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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Frequency Domain Detection and Precoding for Massive MIMO With Single Carrier Modulation

Single carrier modulation (SCM) schemes are attractive for uplink (UL) transmissions due to improved power efficiency at the user equipment (UE) transmitter compared with multi-carrier modulation schemes. In a massive MIMO scenario with SCM, the UL detection must mitigate the effects of inter-symbol interference and multiuser interference. This processing is effectively performed in the frequency domain (FD) using a minimum mean squared error (MMSE) detector when the transmission is framed with a cyclic prefix. This paper presents a MMSEbased detector that is compatible with distributed processing in a time-division duplex (TDD) system. The matrix inverses computed for the UL detection are then reused to perform multi-user precoding for the downlink (DL). We find that this scheme yields a tremendous savings in computational complexity compared to commonly used zero-forcing (ZF) precoding without sacrificing any performance. Since MMSE processing introduces a bias to the estimates, we then derive the scalar coefficients necessary to cancel the MMSE bias. The impact of channel estimation errors are analyzed for both the UL and DL cases in conjunction with a power-efficient approach to SCM channel estimation. Moreover, extensive simulations are performed to confirm our theoretical findings.

42 ENGINEERING↗
DOE OSTI2021

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Friction Experiment Data Analysis

The Spent Fuel Waste Disposition (SFWD) program under the U.S. Department of Energy (DOE) is planning a seismic shake table test of full-scale dry storage systems of spent nuclear fuel (SNF) to close the gap related to the seismic loads on the fuel assemblies in dry storage systems. This test will allow for quantifying the strains and accelerations on surrogate fuel assembly during representative earthquakes. A concrete layer will be installed on the shake table before the test to simulate conditions representative of an ISFSI pad. In the shake table tests with the vertical cask, the cask will be free-standing because this is representative of all, except two, ISFSIs in the U.S. with vertical dry storage casks. The static and dynamic friction coefficients between the steel bottom of the cask and the concrete layer on the shake table are important parameters that will affect cask behavior during the test. These parameters must be known for the pre- and post-test modelling, data analysis, and model validation. The friction experiment was performed at the Engineering Department of the University of New Mexico (UNM) to determine the friction coefficients between a steel plate with the same finish as the bottom of the vertical cask manufactured for the test and different concrete surfaces. In this experiment the steel plate was fixed and the concrete sample was pulled over the plate with a constant displacement rate using an MTS machine. This allowed for collecting continuous horizontal force data over the length of the steel plate. Four displacement rates and three vertical loads were used. The tests were performed with four concrete blocks with different degrees of the surface roughness - light sandblast, light to medium sandblast, medium bush hammer, and heavy sandblast. The total number of tests was 48. The data were used to calculate static and dynamic friction coefficients.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Novel solid-infused durable nonwetting surfaces for sustained condensation heat transfer enhancement

Here, in this article, we report novel nonwetting solid-infused surfaces that are easily fabricated using industrially widely adopted methods such as chemical etching or electrodeposition and exhibit sustained dropwise condensation performance durably than superhydrophobic and lubricant-infused surfaces. Solid-infused surfaces demonstrate fourfold larger condensation heat transfer coefficient and 25% better condensation effectiveness when compared to superhydrophobic surface and conventional filmwise condensation. Although the condensation heat transfer coefficient of lubricant-infused surface initially matches that of solid-infused surface, it is shown through durability studies that the wettability characteristic that underpins the performance of lubricant-infused surfaces degrades quickly with time as the lubricant depletes and lubricant-infused surface transitions to superhydrophobic surface. Solid-infused surface, on the other hand, retains its steadfast superior dropwise condensation effectiveness in the long run, overcoming the challenges of droplet pinning, surface inundation, cloaking, and lubricant depletion that eventually deteriorate condensation on conventional nonwetting superhydrophobic and lubricant-infused surfaces.

42 ENGINEERING↗
DOE OSTI2026

Variations in lithium vapor cave performance predictions due to radial transport and recycling assumptions

The lithium vapor cave is a detached divertor design that uses a single private flux region baffle to contain a dense cloud of lithium vapor to dissipate heat flux. Plasma flows are created via fuel gas puffing in order to minimize lithium contamination of the main plasma. Significant modeling using the 2D edge code SOLPS-ITER has already been performed, predicting that sufficient target heat flux reductions (q$^{max}_{Target}$ MW m −2 ) with ( $n$ Li /$n$ e ) LCFS < 0.05 is possible in a case with 90 MW m −2 unmitigated heat flux in NSTX-U. Low heat flux and low upstream concentration was found with a variety of combinations of divertor geometries, target recycling coefficients, upstream plasma parameters, lithium evaporation locations and deuterium fueling locations, with variations in performance found for each design choice. However, the most universal uncertainty of SOLPS-ITER simulations has until now remained unaddressed systematically, namely the cross-field anomalous particle and heat diffusivities. This article aims to bound the uncertainty in lithium concentration prediction as a result of the assumed deuterium cross-field transport and recycling. For the simulations presented here, a factor of 2.1 increase in the upstream lithium density prediction across a factor of four decrease to the assumed deuterium radial particle diffusivity is found. This result is compared across different assumed deuterium recycling coefficients, known to reduce with lithium injection. Upstream lithium density is found to vary by a factor of 2.4 across a feasible range of recycling coefficients at similar $n$$^{OMP,sep}_{e}$.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Particle Size Distribution Dynamics Can Help Constrain the Phase State of Secondary Organic Aerosol

Particle phase state is a property of atmospheric aerosols that has important implications for the formation, evolution, and gas/particle partitioning of secondary organic aerosol (SOA). In this work, we use a size-resolved chemistry and microphysics model (SOM-TOMAS), updated to include an explicit treatment of particle phase state, to constrain the bulk diffusion coefficient (Db) of SOA produced from ??- pinene ozonolysis. By leveraging data from laboratory experiments performed in the absence of a seed and under dry conditions, we find that the Db for SOA can be constrained (1-7 ×10-15 cm2 s-1 in these experiments) by simultaneously reproducing the time-varying SOA mass concentrations and the evolution of the particle size distribution. Another version of our model that used the predicted SOA composition to calculate the glass transition temperature, viscosity, and, ultimately, Db (~10-15 cm2 s-1) of the SOA was able to reproduce the mass and size distribution measurements when we included oligomer formation (oligomers accounted for about a fifth of the SOA mass). Our work highlights the potential of a size resolved SOA model to constrain the particle phase state of SOA by utilizing historical measurements of the evolution of the particle size distribution.

He, Yicong↗
DOE OSTI2024

Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization

We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2023

From electronic structure to model application of key reactions for gasoline/alcohol combustion: Hydrogen-atom abstractions by $CH_3\dot{O}$ radical

H-atom abstraction by methoxy radical ($CH_3\dot{O}$) plays an important role in capturing the kinetics of reactions between gasoline components and alcohols. This study focuses on determining the reaction rates and thermodynamic properties of methoxy radical reactions with five gasoline fuel components: n-heptane, iso-octane, 1-hexene, cyclopentane and toluene. Electronic structure calculations were per-formed for all the stationary points with M06-2X/6 -311 ++ g(d,p) method. G3 composite method with atomization method is used for determining Δ f H 0 of all the closed shell and radical species, using which the necessary thermodynamic data of all the species was determined. Coupled cluster theory QCISD(T)/cc-pVXZ (where X = D and T) and Møller-Plesset perturbation theory MP2/cc-pVXZ (where X = D, T and Q) were used to calculate single point energies. Subsequently, rate constants for all hydrogen atom ab-straction channels have been performed using conventional transition state theory with unsymmetric tunneling corrections. A systematic comparison of rates for abstraction from different sites within the same species and same site from different species is done in order to get insights into this reaction class. Here, the computed thermodynamic properties and rate constants were incorporated into a recent gasoline mechanism to investigate the impact of the calculations performed in this work. A shift in predicted NTC (negative temperature coefficient) behavior and significant reduction in model reactivity is observed upon incorporating the rates calculated herein.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Fluid dynamic and thermal performance of a slotted cylinder at low Reynolds number

The fluid dynamic and thermal performance of a circular cylinder with a slot parallel to the flow is numerically investigated. The study utilized the semi-implicit finite volume multi-material algorithm MPM-ICE, a component of the Uintah framework. The normalized slot width s/D ranges from 0.1 - 0.3, introducing an additional heat transfer surface area between ~ 10 and ~ 50%, and a mass reduction between ~ 13 and ~ 38% in the cylinder. We assumed two-dimensional incompressible flow and simulated a Reynolds number Re D between 100 and 1000. The slotted cylinders are found to have a total drag force reduction up to ~ 45%, compared to a solid cylinder despite the additional viscous drag force in the slot. Convection heat transfer is enhanced up to ~ 70%. Further, the slotted cylinder performance index, defined as the ratio of the heat rate to the drag force, increases up to maximum of ~ 3, indicating better overall thermal fluid performance. An entropy analysis showed the best performance index occurs at the highest Re D . Correlations for drag coefficient and Nusselt number are proposed along with an entropy optimization method.

42 ENGINEERING↗
DOE OSTI2023

$In-situ$ synchrotron x-ray diffraction and thermal expansion of TiB2 up to ~3050 °C

There is an increasing interest in understanding the performance and properties of ultra-high temperature ceramics due to their high melting points (<3000 °C) that make them promising for extreme environment applications. In-situ high temperature X-ray diffraction experiments were performed on TiB 2 beads up to ~3050 °C. For these experiments, TiB 2 powders were fabricated into spherical beads via gel casting methods and densified in a high temperature graphite furnace. These sample beads were then levitated in a conical nozzle levitator with reducing atmosphere (3% H 2 -Ar) while being heated using a 400 W CO 2 laser. During levitation a collimated synchrotron X-ray source was used to perform in-situ, temperature-dependent structural characterizations. The anisotropic coefficients of thermal expansion of TiB 2 were characterized as a function of temperature up to ~3050 °C. Elucidation of these properties are critical for the advancement of TiB 2 ceramics and other transition metal di-borides for use in high temperature applications such as hypersonic platforms, nuclear reactors, and atmospheric re-entry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bimolecular Peroxy Radical (RO 2 ) Reactions and Their Relevance in Radical Initiated Oxidation of Hydrocarbons

The kinetics of peroxy radical (RO 2 ) reactions have been of long-standing interest in atmospheric and combustion chemistry. Nevertheless, the lack of kinetic studies at higher temperatures for their reactions with other radicals such as OH has precluded the inclusion of this class of reactions in detailed kinetics models developed for combustion applications. In this work, guided by the limited room temperature experimental studies on selected alkyl-peroxy radicals and literature theoretical kinetics on the prototypical CH 3 O 2 + OH system, we have performed parametric studies on the effect of uncertainties in the rate coefficients and branching ratios to potential product channels for RO 2 + OH reactions at higher temperatures. Literature kinetics models were used to simulate autoignition delays, laminar flame speeds, and speciation profiles in flow and stirred reactors for a variety of common combustion-relevant fuels. Inclusion of RO 2 + OH reactions was found to retard autoignition in fuel-lean (φ = 0.5) mixtures of ethane and dimethyl ether in air. The observed effects were noticeably more pronounced in ozone-enriched combustion of ethane and dimethyl ether. The simulations also examined the influence of ozone doping levels, pressures, and equivalence ratios for both ethane and dimethyl ether oxidation. Sensitivity and flux analyses revealed that the RO 2 + OH reaction is a significant sink of RO 2 radicals at the early stage of autoignition, affecting fuel oxidation through RO 2 ↔ QOOH, RO 2 ↔ alkene + HO 2 , or RO 2 + HO 2 ↔ ROOH + O 2 . Additionally, the kinetic stability of the trioxide formed from RO 2 + OH reactions was investigated using master equation analyses. Last, we discuss other bimolecular reactions that are missing in literature kinetics models but are relevant to hydrocarbon oxidation initiated by external radical sources (plasma-enhanced, ozone-enriched combustion, etc.). In conclusion, the present simulations provide a strong motivation for better characterizing the bimolecular kinetics of peroxy radicals.

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

Variations of Stress Parameters in the Southern California Plate Boundary Around the South Central Transverse Ranges

We examine the stress field in Southern California with a focus on the region near the South Central Transverse Ranges (SCTR), using a refined stress inversion methodology. We independently invert declustered and aftershocks focal mechanisms from the years 1981 to 2017. Comparisons between the stress field orientations and the stress ratios from these inversions provide information on local sources of the stress field. Regionally, the SHmax tends to be toward NNE and the stress ratios vary from transtensional stress regime near the Eastern California Shear Zone (ECSZ), to strike-slip faulting near the SCTR, and toward transpression near the Western Transverse Ranges. Detailed analysis of stress parameters near the SCTR indicates deviations from the regional strike-slip faulting including the following: (1) The San Bernardino Mountain area shows S Hmax direction toward NNW and transpressional stress components likely associated with the motions of the San Andreas Fault and ECSZ; (2) the Cajon Pass and San Gorgonio Pass show transpressional stress regime near the bottom of the seismogenic zone likely associated with the elevated topography; and (3) in Crafton Hills, rotation of the principal stress plunges and SHmax direction and transtensional stress regime below ~10 km, along with lower estimated apparent friction coefficient, suggest a weak fault possibly associated with deep creep. The performed multiscale analysis resolves effects of regional and local loadings. The average stress parameters in the analyzed 37 years do not show significant temporal variations of stress field near the SCTR region.

58 GEOSCIENCES↗
DOE OSTI2024

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2021

Hydraulic Testing of the Lost Lake Aquifer Near Recovery Well RWM019

An aquifer pumping test was conducted on the Lost Lake Aquifer Zone (LLAZ) at the recently installed recovery well RWM019 in accordance with the approved test plan (Dixon, 2020). The objective of the testing was to determine baseline well performance parameters and aquifer hydraulic conductivity. This testing consisted of a step-drawdown test to determine well performance properties, a constant pumping rate aquifer test to determine aquifer hydraulic properties, and a post-test aquifer recovery monitoring period also used to estimate aquifer hydraulic properties. Well performance parameters determined included specific capacity, well efficiency, and head loss coefficients. The specific capacity of RWM019 was determined to be approximately 2.6 gpm/ft of drawdown based on the final step of the step-drawdown test. Well efficiency was inversely related to pumping rate and decreased from 87% to 81% over a pumping range of approximately 49 to 79 gpm. The aquifer head loss coefficient was determined to be 2.3 ft/ft 3 /min and the well loss coefficient was determined to be 0.05 min 2 /ft 5 .

42 ENGINEERING↗
DOE OSTI2023

Hydraulic Testing of the Lost Lake Aquifer Near Recovery Wells RWM 6 and RWM 7

Hydrologic tests were conducted on the Lost Lake Aquifer Zone (LLAZ) at recovery wells RWM 6 and RWM 7 in accordance with the approved test plan (Dixon, 2023). The objective of the testing was to determine well performance parameters of RWM 6 and aquifer hydraulic properties near RWM 6 and RWM 7. Tests at RWM 6 consisted of a step-drawdown test to determine well performance parameters, a constant rate pump test to determine aquifer hydraulic properties, and an aquifer recovery test also used to determine aquifer hydraulic properties. Well performance parameters determined included specific capacity, well efficiency, and head loss coefficients. Prior to testing at RWM 6, a constant rate pump test was conducted at RWM 7. Data from this testing was used to determine aquifer hydraulic properties near RWM 7.

42 ENGINEERING↗
DOE OSTI2025

Hydraulic Testing of the Lost Lake Aquifer Near Recovery Well RWM001R

An aquifer pumping test was conducted on the Lost Lake Aquifer Zone (LLAZ) at the recently installed recovery well RWM001R in accordance with the approved test plan (Dixon, 2024). The objective of the testing was to determine baseline well performance parameters and aquifer hydraulic conductivity. This testing consisted of two step-drawdown tests to determine well performance properties and two constant pumping rate aquifer tests with recovery periods to determine aquifer hydraulic properties. Well performance parameters determined included specific capacity, well efficiency, and head loss coefficients. The results of the step-drawdown testing are presented in Tables ES1 and ES2 whereas the results of the constant pumping rate testing are presented in Table ES3. The average specific capacity of RWM001R operating at pump capacity (~54 gpm) was 2.2 gpm/ft using data from the final step of both tests. The average well efficiency determined from both step-drawdown tests was 56% using data from the final step of both tests. For both step-drawdown tests, well efficiency was inversely related to the pumping rate with the average efficiency decreasing from approximately 79% to 56%. Aquifer head loss coefficients from both tests were comparable (Table ES2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗