae/Be stars reveal how the ISM gas returns to make new massive stars. Polstar will combine high-resolution spectroscopy in the time domain with high-precision UV polarimetry. Doppler-shifted UV resonance line opacity will provide information about circumstellar kinematics, while polarization gives complementary geometric information about unseen structures. The composition and magnetic alignment of the smallest interstellar dust grains provides a probe of the ISM utilizing radiative alignment theory (RAT). Polstar will operate in the far-UV (FUV) at 122–200 nm at high spectral resolution of around R ∼ 30k, and at FUV and near-UV (NUV) wavelengths of 122–320 nm at lower spectral resolutions of 0.1 - 1k. Detection of polarization levels as weak as 0.1% are expected, with a temporal cadence ranging from 5–10 minutes for most wind variability studies, to hours or days for sampling rotation, to days or weeks for sampling binary orbits, to months to a year for sampling substructure in the inner regions of protoplanetary disks. Sub-meter-class aperture is well suited to access this wide array of time domain science, made possible by restricting to a few hundred bright, massive stars, necessarily extincted by a small to moderate column of interstellar dust, informing both the attributes of the stars and the ISM through which they are seen. As such, the focus is on our own galaxy and its evolutionary drivers, but a few targets in the Magellanic clouds offer the potential to extend this understanding to low-metallicity environments.

Paul A Scowen↗
NASA NTRS2023

Recurrent 3he-Rich Solar Energetic Particle Injections Observed By Solar Orbiter at ∼0.5 Au

We report Solar Orbiter observations of six recurrent solar energetic particle injections in 2022 March 3−6 at ∼0.5 au. All but one were associated with jets emanating from a plage near a large sunspot in active region 12 957. We saw large jets in injections with high 3He and Fe enrichments and minor jets in injections with no or lower enrichments. Furthermore, the event with the highest enrichment showed a more compact configuration of the underlying photospheric magnetic field. The higher fluences as well as harder spectra were seen in the event with a simultaneous jet and wider eruption. However, in this case, the energy buildup time in the source might be required to produce such spectra. Extreme-ultraviolet images from Solar Orbiter revealed a number of intersecting loops at the base of jets not seen from 1 au that might be a precondition for the recurrent events.

Sun: particle emission↗
NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗
NASA NTRS2024

Predictability of the Minimum Sea Ice Extent from Winter Fram Strait Ice Area Export: Model vs Observations

Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous off-shore ice drift along the Eurasian coastline – leading to local ice thickness anomalies at the onset of the melt season – a signal then amplified by the ice-albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE, a proxy for Eurasian coastal divergence) is present in Global Climate Model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counter-intuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large or late in the simulation when the sea ice is thin, and/or when internal variability increases. More importantly, periods with weak correlation between FSIAE and the Sept SIE dominate, suggesting that summer melt processes dominate over late winter preconditioning and May SIT anomalies. In general, we find that the coupling between the FSIAE and ice thickness anomalies along the Eurasian coastline is an ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

off-shore ice drift↗