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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.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part I: Observational Study

A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented here. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around –18° to –10°C. Further, the case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to –5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II. Significance Statement: Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around –18° to –8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

A Prototype Hail Detection Algorithm and Hail Climatology Developed with the Advanced Microwave Sounding Unit (AMSU)

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus‐7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSR‐E sensor. This lead to climatologies of hail frequency from TMI and AMSR‐E, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSU‐A/B and the MHS on several operational satellites: NOAA‐15 through NOAA‐19; MetOp‐A and ‐B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSR‐E. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU‐based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12‐year period (2000‐2011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 2000‐11 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission.

Ferraro, Ralph↗
NASA NTRS2015

Estimating Planetary Boundary Layer Heights from NOAA Profiler Network Wind Profiler Data

An algorithm was developed to estimate planetary boundary layer (PBL) heights from hourly archived wind profiler data from the NOAA Profiler Network (NPN) sites located throughout the central United States. Unlike previous studies, the present algorithm has been applied to a long record of publicly available wind profiler signal backscatter data. Under clear conditions, summertime averaged hourly time series of PBL heights compare well with Richardson-number based estimates at the few NPN stations with hourly temperature measurements. Comparisons with clear sky reanalysis based estimates show that the wind profiler PBL heights are lower by approximately 250-500 m. The geographical distribution of daily maximum PBL heights corresponds well with the expected distribution based on patterns of surface temperature and soil moisture. Wind profiler PBL heights were also estimated under mostly cloudy conditions, and are generally higher than both the Richardson number based and reanalysis PBL heights, resulting in a smaller clear-cloudy condition difference. The algorithm presented here was shown to provide a reliable summertime climatology of daytime hourly PBL heights throughout the central United States.

Wind Profiler↗
DOE OSTI2024

(Doublon) Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed, such as the Transient Heatsink Overpower Response capsule (THOR), the Transient Water Irradiation System for TREAT (TWIST), and Sirius. Each has provided valuable data for materials performance and reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments can cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used for code verification, validation and uncertainty quantification. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the verification, validation and uncertainty quantification approach. To overcome this problem, the autocorrected reactimeter algorithm (ACRA) has been developed. This approach infers a time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e., when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for developing an improved version of the ACRA that does not require frozen configurations. To develop this new algorithm, we need a precise and unbiased implementation of the inverse point kinetic equations (IPKEs) as any error in the reactivity evaluation will be propagated into the choice of the optimal spatial correction. Indeed, the previous reactimeter algorithm would use approximations, such as a negligible flux amplitude derivative, to focus on rapidity. For the numerical validation of ACRA, we aim at absolute error under for reactivity derived from signals similar to the one of this study. In this summary, we test eight different IPKE implementations. Each will process a mockup signal built for this study, similar to those that the future ACRA will process. Each reactivity output will be compared to the reference reactivity that has been used to generate the mockup signal. The implementation minimizing the difference with the reference reactivity will be used in the development of a new ACRA formulation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1988

The cryptoendolithic microbial environment in the Ross Desert of Antarctica: mathematical models of the thermal regime

Microbial activity in the Antarctic cryptoendolithic habitat is regulated primarily by temperature. Previous field studies have provided some information on the thermal regime in this habitat, but this type of information is limited by the remoteness of the site and the harsh climatic conditions. Therefore, a mathematical model of the endolithic thermal regime was constructed to augment the field data. This model enabled the parameters affecting the horizontal and altitudinal distribution of the community to be examined. The model predicts that colonization should be possible on surfaces with zenith angle less than 15 degrees. At greater zenith angles, colonization should be restricted to surfaces with azimuth angles less than 135 degrees or greater than 225 degrees. The upper elevational limit of the community should be less than 2,500 m. The thermal regime probably does not influence the zonation of the community within a rock.

Non-NASA Center↗
NASA NTRS2019

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SSS↗
NASA NTRS2024

Accurate Infrared Line Lists for 20 Isotopologues of CS 2 at Room Temperature

To facilitate atmospheric and spectroscopic studies of carbon disulfide, or CS 2 , in both planetary and exoplanetary atmospheres, we adopt the “Best Theory + Reliable High-resolution Experiment” algorithm to generate semiempirical IR line lists for the 20 most abundant CS 2 isotopologues, denoted as Ames-296K. The IR lists are computed using the Ames-1 potential energy surface, refined using the experimental transition set and an ab initio dipole moment surface fitted from CCSD(T)/aug-cc-pV(T/Q/5+d)Z dipoles extrapolated to a one-particle basis set limit. The IR lists cover the range of 0–10,000 cm -1 , with an S 296K cutoff at 10 -31 cm -1 /molecule·cm -2 (abundance included). A “natural” IR line list at 296 K includes about 10 million lines of the 20 isotopologues, with their intensities scaled by the corresponding abundances. The zero-point energy, partition functions, and abundances are reported for each isotopologue. The energy levels in the global effective Hamiltonian model for 12 C 32 S 2 are adopted to improve the line position accuracy. This new IR list for the main isotopologue is denoted as A+I.296K. Reliable HITRAN2020 line positions are also utilized to improve the accuracy of the 32 S 12 C 34 S, 32 S 12 C 33 S, and 32 S 13 C 32 S isotopologue line lists. The final composite line list is validated against Pacific Northwest National Laboratory experimental cross sections, showing excellent agreement. The agreement supports the quality of the composite line list and the power of synergy between experiment and theory. The new data are proposed for use in updating and expanding the CS 2 data in HITRAN and other high-resolution IR databases. Supplementary files are available in Zenodo and AHED.

Exoplanet atmospheric composition↗
DOE OSTI2023

Hierarchical Modeling to Enhance Spectrophotometry Measurements—Overcoming Dynamic Range Limitations for Remote Monitoring of Neptunium

A robust hierarchical model has been demonstrated for monitoring a wide range of neptunium concentrations (0.75–890 mM) and varying temperatures (10–80 °C) using chemometrics and feature selection. The visible–near infrared electronic absorption spectrum (400–1700 nm) of monocharged neptunyl dioxocation (Np(V) = NpO2+) includes many bands, which have molar absorption coefficients that differ by nearly 2 orders of magnitude. The shape, position, and intensity of these bands differ with chemical interactions and changing temperature. These challenges make traditional quantification by univariate methods unfeasible. Measuring Np(V) concentration over several orders of magnitude would typically necessitate cells with varying path length, optical switches, and/or multiple spectrophotometers. Alternatively, the differences in the molar extinction coefficients for multiple absorption bands can be used to quantify Np(V) concentration over 3 orders of magnitude with a single optical path length (1 mm) and a hierarchical multivariate model. In this work, principal component analysis was used to distinguish the concentration regime of the sample, directing it to the relevant partial least squares regression submodels. Each submodel was optimized with unique feature selection filters that were selected by a genetic algorithm to enhance predictions. Through this approach, the percent root mean square error of prediction values were ≤1.05% for Np(V) concentrations and ≤4% for temperatures. This approach may be applied to other nuclear fuel cycle and environmental applications requiring real-time spectroscopic measurements over a wide range of conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Progress in AMSR Snow Algorithm Development

Advanced Microwave Scanning Radiometer (AMSR) will be flown on-board of the Japanese Advanced Earth Observing Satellite-II (ADEOS-II) and United States Earth Observation System (EOS) PM-1 satellite. AMSR is a passive microwave radiometer with frequency ranges from 6.9 GHz to 89 GHz. It scans conically with a constant incidence angle of 55 deg at the Earth's surface. The swath width is about 1600 km. With a large antenna, AMSR will provide the best spatial resolution of multi-frequency radiometer from space. This provides us an opportunity to improve the snow parameter retrieval. Accurate determination of snow parameters from space is a challenging effort. Over the years, many different techniques have been used to account for the complicated snow parameters such as the density, stratigraphy, snow grain size, temperature variation of the snow-pack. Forest type, fractional forest cover and land use type also need to be considered in developing an improved retrieval algorithm. However, snow is such a dynamic variable, snow-pack parameter keeps changing once the snow is deposited on the earth surface. Currently, NASDA and NASA are developing AMSR snow retrieval algorithms. These algorithms are now being carefully tested and evaluated using the SSM/I data. Due to limited snow-pack data available for comparison, this activity is progressing slowly. However, it is clear that in order to improve the snow retrieval algorithm, it is necessary to model the metamorphism history of the snow-pack.

Chang, Alfred↗
NASA NTRS2006

JMR Noise Diode Stability and Recalibration Methodology after Three Years On-Orbit

The Jason Microwave Radiometer (JMR) is included on the Jason-1 ocean altimeter satellite to measure the wet tropospheric path delay (PD) experienced by the radar altimeter signal. JMR is nadir pointing and measures the radiometric brightness temperature (T(sub B)) at 18.7, 23.8 and 34.0 GHz. JMR is a Dicke radiometer and it is the first radiometer to be flown in space that uses noise diodes for calibration. Therefore, monitoring the long term stability of the noise diodes is essential. Each channel has three redundant noise diodes which are individually coupled into the antenna signal to provide an estimate of the gain. Two significant jumps in the JMR path delays, relative to ground truth, were observed around 300 and 700 days into the mission. Slow drifts in the retrieved products were also evident over the entire mission. During a recalibration effort, it was determined that a single set of calibration coefficients was not able to remove the calibration jumps and drifts, suggesting that there was a change in the hardware and time dependent coefficients would be required. To facilitate the derivation of time dependent coefficients, an optimal estimation based calibration system was developed which iteratively determines that set of calibration coefficients which minimize the RMS difference between the JMR TBs and on-Earth hot and cold absolute references. This optimal calibration algorithm was used to fine tune the front end path loss coefficients and derive a time series of the JMR noise diode brightness temperatures for each of the nine diodes. Jumps and drifts, on the order of 1% to 2%, are observed among the noise diodes in the first three years on-orbit.

Jason 1↗
DOE OSTI2021

Model-based real-time surface heat flux and temperature estimation for the DIII-D tokamak

A control-oriented model for monitoring of wall power flux densities on the DIII-D tokamak has been successfully implemented and validated experimentally. Future reactors will have to withstand severe steady state high heat flux loads on plasma-facing components (PFCs). Due to the difficulty of directly-measuring local heat fluxes on these components, monitoring and protection of PFCs during the plasma discharge can benefit from simplified physics-based real-time (RT) functional models to estimate and guide heat load control. As a first step into the development, a control-oriented model for monitoring of wall power flux densities and temperatures on DIII-D tokamak has been successfully implemented. The paper discusses the experimental demonstration and comparison of the 2-D model-based wall heat flux algorithm on the DIII-Dinner wall limiter (IWL) against infra-red camera heat flux measurements for limited plasma configurations. The paper also reports on the benchmarking of the field line tracing environment, SMITER, developed at ITER organization on DIII-D tokamak against experimental IR diagnostic data and the derivation of the component shaping weighting factors for the 2-D model-based approach. Here, the extension of the model-based approach for surface temperature estimation on the DIII-D IWL is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus on how to protect the CNN inference process against soft errors as efficiently as possible, with the following three contributions. (1) We propose several systematic ABFT schemes based on checksum techniques and analyze their fault protection ability and runtime thoroughly. Unlike traditional ABFT based on matrix-matrix multiplication, our schemes support any convolution implementations. (2) We design a novel workflow integrating all the proposed schemes to obtain a high detection/correction ability with limited total runtime overhead. (3) We perform our evaluation using ImageNet with well-known CNN models including AlexNet, VGG-19, ResNet-18, and YOLOv2. Here, experimental results demonstrate that our implementation can handle soft errors with very limited runtime overhead (4%~8% in both error-free and error-injected situations).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗
DOE OSTI2018

Experimental realization of Feynman’s ratchet

Feynman's ratchet is a microscopic machine in contact with two heat reservoirs, at temperatures T A and T B , that was proposed by Richard Feynman to illustrate the second law of thermodynamics. In equilibrium (T A = T B ), thermal fluctuations prevent the ratchet from generating directed motion. When the ratchet is maintained away from equilibrium by a temperature difference (T A ≠T B ), it can operate as a heat engine, rectifying thermal fluctuations to perform work. While it has attracted much interest, the operation of Feynman's ratchet as a heat engine has not been realized experimentally, due to technical challenges. In this work, we realize Feynman's ratchet with a colloidal particle in a one dimensional optical trap in contact with two heat reservoirs: one is the surrounding water, while the effect of the other reservoir is generated by a novel feedback mechanism, using the Metropolis algorithm to impose detailed balance. We verify that the system does not produce work when T A = T B , and that it becomes a microscopic heat engine when T A ≠T B . We analyze work, heat and entropy production as functions of the temperature difference and external load. As a result, our experimental realization of Feynman's ratchet and the Metropolis algorithm can also be used to study the thermodynamics of feedback control and information processing, the working mechanism of molecular motors, and controllable particle transportation.

42 ENGINEERING↗
NASA NTRS2004

NASA Tech Briefs, April 2004

Topics covered include: Analysis of SSEM Sensor Data Using BEAM; Hairlike Percutaneous Photochemical Sensors; Video Guidance Sensors Using Remotely Activated Targets; Simulating Remote Sensing Systems; EHW Approach to Temperature Compensation of Electronics; Polymorphic Electronic Circuits; Micro-Tubular Fuel Cells; Whispering-Gallery-Mode Tunable Narrow-Band-Pass Filter; PVM Wrapper; Simulation of Hyperspectral Images; Algorithm for Controlling a Centrifugal Compressor; Hybrid Inflatable Pressure Vessel; Double-Acting, Locking Carabiners; Position Sensor Integral with a Linear Actuator; Improved Electromagnetic Brake; Flow Straightener for a Rotating-Drum Liquid Separator; Sensory-Feedback Exoskeletal Arm Controller; Active Suppression of Instabilities in Engine Combustors; Fabrication of Robust, Flat, Thinned, UV-Imaging CCDs; Chemical Thinning Process for Fabricating UV-Imaging CCDs; Pseudoslit Spectrometer; Waste-Heat-Driven Cooling Using Complex Compound Sorbents; Improved Refractometer for Measuring Temperatures of Drops; Semiconductor Lasers Containing Quantum Wells in Junctions; Phytoplankton-Fluorescence-Lifetime Vertical Profiler; Hexagonal Pixels and Indexing Scheme for Binary Images; Finding Minimum-Power Broadcast Trees for Wireless Networks; and Automation of Design Engineering Processes.

Source record↗
NASA NTRS1979

Finite element methodology for transient conduction/forced-convection thermal analysis

Finite element methodology for steady state thermal analysis of convectively cooled structures has been extended for transient analysis. The finite elements are based on representing the fluid passages by fluid bulk-temperature nodes and fluid-solid interface nodes. The formulation of the finite element equations for a typical flow passage is based on the weighted residual method with upwind weighting functions. Computer implementation of the convective finite element methodology using explicit and implicit time integration algorithms is described. Accuracy and efficiency of the methodology is evaluated by comparisons with analytical solutions and finite-difference lumped-parameter analyses. The comparative analyses demonstrate that finite element conduction/conduction methodology may be used to predict transient temperatures with an accuracy equal or superior to the lumped-parameter finite-difference method.

Thornton, E. A.↗
DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗
NASA NTRS1986

A two-dimensional model of the ionosphere of Venus - Thermal structure

A spectral model is defined for the electron and ion temperature profiles of the Venus ionosphere. The model is developed using data collected with the retarding potential analyzer on the Pioneer Venus Orbiter, and account is taken of horizontal bulk heat transport and a heat flux saturation effect. Coupled ion and energy equations and thermal flux equations are defined. A finite difference algorithm is applied to solve the equations, assuming an ionopause at 740 km altitude. Horizontal plasma flow velocities of 2.5-5.6 km/sec are found necessary in order to account for a dip in the ion temperature around the terminator and a sharp rise at about 140 deg, i.e., far past the solar zenith angle. An external heat source of 0.0001-0.0002 ergs/sq cm per sec, uniformly distributed around Venus, is required to maintain observed dayside and nightside ion temperatures. The heat source may be the solar wind, which would be sufficient without shocks.

Singhal, R. P.↗