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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Development of a Deep-Penetrating, Compact Geothermal Heat Flow System for Robotic Lunar Geophysical Missions

Geothermal heat flow measurements are a high priority for the future lunar geophysical network missions recommended by the latest Decadal Survey of the National Academy. Geothermal heat flow is obtained as a product of two separate measurements of geothermal gradient and thermal conductivity of the regolith/soil interval penetrated by the instrument. The Apollo 15 and 17 astronauts deployed their heat flow probes down to 1.4-m and 2.3-m depths, respectively, using a rotary-percussive drill. However, recent studies show that the heat flow instrument for a lunar mission should be capable of excavating a ~3-m deep hole to avoid the effect of potential long-term changes of the surface thermal environment. For a future robotic geophysical mission, a system that utilizes a rotary/percussive drill would far exceed the limited payload and power capacities of the lander/rover. Therefore, we are currently developing a more compact heat flow system that is capable of 3-m penetration. Because the grains of lunar regolith are cohesive and densely packed, the previously proposed lightweight, internal hammering systems (the so-called moles ) are not likely to achieve the desired deep penetration. The excavation system for our new heat flow instrumentation utilizes a stem which winds out of a pneumatically driven reel and pushes its conical tip into the regolith. Simultaneously, gas jets, emitted from the cone tip, loosen and blow away the soil. Lab tests have demonstrated that this proboscis system has much greater excavation capability than a mole-based heat flow system, while it weighs about the same. Thermal sensors are attached along the stem and at the tip of the penetrating cone. Thermal conductivity is measured at the cone tip with a short (1- to 1.5-cm long) needle sensor containing a resistance temperature detector (RTD) and a heater wire. When it is inserted into the soil, the heater is activated. Thermal conductivity of the soil is obtained from the rate of temperature increase during the heating. By stopping during the excavation, it is possible to measure thermal conductivities at different depths. The gas jets are turned off when the penetrating cone reaches the target depth. Then, the stem pushes the needle sensor into the undisturbed soil at the bottom of the hole and carries out a thermal conductivity measurement. When the measurement is complete, the system resumes excavation. RTDs, placed along the stem at short (approx 30 cm) intervals, will monitor long-term temperature stability of the subsurface. Temperature in the shallow subsurface would fluctuate with the diurnal, annual, and precession cycles of the Moon. These thermal waves penetrate to different depths into the regolith. Longterm monitoring of the subsurface temperature would allow us to accurately delineate these cyclic signals and separate them from the signal associated with the outward flow of the Moon s endogenic heat. Further, temperature toward bottom of the 3-m hole should be fairly stable after the heat generated during the excavation dissipates into the surrounding soil. The geothermal gradient may be determined reliably from temperature measurements at the RTDs near the bottom. In order to minimize the heat conduction along the stem from affecting the geothermal gradient measurements, we plan to use low-conductive materials for the stem and develop a mechanism to achieve close coupling between the RTDs and the wall of the excavated hole.

Nagihara, Seiichi↗
DOE OSTI2024

Assessing and benchmarking the fidelity of posterior inference methods for astrophysics data analysis

In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2008

NASA's In Space Propulsion Technology Program Accomplishments and Lessons Learned

NASA's In-Space Propulsion Technology (ISPT) Program was managed for 5 years at the NASA MSFC and significant strides were made in the advancement of key transportation technologies that will enable or enhance future robotic science and deep space exploration missions. At the program's inception, a set of technology investment priorities were established using an NASA-wide, mission-driven prioritization process and, for the most part, these priorities changed little - thus allowing a consistent framework in which to fund and manage technology development. Technologies in the portfolio included aerocapture, advanced chemical propulsion, solar electric propulsion, solar sail propulsion, electrodynamic and momentum transfer tethers, and various very advanced propulsion technologies with significantly lower technology readiness. The program invested in technologies that have the potential to revolutionize the robotic exploration of deep space. For robotic exploration and science missions, increased efficiencies of future propulsion systems are critical to reduce overall life-cycle costs and, in some cases, enable missions previously considered impossible. Continued reliance on conventional chemical propulsion alone will not enable the robust exploration of deep space - the maximum theoretical efficiencies have almost been reached and they are insufficient to meet needs for many ambitious science missions currently being considered. By developing the capability to support mid-term robotic mission needs, the program was to lay the technological foundation for travel to nearby interstellar space. The ambitious goals of the program at its inception included supporting the development of technologies that could support all of NASA's missions, both human and robotic. As time went on and budgets were never as high as planned, the scope of the program was reduced almost every year, forcing the elimination of not only the broader goals of the initial program, but also of funding for over half of the technologies in the original portfolio. In addition, the frequency at which the application requirements for the program changed exceeded the development time required to mature technologies: forcing sometimes radical rescoping of research efforts already halfway (or more) to completion. At the end of its fifth year, both the scope and funding of the program were at a minimum despite the program successfully meeting all of it's initial high priority objectives. This paper will describe the program, its requirements, technology portfolio, and technology maturation processes. Also discussed will be the major technology milestones achieved and the lessons learned from managing a $100M+ technology program.

Johnson, Les C.↗
DOE OSTI2021

Solvation Dynamics of Wet Ethaline: Water is the Magic Component

The past two decades witnessed the development of a new type of solvent system, named deep eutectic solvents, which have become increasingly investigated because they offer new and potentially favorable properties, such as wide tunability in electrochemical, mechanical, and transport properties. Deep eutectic solvent (DES) systems are composed of at least one main solvent and an additional component that is meant to interrupt the original solvent/solvent interactions, thereby introducing lower melting points relative to each individual component. Ethaline (a 1:2 mole% mixture of choline chloride and ethylene glycol) is one of the most promising DES systems. However, it is also known to be very hygroscopic, which is a constant concern because water absorption during the use of ethaline alters its properties. Within this work, we demonstrate that modest amounts of water addition (1-10%) to ethaline are of little concern for practical use and can even lead to performance improvements, such as accelerated relaxation and solvation. In contrast, very small amounts of <1% of water lead to additional slowing of the solvent response. Thus, we suggest that the attempt to dry ethaline below 1% moisture is rather counter-productive if one attempts to achieve effective solvation and charge transport properties from DESs. This study investigates the effect of water content on the diffusional relaxation dynamics of ethaline. A set of independent spectroscopic experiments and computational simulations are aimed to provide insight into the solvent response of the DES system using femtosecond time-resolved absorption spectroscopy (fs-TA), broadband dielectric spectroscopy (BDS), nuclear magnetic resonance (NMR) diffusometry and broadband relaxometry, and molecular dynamics simulations (MDS) on ethaline with 0, 0.1, 1, 10, and 28.5wt% added water. For dry ethaline, we identify choline chloride as the rate-limiting solvation component in ethaline. However, the role of the solvent components changes gradually as water is added. Further, we provide quantitative solvent relaxation rates using the different presented time-resolved spectroscopic techniques and find remarkable agreement between them. Based on the solvent relaxation rates and combined with MDS, we develop a molecular understanding of the individual solvent components and their interactions in dry and wet ethaline with varying amounts of water content.

25 ENERGY STORAGE↗
DOE OSTI2021

Influence of Agricultural Managed Aquifer Recharge (AgMAR) and Stratigraphic Heterogeneities on Nitrate Reduction in the Deep Subsurface

This data package contains input files for TOUGHREACT for a modeling study examining the effects of managed aquifer recharge on agricultural lands on nitrate cycling and transport in the Central Valley of CA near Modesto. The files contain all the geochemical species, reactions, and hydrological parameters for the model. The files are text files used for the TOUGH family of code created by LBNL. To use the files a license is required. https://tough.lbl.gov/licensing-download/toughreact-licensing-download/Accompanying Paper Abstract: Agricultural managed aquifer recharge (AgMAR) is a proposed management strategy whereby surface water flows are used to intentionally flood croplands with the purpose of recharging underlying aquifers. However, legacy nitrate (NO3-) contamination in agriculturally-intensive regions poses a threat to groundwater resources under AgMAR. To address these concerns, we use a reactive transport modeling framework to better understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on N leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. In particular, we examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (~15 m) using variable AgMAR application rates on two-dimensional representations of differently textured soils, soils with discontinuous bands/channels, and soils with preferential flow paths characteristic of typical agricultural field sites. Our results indicate that finer textured sediments, such as silt loams, alone or embedded within high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results further suggest that applying water all-at-once rather than in increments for a fixed volume of recharge transports higher concentrations of NO3- deeper into the profile, which has the potential to exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all-at-once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR management strategies can be designed to enhance denitrification in the subsurface and reduce N leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as riparian corridors, floodplains, wetlands, and other managed landscapes like dedicated recharge basins.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Aerial Vehicles to Detect Maximum Volume of Plume Material Associated with Habitable Areas in Extreme Environments

Current technologies of exploring habitable areas of icy moons are limited to flybys of space probes. This research project addresses long-term navigation of icy moons by developing a MATLAB adjustable trajectory based on the volume of plume material observed. Plumes expose materials from the sub-surface without accessing the subsurface. Aerial vehicles capable of scouting vapor plumes and detecting maximum plume material volumes, which are considered potentially habitable in inhospitable environments, would enable future deep-space missions to search for extraterrestrial organisms on the surface of icy moons. Although this platform is still a prototype, it demonstrates the potential aerial vehicles can have in improving the capabilities of long-term space navigation and enabling technology for detecting life in extreme environments. Additionally, this work is developing the capabilities that could be utilized as a platform for space biology research. For example, aerial vehicles that are sent to map extreme environments of icy moons or the planet Mars, could also carry small payloads with automated cell-biology experiments, designed to probe the biological response of low-gravity and high-radiation planetary environments, serving as a pathfinder for future human missions.

Vehicles Extreme Environment↗
DOE OSTI2021

Deeply Rooted: Evaluating Plant Rooting Depth as a Means for Enhanced Soil Carbon Sequestration (Abbreviated Final Report)

Soils store three times as much carbon (C) as the atmosphere, but are not at capacity, and enhanced soil C storage is considered an essential strategy to mitigate rising atmospheric CO 2 levels. Agricultural soils have experienced substantial C loss in the past century due to poor agricultural practices and erosion. A shift towards deep-rooting crops and low-impact soil management could potentially increase long-term sequestration of C fixed by plants and stored in their root tissues, particularly for crops that have naturally deep root systems (>1 meter). A substantial amount of the CO 2 taken up by plants is allocated to their root systems, and because C deposited in deep soil layers has a longer residence time (up to millennia, in contrast to C deposited in topsoils), C increases at depth may have better long-term C sequestration potential than topsoils. However, the accrual, turnover, and stabilization of C in subsoils is a critical knowledge gap.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Deep-Space Navigation Using Optical Communications Systems

Optical communication links using lasers can potentially deliver data rates much higher than those possible using radio frequencies. If optical communications equipment is going to be carried by future deep-space missions, this equipment, with some adaptations, could also be used to perform tracking for trajectory determination. A number of experiments have been performed in Earth orbit and in lunar orbit using optical data links, while other missions have demonstrated optical links over interplanetary distances. Laser ranging using corner cube retroreflectors is a well-established technique that has been used for orbit determination of Earth orbiting spacecraft, for geodesy, and for lunar research, achieving centimeter-level precisions, but it is not a practical method for deep-space distances. There are two main optical tracking types that are being considered for deep-space navigation. The first is optical astrometry of spacecraft: a telescope on the ground images the laser beam coming from a spacecraft against the star background, determining its plane-of-sky position as seen from the observatory. This type will greatly benefit from the release of the high-accuracy star catalog produced by ESA’s Gaia mission, allowing for the generation of plane-of-sky measurements with an accuracy similar to that obtained today using VLBI tracking techniques. The second is optical ranging using active optical systems at both ends of the link, requiring a more careful design of the spacecraft optical communications system. One of the advantages of using optical frequencies is that they are not affected by charged particles in the signal path the way that radio frequencies are, eliminating solar plasma and ionospheric effects from the light-time calculation and the corresponding noise. On the other hand, clouds would preclude any type of optical communication, and daytime light scattering precludes astrometric measurements. This paper presents our analysis so far of the performance that could be achieved using optical data types in a number of deep-space scenarios. One of the questions that we are trying to answer is whether spacecraft equipped with optical communications terminals would also need to carry radio-frequency equipment for navigational purposes. We also want to understand how accurately we will be able to navigate spacecraft in different mission types and phases, and what would be the constraints, advantages, and disadvantages of using optical communications systems for deep-space navigation.

Karimi, Reza↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Calculations of cosmogenic nuclide production rates in the Earth's atmosphere and their inventories

The production rates of cosmogenic isotopes in the Earth's atmosphere and their resulting terrestrial abundances have been calculated, taking into account both geomagnetic and solar-modulatory effects. The local interstellar flux was assumed to be that of Garcia-Munoz, et al. Solar modulation was accounted for using the heliocentric potential model and expressed in terms of the Deep River neutron monitor count rates. The geomagnetic field was presented by vertical cutoffs calculated by Shea and Smart and the non-vertical cutoffs calculated using ANGRI. The local interstellar particle flux was first modulated using the heliocentric potential field. The modulated cosmic-ray fluxes reaching the earth's orbit then interacted with the geomagnetic field as though it were a high-pass filter. The interaction of the cosmic radiation with the Earth's atmosphere was calculated utilizing the Bolztmann transport equation. Spallation cross sections for isotope production were calculated using the formalism of Silberberg and Tsao and other cross sections were taken from standard sources. Inventories were calculated by accounting from the variation in solar modulation and geomagnetic field strength with time. Results for many isotope, including C-14, Be-7 and Be-10 are in generally good agreement with existing data. The C-14 inventory, for instance, amounts to 1.75/sq cm(e)/s, in excellent agreement with direct estimates.

Obrien, K.↗
NASA NTRS1991

Design of multihundred-watt dynamic isotope power system for robotic space missions

The design of a multihundred-watt dynamic isotope power system (DIPS) based on the US Department of Energy (DOE) general-purpose heat source (GPHS) and small (multihundred-watt) free-piston Stirling engine (FPSE) is described as a potential lower cost alternative to radioisotope thermoelectric generators (RTGs). The design is targeted at the power needs of future unmanned deep space and planetary surface exploration missions. Since the competitive potential of FPSE as an isotope convertor was first identified, work has focused on the feasibility of directly integrating GPHS with the Stirling heater head. Thermal modeling of various radiatively coupled heat source/heater head geometries has been performed using data furnished by the developers of FPSE and GPHS. The analysis indicates that, for the 1050 K heater head configurations considered, GPHS fuel clad temperatures remain safe operating limits under all conditions including shutdown of one engine on a twin engine unit. Based on these results, preliminary characterizations of multihundred-watt units have been established. They indicate that, per electrical watt, the GPHS/small Stirling DIPS will be roughly equivalent to MOD RTG in size and mass but will require about a third the amount of isotope fuel.

Bents, D. J.↗
DOE OSTI2022

Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign

Abstract An aerosol indirect effect on deep convective cores (DCCs), by which increasing aerosol concentration increases cloud-top height via enhanced latent heating and updraft velocity, has been proposed in many studies. However, the magnitude of this effect remains uncertain due to aerosol measurement limitations, modulation of the effect by meteorological conditions, and difficulties untangling meteorological and aerosol effects on DCCs. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in 2018–19 produced concentrated aerosol and cloud observations in a location with frequent DCCs, providing an opportunity to examine the proposed aerosol indirect effect on DCC depth in a rigorous and robust manner. For periods throughout the campaign with well-mixed boundary layers, we analyze relationships that exist between aerosol variables (condensation nuclei concentration > 10 nm, 0.4% cloud condensation nuclei concentration, 55–1000-nm aerosol concentration, and aerosol optical depth) and meteorological variables [level of neutral buoyancy (LNB), convective available potential energy, midlevel relative humidity, and deep-layer vertical wind shear] with the maximum radar-echo-top height and cloud-top temperature (CTT) of DCCs. Meteorological variables such as LNB and deep-layer shear are strongly correlated with DCC depth. LNB is also highly correlated with three of the aerosol variables. After accounting for meteorological correlations, increasing values of the aerosol variables [with the exception of one formulation of aerosol optical depth (AOD)] are generally correlated at a statistically significant level with a warmer CTT of DCCs. Therefore, for the study region and period considered, increasing aerosol concentration is mostly associated with a decrease in DCC depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Surface Pb nanoparticle aggregation, coalescence and differential capacitance in a deep eutectic solvent using a simultaneous sample-rotated small angle x-ray scattering and electrochemical methods approach [Surface Pb nanoparticle aggregation, coalescence and differential capacitance in a deep eutectic solvent using a simultaneous grazing transmission small angle x-ray scattering and electrochemical methods approach]

Nanoparticle electrodeposition is a simple and scalable approach to synthesizing supported nanoparticles. Used with a deep eutectic solvent (DES), surface nanoparticles can be assembled and exhibit unique surface charge separation when the DES is adsorbed on the nanoparticle surface. Key to understanding and controlling the assembly and the capacitance is a thorough understanding of surface particle mobility and charge screening, which requires an in-situ approach. In this study, Pb particle formation, size, shape and capacitance are resolved in a 1:2 choline Cl – : urea deep eutectic solvent whilst sweeping the cell potential in the range: 0.2 V to –1.2 V (vs. Ag/AgCl). These system parameters were resolved using a complementary suite of sample-rotated small angle X-ray scattering (SR-SAXS) and electrochemical impedance spectroscopy (EIS), which are presented and discussed in detail. This approach is able to show that both particle and ion transport are impeded in the DES, as aggregation occurs over the course of 6 minutes, and dissolved Pb ions accumulate and remain near the surface after a nucleation pulse is applied. The DES-Pb interactions strongly depend on the cell potential as evidenced by the specific differential capacitance of the Pb deposit, which has a maximum value of 2.5 +/– 0.5 F g –1 at –1.0 V vs. Ag/AgCl. Together, the SR-SAXS-EIS approach is able to characterize the unique nanoparticle capacitance, mobility and ion mobility in a DES and can be used to study a wide range of nanoparticle deposition systems in-situ .

36 MATERIALS SCIENCE↗
DOE OSTI2013

Homeowner Decision Making and Behavior Relating to Deep Home Retrofits

Existing homes present an enormous, largely untapped potential for energy savings. The U.S. Department of Energy (DOE), utilities, and other organizations around the country have launched many initiatives intended to achieve significant energy savings through home retrofits—or energy-saving home improvements. Retrofit programs vary in their specific objectives, the kind of homes or homeowners targeted, their approaches, the metrics for their “success,” and various measures of their effectiveness. Each initiative explicitly or implicitly reflects ideas or assumptions about “what it takes” for homeowners to participate in energy-saving retrofit programs, go through with audits, install recommended energy saving measures, and/or achieve desired or predicted energy savings. Yet, there is not enough research to identify with certainty the approaches likely to work best for a particular population in a particular setting. The interrelated tendencies to focus on actions homeowners take (e.g., via participation rates) and/or to make assumptions about homeowners’ motivations contributes to this knowledge gap. This report seeks to shrink this knowledge gap by focusing on homeowner decision making. In particular, it focuses on a set of homeowners who agreed to participate in a technical deep-home retrofit project wherein they would implement a variety of measures (at their own personal cost) and have the effectiveness of those measures monitored for a year after installation.

Wolfe, Amy K.↗
DOE OSTI2022

Aerodynamic Analysis of Wind Loading on Parabolic Trough Collectors Using High-Fidelity CFD Modeling

Wind loading on parabolic solar collector support structures is the key driver for material costs. As identified by NREL's CSP Best Practices project [1], ability to predict wind-loading in deep-arrays and the impact of edge effects has potential to decrease levelized cost of electricity (LCoE) by 15%. In this study, we present a high-fidelity computational model that can model the flow around deep arrays of parabolic troughs under varying turbulent inflow conditions. Ability to accurately reproduce atmospheric boundary layer (ABL) profiles is first demonstrated through Large Eddy Simulation (LES) simulations and compared against wind tunnel measurements. Thereafter, unsteady turbulent flow past single row and six row configurations is computed using hybrid RANS/LES simulations. Investigations of wind loading, through parametric tilt angle variations of single row and on waked collectors in six-row configuration, are used to highlight design considerations for parabolic troughs only possible through high-fidelity unsteady calculations.

aerodynamics↗
DOE OSTI2018

On the role of aerosols, humidity, and vertical wind shear in the transition of shallow-to-deep convection at the Green Ocean Amazon 2014/5 site

Abstract. The preconditioning of the atmosphere for a shallow-to-deep convective transition during the dry-to-wet season transition period (August–November) is investigated using Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) GoAmazon2014/5 campaign data from March 2014 to November 2015 in Manacapuru, Brazil. In comparison to conditions observed prior to shallow convection, anomalously high humidity in the free troposphere and boundary layer is observed prior to a shallow-to-deep convection transition. An entraining plume model, which captures this leading dependence on lower tropospheric moisture, is employed to study indirect thermodynamic effects associated with vertical wind shear (VWS) and cloud condensation nuclei (CCN) concentration on preconvective conditions. The shallow-to-deep convective transition primarily depends on humidity, especially that from the free troposphere, which tends to increase plume buoyancy. Conditions preceding deep convection are associated with high relative humidity, and low-to-moderate CCN concentration (less than the 67th percentile, 1274 cm−3). VWS, however, shows little relation to moisture and plume buoyancy. Buoyancy estimates suggest that the latent heat release due to freezing is important to deep convective growth under all conditions analyzed, consistent with potential pathways for aerosol effects, even in the presence of a strong entrainment. Shallow-only convective growth, however, shows an association with a strong (weak) low (deep) level VWS and with higher CCN concentration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Carbon dioxide (CO 2 ) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO 2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO 2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO 2 solubility (ln(x CO 2 )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO 2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO 2 capture and utilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗