Results for “balanced clustering” — Page 11

DOE OSTI2022

Effects of warming on bacterial growth rates in a peat soil under ambient and elevated CO 2

Boreal peatlands are important global carbon reservoirs that are particularly vulnerable to predicted climate changes such as increasing CO 2 and temperature. Since microbial activities regulate the balance of carbon sequestered into soil organic matter or remineralized to CO 2 , characterizing their response to these environmental factors is critical to predicting how peatland ecosystems will affect climate-carbon cycle feedbacks. Here we examined in-situ taxon-specific variation in microbial growth under long-term elevated CO 2 and across a gradient of warming treatments in a northern Minnesota peat bog using quantitative stable isotope probing with 18 O-water. Across temperatures, bacterial taxa were grouped according to the excess atom fraction 18 O (EAF) of their genomes, a proxy for DNA replication and hence, growth. Taxon-specific growth across CO 2 and temperature treatments clustered into relatively few response patterns. While a large portion of taxa showed little to no growth under ambient CO 2 , many of the same taxa grew rapidly under elevated CO 2 . We found support for phylogenetic conservation of response patterns among Acidobacteria and Proteobacteria, the two most abundant phyla in our data. Our results suggest certain taxa may be primed for new climate conditions and have a greater influence on carbon cycling with implications for future climate mitigation strategies.

16S amplicon sequencing, Carbon Dioxide (CO2), pea↗

DOE OSTI2025

Identifying High Ionic Conductivity Compositions of Ionic Liquid Electrolytes Using Features of the Solvation Environment

Binary mixtures of ionic liquids with molecular solvents are gaining interest in electrochemical applications due to the improvement in their performance over neat ionic liquids. Dilution with suitable molecular solvents can reduce the viscosity and facilitate faster diffusion of ions, thereby yielding substantially higher ionic conductivity than that for a pure ionic liquid. Although viscosity and diffusion coefficients typically behave as monotonic functions of concentration, ionic conductivity often passes through a peak value at an optimum molar ratio of the molecular solvent to the ionic liquid. The ionic conductivity maximum is generally explained in terms of a balance between the ease of charge transport and the concentration of the charge carriers. In this work, fluctuation in the local environment surrounding an ion is invoked as a plausible explanation for the ionic conductivity mechanism with a binary mixture of 1-ethyl-3-methylimidazolium tetrafluoroborate and ethylene glycol as an example. The magnitude of the dynamism in the local environment is captured by measuring the spatial and temporal features of the solvation environment. Standard deviation in the number of ions in the solvation environment serves as a spatial feature, while the cage correlation lifetimes for oppositely charged ions within the first solvation shell serve as a temporal feature. Large standard deviations in the cluster ion population and short cage correlation lifetimes are indicators of highly dynamic ionic environment at the molecular level and consequently yield high ionic conductivity. Such compositions were found to be in good agreement with the optimum ionic liquid mole fractions obtained through experimental measurement. Short cage correlation lifetimes enable the identification of optimum mixture compositions using simulation trajectories significantly shorter than those required to implement the Nernst–Einstein or Einstein formalisms for calculating ionic conductivity. We validated the applicability of this approach across force fields and in six ionic liquid-molecular solvent electrolytes formed with combination of cations, anions, and solvents. We offer a computationally efficient approach of screening ionic liquid-molecular solvent binary mixture electrolytes to identify molar ratios that yield high ionic conductivity.

25 ENERGY STORAGE↗

DOE OSTI2026

Mesoscale Organization in Cumulus-Coupled Stratocumulus

Marine cloud systems cover a substantial portion of the world’s oceans. Most of these clouds form relatively close to the ocean surface, typically within one to two kilometers, a region referred to by meteorologists as the marine boundary layer. They are composed predominantly of liquid water, although ice particles can occur in mid- and high-latitude marine clouds during winter. In satellite imagery, these clouds appear bright against the darker ocean surface below, reflecting a large fraction of incoming sunlight back into space that would otherwise warm the ocean. Because marine boundary layer clouds cover such an extensive area of the ocean, they exert a significant influence on Earth’s overall transfer of solar energy absorbed by the surface and thermal energy emitted to space, a balance known as the planetary radiation budget. Marine boundary layer clouds are typically thin, and their formation and dissipation depend on a delicate balance between processes acting at the ocean surface below and the warm, dry air above. They are notoriously difficult to simulate accurately in weather forecast models, which often produce too few marine low clouds in midlatitudes and clouds in tropical regions that are excessively bright, meaning they reflect too much solar radiation. The marine boundary layer is frequently characterized by widespread overcast cloud cover that often transitions from a continuous, single-layer deck to more broken cloud fields toward the tropics. These transitions typically proceed through an intermediate stage in which shallow, broken clouds form beneath the overlying stratiform cloud deck. Once broken clouds develop below the overcast, they frequently self-organize into cloud clusters known as marine boundary layer convective complexes (MBLCCs), although the mechanisms governing the formation and organization of MBLCCs remain poorly understood. Accurately representing these transitions in long-range weather forecast models is essential because they influence the properties of air masses advected over the continental United States and Europe, and they become increasingly important for forecasts on seasonal and longer timescales. We employed two complementary approaches to investigate the processes controlling MBLCCs and their impact on marine cloud cover. Long-term observations from the U.S. Department of Energy’s Eastern North Atlantic (ENA) Observatory provided a unique dataset that allowed us to characterize fundamental properties of MBLCCs, including their typical size and frequency of occurrence. These observations were combined with high-resolution numerical simulations performed on supercomputers to examine the evolution of MBLCCs during cold-air outbreaks over the ENA region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Mixed Nd 4+/3+ and Cluster Magnetism in Hexagonal Perovskite 12R-Ba 4 NdMn 3 O 12−γ

Hexagonal perovskite oxides with 12R stacking host well-separated face-sharing octahedral metal trimers with short metal–metal distances, leading in some cases to large degrees of magnetic frustration and cluster magnetism. Introducing magnetic ions in proximity to these trimers can influence the degree of frustration and cluster magnetism, but the extent to which the magnetism can be tuned by varying neighboring metal cations remains an open question. In this work, we test the impacts of using Nd in proximity to Mn trimers in the hexagonal perovskite 12R-Ba 4 NdMn 3 O 12−γ . Given this stoichiometry, Nd should assume the 4+ oxidation state with a spin state of S = 1, which would be the first realization of Nd 4+ in an oxide environment. Through detailed bulk magnetic, X-ray absorption spectroscopic, and powder neutron diffraction (PND) measurements, we find that Nd 4+ is realized in this material, but there is also partial reduction to Nd 3+ , which is charge-balanced by O vacancies. Magnetometry measurements indicate an antiferromagnetic ordering temperature T N ≈ 16 K, and PND measurements reveal a surprising collinear antiferromagnetic structure with magnetic space group Pc2/m (no. 10.49 in BNS notation), which has not previously been seen in this class of materials. Our results represent a comprehensive analysis of the structural, electronic, and magnetic properties of 12R-Ba 4 NdMn 3 O 12−γ , showing the first observation of partial Nd 4+ in an oxide, and demonstrating that this structural class can host a broad range of magnetic structures which are not easily predicted based on compositional trends.

14 SOLAR ENERGY↗

NASA NTRS2018

The Diversity of Refractory Organic Material in Comets

Organic matter exists in comets (most notably in 81P/Wild 2 [Stardust], 67P/Churyomov- Gerasimenko (67P/C-G) [Rosetta], chondritic porous IDPS, and UCAMMs) and in primitive carbonaceous chondrites that likely retain some chemistry that reflects an origin in the prenatal cold molecular cloud (Alexander+2017). Heavy isotopic enrichments, 15N/14N and possibly D/H, signify preserved molecular cloud organics. In the cold outer disk, if grains are lofted above the disk mid-plane then organics likely experience significant UV processing (Ciesla+2012). In remote sensing of comet comae, organics in the dust are considered refractory or semirefractory. Semi-refractory organics have limited comae lifetimes and produce distributed sources of molecules (H2CO and CO). Rosetta's close passes of 67P/C-G's nucleus (10-15 km) reveals a distributed source of glycine, methyl amine and ethylamine (Altwegg+2016). Cometary samples and primitive meteorites have two types of organic matter: (1) acid-insoluble organic matter (IOM), which is a macromolecular polymer with a mixture of aromatic and aliphatic moieties, and (2) labile, soluble organics, which includes the amino acids, such as glycine (Stardust, Elsila+2009). Meteoritic IOM is robust, withstanding experimental temperatures of 1200 K (Dobrica+2011, Cody+2008). Nanoglobules are a type of IOM; they have a distinct physical structure, but often share the same chemistry as the other IOM from the same meteorite. Moderate-sized PAHs (20 C-atoms) are detected in Stardust samples (Clemett+2010). Refractory organic IOM is ubiquitous yet has a great diversity of abundances between cometary samples. IOM is in primitive chondrites, 67P/C-G (Rosetta), 81P/Wild 2 (Stardust), 1P/Halley, 26P/Grigg-Skjellerup, UCAMMs, anhydrous IDPs, and in chondritic porous IDPs (CP IDPs) and larger cluster IDPs (e.g., Fray+2016, Fomenkova+94, Busemann+ 09, Dobrica+2011, Dobrica+2012). 81P's refractory organic matter is of two types (De Gregorio+2011): nanoglobules of highly aromatic refractory organic matter and polyaromatic carbonyl-containing organic matter, which is similar to IOM in primitive meteorites and IDPs. Fray+2017 estimate that 50% of carbon in 67P/C-G is in IOM. 67P/C-G's organics appear to lack the soluble organic matter, aliphatic carbon, amino acids, and PAHs (Fray+2016). Other notable aspects of the diversity in IOM in cometary samples are the ranges of atomic ratios of N/C, O/C, and H/C, and the range of isotopic enrichments of 15N/14N and D/H. Aqueous and thermal processing on asteroids changes the balance of soluble to insoluble organics, and may be important for diversifying the range of OM delivered to Earth.

Wooden, Diane↗

DOE OSTI2025

Baryon fraction from the BAO amplitude: a consistent approach to parameterizing perturbation growth

Galaxy clustering constrains the baryon fraction Omega_b/Omega_m through the amplitude of baryon acoustic oscillations and the suppression of perturbations entering the horizon before recombination. This produces a different pre-recombination distribution of baryons and dark matter. After recombination, the gravitational potential responds to both components in proportion to their mass, allowing robust measurement of the baryon fraction. This is independent of new-physics scenarios altering the recombination background (e.g. Early Dark Energy). The accuracy of such measurements does, however, depend on how baryons and CDM are modeled in the power spectrum. Previous template-based splitting relied on approximate transfer functions that neglected part of information. We present a new method that embeds an extra parameter controlling the balance between baryons and dark matter in the growth terms of the perturbation equations in the CAMB Boltzmann solver. This approach captures the baryonic suppression of CDM prior to recombination, avoids inconsistencies, and yields a clean parametrization of the baryon fraction in the linear power spectrum, separating out the simple physics of growth due to the combined matter potential. We implement this framework in an analysis pipeline using Effective Field Theory of Large-Scale Structure with HOD-informed priors and validate it against noiseless LCDM and EDE cosmologies with DESI-like errors. The new scheme achieves comparable precision to previous splitting while reducing systematic biases, providing a more robust way to baryon-fraction measurements. In combination with BBN constraints on the baryon density and Alcock-Paczynski estimates of the matter density, these results strengthen the use of baryon fraction measurements to derive a Hubble constant from energy densities, with future DESI and Euclid data expected to deliver competitive constraints.

Crespi, Andrea [U. Waterloo (main); Waterloo U., I↗

DOE OSTI2023

New techniques for jet calibration with the ATLAS detector

A determination of the jet energy scale is presented using proton–proton collision data with a centre-of-mass energy of $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 140 fb -1 collected using the ATLAS detector at the LHC. Jets are reconstructed using the ATLAS particle-flow method that combines charged-particle tracks and topo-clusters formed from energy deposits in the calorimeter cells. The anti-k t jet algorithm with radius parameter R = 0.4 is used to define the jet. Novel jet energy scale calibration strategies developed for the LHC Run 2 are reported that lay the foundation for the jet calibration in Run 3. Jets are calibrated with a series of simulation-based corrections, including state-of-the-art techniques in jet calibration such as machine learning methods and novel in situ calibrations to achieve better performance than the baseline calibration derived using up to 81 fb -1 of Run 2 data. The performance of these new techniques is then examined in the in situ measurements by exploiting the transverse momentum balance between a jet and a reference object. The b -quark jet energy scale using particle flow jets is measured for the first time with around 1% precision using $\gamma$+jet events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗

NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗

DOE OSTI2024

Studying strangeness and baryon production mechanisms through angular correlations between charged Ξ baryons and identified hadrons in pp collisions at $\sqrt{s}$ = 13 TeV

The angular correlations between charged Ξ baryons and associated identified hadrons (pions, kaons, protons, Λ baryons, and Ξ baryons) are measured in pp collisions at $\sqrt{s}$ = 13 TeV with the ALICE detector to give insight into the particle production mechanisms and balancing of quantum numbers on the microscopic level. In particular, the distribution of strangeness is investigated in the correlations between the doubly-strange Ξ baryon and mesons and baryons that contain a single strange quark, K and Λ. As a reference, the results are compared to Ξπ and Ξp correlations, where the associated mesons and baryons do not contain a strange valence quark. These measurements are expected to be sensitive to whether strangeness is produced through string breaking or in a thermal production scenario. Furthermore, the multiplicity dependence of the correlation functions is measured to look for the turn-on of additional particle production mechanisms with event activity. The results are compared to predictions from the string-breaking model P YTHIA 8, including tunes with baryon junctions and rope hadronisation enabled, the cluster hadronisation model H ERWIG 7, and the core-corona model E POS-LHC . While some aspects of the experimental data are described quantitatively or qualitatively by the Monte Carlo models, no model can match all features of the data. These results provide stringent constraints on the strangeness and baryon number production mechanisms in pp collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2017

Orion Multi-Purpose Crew Vehicle Solving and Mitigating the Two Main Cluster Pendulum Problem

The Orion Multi-purpose Crew Vehicle (MPCV) Orion spacecraft will return humans from beyond earth's orbit, including Mars and will be required to land 20,000 pounds of mass safely in the ocean. The parachute system nominally lands under 3 main parachutes, but the system is designed to be fault tolerant and land under 2 main parachutes. During several of the parachute development tests, it was observed that a pendulum, or swinging, motion could develop while the Crew Module (CM) was descending under two parachutes. This pendulum effect had not been previously predicted by modeling. Landing impact analysis showed that the landing loads would double in some places across the spacecraft. The CM structural design limits would be exceeded upon landing if this pendulum motion were to occur. The Orion descent and landing team was faced with potentially millions of dollars in structural modifications and a severe mass increase. A multidisciplinary team was formed to determine root cause, model the pendulum motion, study alternate canopy planforms and assess alternate operational vehicle controls & operations providing mitigation options resulting in a reliability level deemed safe for human spaceflight. The problem and solution is a balance of risk to a known solution versus a chance to improve the landing performance for the next human-rated spacecraft.

Ali, Yasmin↗

NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗

DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

NASA NTRSDate not supplied

Effect of Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for 1st Generation Nuclear Thermal Propulsion Flight Test Systems

Following the first Nuclear Thermal Propulsion (NTP) system test, also known as DRACO, the next NTP system to be developed would be the 1st Generation NTP system. An analysis was conducted to determine the performance of different vehicle configurations utilizing hydrogen (H-NTP), ammonia (A-NTP), and methane (M-NTP) as propellants launched onboard commercial launch vehicles. This was enabled by a quasi-steady-state power balance engine model and vehicle component physics that sized the vehicle system using Master Equipment List (MEL) parameters. The analysis considered configuration cases outlined by a matrix of different mission classes and vehicle configurations that covered the design space of the 1st Generation NTP system to explore various propellant options, engine architectures, and mission scopes. Four mission classes were evaluated which included single burn missions performing maneuvers having a ΔV of 1 km/s and 2.5 km/s followed by 2-burn and 4-burn missions that aimed to exhaust the launch vehicles volume or mass limitations. In all cases, the NTP systems with the lowest thrust class had the longest burn time of which A-NTP and M-NTP systems provided the longest and shortest burn times depending on the launch vehicle used while H-NTP systems tended to cluster together in the middle. Longer burn times could be useful as a testing platform to increase the time for data accumulation. Across the multi-burn cases, H-NTP systems were found to be volume limited while A-NTP and M-NTP systems were mass limited. Both single burn missions showed that A-NTP configurations provided the lowest dry mass given that ammonia had the highest density with comparable performance to M-NTP systems and no requirement for cryocoolers. The results showed that beyond the propellant selection type, the launch vehicle selection, which included Starship, New Glenn, Vulcan, Falcon 9 (recoverable), and Falcon Heavy (recoverable), was a primary driving factor in the test vehicle’s capabilities. Trends were determined based on a set of dimensionless parameters that included the mass ratio of inert mass to initial wetted mass, ratio of specific impulse to burn time, and a dimensionless engine parameter (ratio of impulse to engine momentum). These relationships found the “knees-in-the-curves” that could be a significant point of reference for the designer as they indicate a change in the trend which is located at a specific impulse to burn time ratio of 1, a mass ratio of 0.6, and an engine performance parameter of 4. This study did not attempt to make a recommendation rather provide the tools for the reader to select their own configuration based on their needs.

Propellant↗

DOE OSTI2022

Connecting the Radiative Influences of Aerosol upon the Mass Flux Profiles of Shallow Cumuli across the Southeast Atlantic Ocean Basin and its Boundaries (Final Report)

The Atlantic Ocean covers approximately 25% of Earth’s surface and the atmosphere above it is home to a complex array of clouds and aerosols that have important influences on regional and global weather and climate. These influences must be accurately depicted in short range, medium range, seasonal, and climate forecast models. Conditions over the tropical Atlantic are particularly complex due to continental scale plumes of dust from the Sahara Desert and smoke from agricultural burning in Africa that drift across the Atlantic Ocean basin toward the Americas. These plumes are often found meandering in the lower atmosphere above shallow tropical clouds that form above the ocean surface, presumably mingling with these clouds on occasion due to convective mixing processes. Elevated dust and smoke particles absorb incoming sunlight and substantially warm the marine atmosphere in the layer in which they are present. This warming may alter the thermal stability of the marine atmosphere and may throttle or enhance the development of clouds, change their internal structure and the rate at which they precipitate. Alternatively, it may isolate the lower atmosphere from drier layers above enabling water vapor to accumulate near the ocean surface potentially leading to the development of deeper convection. Our study was organized around the principal concept of determining the impact of African smoke and dust plumes upon cloud development at ASI and understanding how a popular shallow convection parameterization used in models responds to the presence of this aerosol. We analyzed observations collected during the US Department of Energy (DOE) Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign using the Atmospheric Radiation Measurement Program’s Mobile Facility #1 (AMF-1), which was deployed to Ascension Island (ASI) in Southeastern Atlantic for a one-year period. Ascension Island is immediately downwind from an African source of these plumes, but far enough removed to enable the lower atmosphere to have reacted to their presence. A main goal of our study was to compute radiative heating rate profiles over Ascension Island and along the trajectory from the biomass burning regions along coastal Africa to Ascension Island. To compute the radiative heating rate due to aerosols and clouds, we employed observed profiles of temperature, humidity, and clouds from LASIC alongside aerosol optical properties from the Modern-era Retrospective analysis for Research and Applications Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM). Radiative heating was also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We were successful in this effort and the resulting publication is already being cited despite its relatively short lifetime in the literature. The second part of our study involved the process-level representation of the clouds observed over the Southeastern Atlantic in models. Our initial task, upon which the balance of this portion of the study depended, was to evaluate the representativeness of the clouds observed by AMF-1. What orographic influence did Ascension Island have on the measured cloud structure? We set out to answer this question by trying to separate observations that were clearly indicative of orographic forcing from those that were representative of open ocean. The siting of AMF-1, which was 340-m above sea-level on the slope of a steep escarpment, proved demonstrably problematic for cloud process measurements. Despite considerable effort using multiple approaches including artificial intelligence (AI), we were unable to successfully compensate for the orographic forcing present at the AMF-1 site on ASI and, hence, produce process-level summaries of the convective mass flux representative of open ocean over the Southeastern Atlantic. Even though the project has officially ended, we are making a final attempt using two new types of clustering algorithm (i.e., AI) on the recommendation of a former student, who is an expert in this area. Since we have only recently begun to test these new AI schemes, the recommendations from the second part of our project outlined below are based on our experience at the time of this report.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Molecular Cooling as a Probe of Star Formation: Spitzer Looking Forward to Herschel

We explore here the question of how cloud physics can be more directly probed when one observes the majority of cooling emissions from molecular gas. For this purpose we use results from a recent Spitzer Space Telescope study of the young cluster of embedded objects in NGC1333. For this study we mapped the emission from eight pure H2 rotational lines, from S(0) to S(7). The H2 emission appears to be associated with the warm gas shocked by the multiple outflows present in the region. The H2 lines are found to contribute to 25 - 50% of the total outflow luminosity, and can be used to more directly ascertain the importance of star formation feedback on the natal cloud. From these lines, we determine the outflow mass loss rate and, indirectly, the stellar infall rate, the outflow momentum and the kinetic energy injected into the cloud over the embedded phase. The latter is found to exceed the binding energy of individual cores, suggesting that outflows could be the main mechanism for cores disruption. Given the recent launch of Herschel and the upcoming operational lifetime of SOFIA we discuss how studies of molecular cooling can take a step beyond understanding thermal balance to exploring the origin, receipt, and transfer of energy in atomic and molecular gas in a wide range of physical situations.

Bergin, Edwin A.↗