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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Universal features of the equation of state of solids

A study of the energetics of solids leads to the conclusion that the equation of state for all classes of solids in compression can be expressed in terms of a universal function. The form of this universal function is determined by scaling experimental compression data for measured isotherms of a wide variety of solids. The equation of state is thus known (in the absence of phase transitions), if zero-pressure volume and isothermal compression and its pressure derivative are known. The discovery described in this paper has two immediate consequences: first, despite the well known differences in the microscopic energetics of the various classes of solids, there is a single equation of state for all classes in compression; and second, a new method is provided for analyzing measured isotherms and extrapolating high-pressure data from low-pressure (e.g. acoustic) data.

Vinet, Pascal↗
NASA NTRS2002

Lobed Mixer Design for Noise Suppression Acoustic and Aerodynamic Test Data Analysis

A comprehensive database for the acoustic and aerodynamic characteristics of several model-scale lobe mixers of bypass ratio 5 to 6 has been created for mixed jet speeds up to 1080 ft/s at typical take-off (TO) conditions of small-to-medium turbofan engines. The flight effect was simulated for Mach numbers up to 0.3. The static thrust performance and plume data were also obtained at typical TO and cruise conditions. The tests were done at NASA Lewis anechoic dome and ASK's FluiDyne Laboratories. The effect of several lobe mixer and nozzle parameters, such as, lobe scalloping, lobe count, lobe penetration and nozzle length was examined in terms of flyover noise at constant altitude. Sound in the nozzle reference frame was analyzed to understand the source characteristics. Several new concepts, mechanisms and methods are reported for such lobed mixers, such as, "boomerang" scallops, "tongue" mixer, detection of "excess" internal noise sources, and extrapolation of flyover noise data from one flight speed to different flight speeds. Noise reduction of as much as 3 EPNdB was found with a deeply scalloped mixer compared to annular nozzle at net thrust levels of 9500 lb for a 29 in. diameter nozzle after optimizing the nozzle length.

Mengle, Vinod G.↗
NASA NTRS2001

Full-Span Tiltrotor Aeroacoustic Model (TRAM) Overview and 40- by 80-Foot Wind Tunnel Test

Most helicopter data trends cannot be extrapolated to tiltrotors because blade geometry and aerodynamic behavior, as well as rotor and fuselage interactions, are significantly different for tiltrotors. A tiltrotor model has been developed to investigate the aeromechanics of tiltrotors, to develop a comprehensive database for validating tiltrotor analyses, and to provide a research platform for supporting future tiltrotor designs. The Full-Span Tiltrotor Aeroacoustic Model (FS TRAM) is a dual-rotor, powered aircraft model with extensive instrumentation for measurement of structural and aerodynamic loads. This paper will present the Full-Span TRAM test capabilities and the first set of data obtained during a 40- by 80-Foot Wind Tunnel test conducted in late 2000 at NASA Ames Research Center. The Full-Span TRAM is a quarter-scale representation of the V-22 Osprey aircraft, and a heavily instrumented NASA and U.S. Army wind tunnel test stand. Rotor structural loads are monitored and recorded for safety-of-flight and for information on blade loads and dynamics. Left and right rotor balance and fuselage balance loads are monitored for safety-of-flight and for measurement of vehicle and rotor aerodynamic performance. Static pressure taps on the left wing are used to determine rotor/wing interactional effects and rotor blade dynamic pressures measure blade airloads. All of these measurement capabilities make the FS TRAM test stand a unique and valuable asset for validation of computational codes and to aid in future tiltrotor designs. The Full-Span TRAM was tested in the NASA Ames Research Center 40- by 80-Foot Wind Tunnel from October through December 2000. Rotor and vehicle performance measurements were acquired in addition to wing pressures, rotor acoustics, and Laser Light Sheet (LLS) flow visualization data. Hover, forward flight, and airframe (rotors off) aerodynamic runs were performed. Helicopter-mode data were acquired during angle of attack and thrust sweeps for a variety of tunnel speeds. Wake geometry images were acquired using LLS photographs and suggest dual tip vortex formation at low thrust conditions. The full paper will include comparisons to isolated-rotor TRAM data acquired at the Duits-Nederlandse Windtunnel (DNW) in 1998. The FS TRAM has been established as a valuable national asset for tiltrotor research. Data reduction and analysis of the 40- by 80-Foot Wind Tunnel test results are underway. Follow-on testing of the FS TRAM is currently being planned for the NASA Ames 80- by 120-Foot Wind Tunnel in late 2001.

McCluer, Megan S.↗
NASA NTRS1958

Comparison of Calculated and Experimental Temperatures and Coolant Pressure Losses for a Cascade of Small Air-Cooled Turbine Rotor Blades

Average spanwise blade temperatures and cooling-air pressure losses through a small (1.4-in, span, 0.7-in, chord) air-cooled turbine blade were calculated and are compared with experimental nonrotating cascade data. Two methods of calculating the blade spanwise metal temperature distributions are presented. The method which considered the effect of the length-to-diameter ratio of the coolant passage on the blade-to-coolant heat-transfer coefficient and assumed constant coolant properties based on the coolant bulk temperature gave the best agreement with experimental data. The agreement obtained was within 3 percent at the midspan and tip regions of the blade. At the root region of the blade, the agreement was within 3 percent for coolant flows within the turbulent flow regime and within 10 percent for coolant flows in the laminar regime. The calculated and measured cooling-air pressure losses through the blade agreed within 5 percent. Calculated spanwise blade temperatures for assumed turboprop engine operating conditions of 2000 F turbine-inlet gas temperature and flight conditions of 300 knots at a 30,000-foot altitude agreed well with those obtained by the extrapolation of correlated experimental data of a static cascade investigation of these blades.

Stepka, Francis S↗
NASA NTRS1986

Addressing the hypersonic simulation problem

The hypersonic simulation problem is solved by the meshing of bench-marked experimental data with results from validated computational fluid dynamic codes. The example presented is for the reentry of the Space Shuttle Orbiter. Mach number effects were assessed by parametrically varying free-stream Mach number and angle of attack in a series of inviscid, perfect-gas computations carried out on a modified Orbiter geometry. Real-gas effects were determined by making calculations at specific points of the reentry trajectory using equilibrium air thermodynamics and comparing with corresponding perfect-gas computations. Viscous computations were also made for both the basic Orbiter body and control surfaces. A methodology is developed that permits the extrapolation of wind tunnel data to flight situations, providing a solution to the hypersonic simulation problem.

Griffith, B. J.↗
DOE OSTI2025

Thermophysical Properties of Ti3SiC2 MAX Phase Composites with SiC Reinforcement

In the present work, dense (∼100%) Ti 3 SiC 2 composites (TSC) are processed along with 20 vol% of SiC reinforcement (TSC20) via spark plasma sintering at 1400°C, 40 MPa, 15 min, and dynamic vacuum environment. Thermal expansion of both the composites increases from RT to 1273 K and linear fitting of data yields coefficient of thermal expansion (CTE) of 9.4 × 10 −6 K −1 for TSC which decreases to 8.3 × 10 −6 K −1 for TSC20. With increase in temperature from RT to 773 K, specific heat for both TSC and TSC20 composites is observed to increase from 598-850 J.kg −1 .K −1 , whereas thermal diffusivity and thermal conductivity values decrease with testing temperature. SiC reinforcement in Ti 3 SiC 2 resulted in improved thermal diffusivity from 12.7 to 18.7 mm 2 .s −1 and thermal conductivity from ∼57 to ∼79 W.m −1 .K −1 at RT. However, with increase in temperature (773 K), thermal diffusivity and conductivity decrease, and values get closer for both TSC and TSC20 composites. Further extrapolation of thermal conductivity data showed cross-over at ∼973 K due to domination of phonon-phonon scattering and thus lower values of thermal conductivity for TSC20 than TSC. Therefore, reduced CTE and higher thermal conductivity of TSC20 make it a viable choice for applications in high temperatures.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Ensemble Methodologies for Astronaut Cancer Risk Assessment in the face of Large Uncertainties

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.

space radiation↗
NASA NTRSDate not supplied

Ensemble Cancer Risk Model for Astronaut Risk Assessment

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.

Lisa C Simonsen↗
NASA NTRS2011

Analysis of Terminal Deletions using a Generalized Time-Dependent Model of Radiation-Induced Formation of Chromosomal Aberrations

We have developed a model that can simulate different types of radiation induced chromosomal aberrations (CA's) and can provide predictions on the frequency and size of chromosomes with terminal deletions. Chromosomes with terminal deletions lack telomeres and this can elicit sister chromatid unions and the prolonged breakage/fusion/bridge (B/F/B) cycles that have been observed in mammalian tumors. The loss of a single telomere has been shown to cause extensive genomic instability through the B/F/B cycle process. Our model uses a stochastic process of DNA broken end joining, in which a realistic spectrum of CA's is created from improperly joined DNA free ends formed by DNA double strand breaks (DSBs). The distribution of the DNA free ends is given by a mechanistic model that takes into account the chromatin structure and track structure for high-LET radiation. The model allows for DSB clustering from high-LET radiation and simulates the formation of CA's in stages that correspond to the actual time after radiation exposure. The time scale for CA formation is derived from experimental data on DSB repair kinetics. At any given time a nucleus may have intact chromosomes, CA's, and/or unrepaired fragments, some of which are defined as terminal deletions, if they are capped by one telomere. The model produces a spectrum of terminal deletions with their corresponding probabilities and size distributions for different heavy ions exposures for the first division after exposure. This data provides valuable information because there is limited experimental data available in the literature on the on the actual size of terminal deletions. We compare our model output to the available experimental data and make a reasonable extrapolation on the number of chromosomes lacking telomeres in human lymphocytes exposed to heavy ions. This model generates data which may lead to predictions on the rate of genomic instability in cells after exposure to high charge and energy nuclei affecting astronauts during space missions.

Ponomarev, Artem L.↗
NASA NTRS2024

Development of a High Frequency Class-A Amplifier for High Temperature Wireless Microsystems based on a Silicon Carbide Static Induction Transistor

Next generation gas turbine engine health monitoring for high performance jet engine-powered aircraft will incorporate integrated microelectronic sensors specifically designed to enhance aircraft functionality, engine efficiency, and safety. Temperature, air flow and pressure associated with the gas path as well as emissions from the engine correlate to the state of the combustor and other engine health conditions. Monitoring these parameters accurately requires positioning electronic sensors in the hot zone of the gas turbine engine, which exposes them to temperatures in excess of 400ºC. At these temperatures, signal amplification is needed at the transducer level to distinguish the desired measured signal from the significant electronic noise generated by the harsh environment. In this presentation, we report on the simulation, development, fabrication, and testing on a high temperature Class-A amplifier which utilizes a 4H-SiC Static Induction Transistor (SIT) as the active device and input/output matching and DC bias networks comprised of thin-film spiral inductors, metal-insulator-metal capacitors, and thick film chip resistors. A small signal model that emulates the operation of the 4H-SiC SIT from 25 to 400ºC, with an emphasis on operation at 400οC, was utilized. Measurements were performed from 25 to 400ºC to generate current-voltage curves, capacitive transistor characteristics and high frequency scattering parameters (S-parameters). The measured data was used to extrapolate the transconductance, gm, as a function of temperature for model development. Circuit simulation tools were used to generate S-parameters, which were compared to the measured values. At 400οC, a maximum difference between measured and simulated S-parameters for frequencies from 20 to 100 MHz were 3.84%, 0.68%, 10.61%, and 3.26% for S21, S12, S11, and S22, respectively. The average transit frequency, ft, was calculated from measured values to be 197.8 MHz, while the simulated value from the model was found to be 200 MHz. The Amplifier’s S-parameters were recorded at 20-100 MHz over a temperature range of 25-400ºC and show a gain of approximately 15.8 and 5.80 dBm at 25 and 400ºC, respectively. The input and output reflection coefficients at 50 MHz and 400ºC were -18.5 and -15.2 dB, respectively. The noise figure and phase noise were measured over the temperature range of 25-400ºC and recorded. The noise figure increased 21% at 50 MHz over the temperature range, while the 1 kHz offset of the phase noise remained below -110 dB. The stability factor, K, calculated with measured and simulated data demonstrates unconditional stability over the frequency range at 400ºC. Lastly, the 1-dB compression point was measured at 50 MHz and 400ºC with an approximated output of 9.5 dBm.

SiC↗
NASA NTRS1995

Smoke detection in low-G fires

Fires in spacecraft are considered a credible risk. To respond to this risk, NASA flew fire detectors on Skylab and the Space Shuttle (STS) and included them in the design for International Space Station Alpha (ISSA). In previous missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was not considered credible that the astronauts could fail to observe a fire. The Skylab nodule included approximately 20 UV fire detectors. The space shuttle has 9 ionization detectors in the mid deck and flight deck and Spacelab has six additional ionization detectors. The planned detectors for ISSA are laser-diode, forward-scattering, smoke or particulate detectors. Current plans for the ISSA call for two detectors in the open area of the module and detectors in racks that have both cooling air flow and electrical power. Due to the complete absence of data concerning the nature of particulate and radiant emission from low-g fires, all three of these detector systems were designed based upon 1-g test data. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew independent, fire detection grows significantly. This requires more knowledge concerning low-gravity fires and how they might be detected. To date, no combustion-generated particulate samples have been collected for well-developed microgravity flames. All of the extant data come from drop tower tests and therefore only correspond to the early stages of a fire. The fuel sources were restricted to laminar gas-jet diffusion flames and rapidly overheated wire insulation. These gas-jet drop tower tests indicate, through thermophoretic sampling, that soot primaries and aggregates (groups of primary particles) in micro-g may be significantly larger than those in normal-g (ng). This raises new scientific questions about soot processes as well as practical issues for particulate detection/alarm threshold levels used in on-orbit smoke detectors. Furthermore, it is widely speculated but unverified that the aggregates will grow to very large scales in a microgravity fire of longer duration than available on the ground. Preliminary tests in the 2.2 second drop tower suggest that particulate generated by overheated wire insulation will also be larger in microgravity than in normal gravity. TEM grids downstream of the fire region in the WIF experiment as well as visual observation of long string-like aggregates, further confirm this suggestion. The combined impact of these limited results and theoretical predictions is that direct knowledge of low-g combustion particulate as opposed to extrapolation from 1-g data is needed for a more confident design of smoke detectors for spacecraft.

Urban, David L.↗
NASA NTRS2003

Geology of the Icy Galilean Satellites: Understanding Crustal Processes and Geologic Histories Through the JIMO Mission

Knowledge of the geology, style and time history of crustal processes on the icy Galilean satellites is necessary to understanding how these bodies formed and evolved. Data from the Galileo mission have provided a basis for detailed geologic and geo- physical analysis. Due to constrained downlink, Galileo Solid State Imaging (SSI) data consisted of global coverage at a -1 km/pixel ground sampling and representative, widely spaced regional maps at -200 m/pixel. These two data sets provide a general means to extrapolate units identified at higher resolution to lower resolution data. A sampling of key sites at much higher resolution (10s of m/pixel) allows evaluation of processes on local scales. We are currently producing the first global geological map of Europa using Galileo global and regional-scale data. This work is demonstrating the necessity and utility of planet-wide contiguous image coverage at global, regional, and local scales.

Figueredo, P. H.↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The solubility of NdPO 4 and DyPO 4 and stability of Nd and Dy chloride and hydroxyl complexes as a function of pH and salinity to 450 °C

The mobility of rare earth elements (REE) in geological systems is often controlled by the stability of monazite and xenotime, which are important hosts for the light and heavy REE. These REE phosphates constitute important resources and provide information on ore formation conditions and timing due to their uses as geothermometers and geochronometers. While the thermodynamic properties for these minerals are well-established up to high temperature and pressure, the properties for the aqueous REE complexes are not well constrained with limited information up to 300 °C. In this study, the solubility of synthetic NdPO 4 and DyPO 4 endmembers were measured in hydrothermal sub- to supercritical NaCl-bearing aqueous solutions from 350 at saturated water vapor pressure to 450 °C at 700 bar. The speciation of Nd and Dy was determined in acidic to alkaline solutions (pH 25 °C values from 2 to 10), and indicates that the hydroxyl species REE(OH) 2 + and REE(OH) 3 0 predominate at low salinity (0.01 mol/kg NaCl) with some contribution of REE chloride species REECl 2+ and REECl 2 + in acidic fluids. The REE phosphate solubility measured in these experiments is up to two orders of magnitude higher than predicted using existing thermodynamic properties from literature for aqueous species extrapolated from lower temperature data to supercritical conditions. To address this discrepancy, the thermodynamic properties of the REE aqueous species were optimized using GEMSFITS to derive the formation constants for the REE chloride (β Cl ) and hydroxyl (β OH ) complexes in the studied temperature range. This study highlights the importance of the hydroxyl species REE(OH) 2 + and REE(OH) 3 0 over a wide range of pH and temperature. As a result, the revised thermodynamic properties for Dy and Nd chloride and hydroxyl species more accurately predict the solubility of NdPO 4 and DyPO 4 and provide insight into the speciation and mobility of the light and heavy REE in hydrothermal supercritical fluids in the crust.

58 GEOSCIENCES↗
DOE OSTI2020

Morphology of Vapor-Deposited Acetonitrile Films

Crystalline acetonitrile has two polymorphs, a high temperature (HT) phase that is stable between 217 K and its melting point at 229 K, and a low temperature (LT) phase that is stable below 217 K. Solid acetonitrile films can be prepared by vapor deposition in an ultra-high vacuum chamber. To prevent sublimation of the film temperatures are often kept below 150 K. While the LT phase is thermodynamically favored at these low temperatures, such preparation usually results in the formation of the metastable HT polymorph. In this work we use reflection adsorption infrared spectroscopy (RAIRS) and temperature programmed desorption (TPD) experiments to investigate the effects of the deposition temperature and underlying substrate on the morphology of acetonitrile films prepared with molecular beam deposition. We obtained the elusive LT phase when dosing at 120 K on a graphene substrate and on a crystalline decane layer. Dosing acetonitrile on other surfaces produced the HT phase, as did annealing of amorphous films. We used TPD experiments to determine the Gibbs energy difference between the HT and LT phases. Our ΔG values agree with extrapolation of equilibrium calorimetry data. Furthermore, we also observed that acetonitrile films were amorphous when dosed at temperatures ≤ 60 K, and porous for temperatures ≤ 50 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reply to Comment by Peterie Et Al. on “Accelerated Fill‐Up of the Arbuckle Group Aquifer and Links to U.S. Midcontinent Seismicity”

Abstract Peterie et al. question one observation in our paper: associating pressure increases to injection volumes at distances of up to 25 km from an injection well. In this reply, we show that the comment misunderstands our analysis and the evidence that led to this conclusion. We also show that gauge‐depth‐corrected pressures, used by the authors to produce statewide pressure maps, are discrepant with the static fluid level data, provided in our original compilation and analysis. The discrepancies are a result of the pressure correction method employed, which naïvely substitutes formation pressure for bottomhole pressure to calculate wellbore fluid density. Their linearly interpolated pressure maps, based on sparse data, contain interpolation and extrapolation artifacts that contradict injection trends in the state, the Theis solution, and the superposition principle. We reiterate that pressure and static fluid level increases in Class I wells existed prior to 2013, most notably in central Kansas, where recent earthquakes are cited in the comment as evidence of a pressure plume emanating from the Kansas‐Oklahoma border, 90 km away. We show that the space‐time pattern of seismicity in this area is inconsistent with a northward propagating pressure plume and, instead, seismicity appears to be centered on and near a cluster of high‐rate injection wells, two of which are among the highest rate wells in the state. These observations, along with recent M4 + earthquakes during continued decreases in wastewater injection in southern Kansas and northern Oklahoma, question the usefulness of the comment for understanding and managing societally significant earthquakes.

Ansari, Esmail↗