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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Generalized fluid theory including non-Maxwellian kinetic effects

The results obtained by the plasma physics community for the validation and the prediction of turbulence and transport in magnetized plasmas come mainly from the use of very central processing unit (CPU)-consuming particle-in-cell or (gyro)kinetic codes which naturally include non-Maxwellian kinetic effects. To date, fluid codes are not considered to be relevant for the description of these kinetic effects. Here, after revisiting the limitations of the current fluid theory developed in the 19th century, we generalize the fluid theory including kinetic effects such as non-Maxwellian super-thermal tails with as few fluid equations as possible. The collisionless and collisional fluid closures from the nonlinear Landau Fokker–Planck collision operator are shown for an arbitrary collisionality. Indeed, the first fluid models associated with two examples of collisionless fluid closures are obtained by assuming an analytic non-Maxwellian distribution function. One of the main differences with the literature is our analytic representation of the distribution function in the velocity phase space with as few hidden variables as possible thanks to the use of non-orthogonal basis sets. These new non-Maxwellian fluid equations could initiate the next generation of fluid codes including kinetic effects and can be expanded to other scientific disciplines such as astrophysics, condensed matter or hydrodynamics. As a validation test, we perform a numerical simulation based on a minimal reduced INMDF fluid model. The result of this test is the discovery of the origin of particle and heat diffusion. The diffusion is due to the competition between a growing INMDF on short time scales due to spatial gradients and the thermalization on longer time scales. Here, the results shown here could provide the insights to break some of the unsolved puzzles of turbulence.

42 ENGINEERING↗
DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2022

Emulation Framework for Distributed Large-Scale Systems Integration

Recent trends in systems engineering include integration of very large-scale systems, which entails significant challenges when they are geographically dispersed. In these scenarios, intelligent integration of distributed large-scale systems requires significant coordination among hardware elements as well as all software components. The approach of integrated systems (both computing platform and experimental equipment) for end-to-end orchestration is called federation. Virtual frameworks can aid in the testing, assessment, and implementation of a functional system of interconnected resources. We present an emulation framework that replicates the software environments of multi-site federations of computing systems and instruments. Our emulation framework allows systems engineers to reduce developmentcost and avoid disruptions to production infrastructure. Our framework was effectively used to develop and test software modules for various tasks including container orchestration and instrument access. For performance assessment, however, the emulated framework is severely limited in providing accurate network and IO measurements at 10 Gbps and higher data rates. The data transfer performance profiles estimated using these emulated measurements are usually inaccurate for high bandwidth and high latency connections, since emulation does not accurately reflect the critical network transport dynamics.We utilize measurements from a physical testbed with hardware network emulators to obtain data transfer profiles that closely match the expected profiles for the emulated federations. We show the effectiveness of our approach by an illustrative example of integrated (federated) multi-site ultra large-scale systems that are connected via high speed wide area networks.

Imam, Neena↗
DOE OSTI2016

SU-F-T-169: A Periodic Quality Assurance Program for a Spot-Scanning Proton Treatment Facility

Purpose: To develop daily and monthly quality assurance (QA) programs in support of a new spot-scanning proton treatment facility using a combination of commercial and custom equipment and software. Emphasis was placed on efficiency and evaluation of key quality parameters. Methods: The daily QA program was developed to test output, spot size and position, proton beam energy, and image guidance using the Sun Nuclear Corporation rf-DQA™3 device and Atlas QA software. The program utilizes standard Atlas linear accelerator tests repurposed for proton measurements and a custom jig for indexing the device to the treatment couch. The monthly QA program was designed to test mechanical performance, image quality, radiation quality, isocenter coincidence, and safety features. Many of these tests are similar to linear accelerator QA counterparts, but many require customized test design and equipment. Coincidence of imaging, laser marker, mechanical, and radiation isocenters, for instance, is verified using a custom film-based device devised and manufactured at our facility. Proton spot size and position as a function of energy are verified using a custom spot pattern incident on film and analysis software developed in-house. More details concerning the equipment and software developed for monthly QA are included in the supporting document. Thresholds for daily and monthly tests were established via perturbation analysis, early experience, and/or proton system specifications and associated acceptance test results. Results: The periodic QA program described here has been in effect for approximately 9 months and has proven efficient and sensitive to sub-clinical variations in treatment delivery characteristics. Conclusion: Tools and professional guidelines for periodic proton system QA are not as well developed as their photon and electron counterparts. The program described here efficiently evaluates key quality parameters and, while specific to the needs of our facility, could be readily adapted to other proton centers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Error Analysis of the Thornado Supernova Simulation Code

The iron core collapse of a supernova throws 1.5 solar masses at speeds up to 35% the speed of light into a space with radius 50km, which creates a high density environment capable of generating neutrinos, producing gravitational waves, and driving chemical evolution of matter past iron. By designing a simulation of the process, we can better understand the origins and properties of these rare phenomena which are not seen anywhere else in the universe. Typically, supernovas are simulated with a finite volume method, but we want to incorporate a Nodal Discontinuous Galerkin Method to better utilize high performance computing because the DG method allows us to increase precision without increasing the number of computational elements. This project focuses on the fluid dynamics during collapse and the resulting shockwaves. To understand this, we improved on an existing simulation by comparing benchmark tests against published and theoretical results to identify and correct sources of error. The DG Method creates discontinuous functions across computational elements by solving the Euler fluid equations computed over Runge-Kutta time steps. The DG method allows for discontinuous functions to be built from polynomials defined in individual elements; however, it can lead to unphysical oscillation near discontinuities. We apply the characteristic slope limiter to reduce these affects, but when applied globally it leads to a 1% increase in absolute error. By introducing a Trouble Cell Indicator, which marks certain fluid elements based on the rate of change of observables, we can constrain the limiter to elements near shocks and manage the resulting absolute error. By constraining the limiter to discontinuous regions, we can remove what we know will be unrealistic without impacting naturally occurring oscillations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Energy Characterization of the UMD Electron Accelerator

Multiple different groups at Los Alamos National Laboratory have a cited interest in understanding the effects of ionizing radiation in materials. Of especial interest to the ISR division is understanding the effects in materials that are used in the space environment, where radiation is omnipresent and impossible to fully shield from. Development is under way to simulate the effects of radiation damage in detectors and electronics, primarily with particle transport codes such as Geant4 and RAMPART, a code that provides a user friendly interface to Geant4. However, these simulations need benchmarking to experimental tests, some of which have been performed at a variety of small electron accelerator facilities. One of these facilities is the Linear Electron Accelerator at the University of Maryland Radiation Facilities. This facility can provide electron beams on the order of 100 mA delivered in tight macropulse bunches, which allows for experiments where the behavior of electronics is monitored as a function of pulse number. While the beam rates per macropulse of this accelerator is monitored during an experiment via a current pick-off connected to a large graphite beamstop, the energy distribution of this electron beam is not measured during the experiment and has not been well characterized. The energy distribution of the beam is pertinent for these types of experiments, since dose is directly related to energy deposited in a medium. Further, these experiments are performed in open air and sample placement in the room can dictate what dose rate they receive per macropulse. We have created simulated dosemaps for the UMD vault room, but these simulations assume a given energy of the beam, both for calculating the dose and to simulate how much the beam diverges in the air. There have been two attempts now to characterize this accelerator beam energy with a plate type spectrometer, including the most recent experiment earlier this year to quantify the beam energy distribution. These results will also be compared to a dosimetry experiment performed by collaborators at the UMD facility, the details of which are described in Reference. Understanding the electron beam energy is important for future experiments, because the energy directly impacts the dose and radiation damage given to a sample and is important for properly simulating the electron beam when comparing simulation to experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Fully self-consistent finite-temperature $GW$ in Gaussian Bloch orbitals for solids

In this work, we present algorithmic and implementation details for the fully self-consistent finite-temperature $GW$ method in Gaussian Bloch orbitals for solids. Our implementation is based on the finite-temperature Green's function formalism in which all equations are solved on the imaginary axis, without resorting to analytical continuation during the self-consistency. No quasiparticle approximation is employed and all matrix elements of the self-energy are explicitly evaluated. The method is tested by evaluating the band gaps of selected semiconductors and insulators. We show agreement with other, differently formulated, finite-temperature sc ⁢$GW$ implementations when finite-size corrections and basis-set errors are taken into account. By migrating computationally intensive calculations to graphics processing units, we obtain scalable results on large supercomputers with nearly optimal performance. Our work demonstrates the applicability of Gaussian orbital based sc⁢ $GW$ for ab initio correlated material simulations and provides a sound starting point for embedding methods built on top of $GW$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Predicting Li-Ion Battery Capacity Fade Using Early-Life Data and a Hybrid Data-Driven Gaussian Process-Bayesian Regression Approach

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.

42 ENGINEERING↗
DOE OSTI2021

Multiphysics and Multiscale Modeling of Coupled Transport of Chloride Ions in Concrete

Chloride ions (Cl−)-induced corrosion is one of the main degradation mechanisms in reinforced concrete (RC) structures. In most situations, the degradation initiates with the transport of Cl− from the surface of the concrete towards the reinforcing steel. The accumulation of Cl− at the steel-concrete interface could initiate reinforcement corrosion once a threshold Cl− concentration is achieved. An accurate numerical model of the Cl− transport in concrete is required to predict the corrosion initiation in RC structures. However, existing numerical models lack a representation of the heterogenous concrete microstructure resulting from the varying environmental conditions and the indirect effect of time dependent temperature and relative humidity (RH) on the water adsorption and Cl− binding isotherms. In this study, a numerical model is developed to study the coupled transport of Cl− with heat, RH and oxygen (O2) into the concrete. The modeling of the concrete microstructure is performed using the Virtual Cement and Concrete Testing Laboratory (VCCTL) code developed by the U.S. National Institute of Standards and Technology (NIST). The concept of equivalent maturation time is utilized to eliminate the limitation of simulating concrete microstructure using VCCTL in specific environmental conditions such as adiabatic. Thus, a time-dependent concrete microstructure, which depends on the hydration reactions coupled with the temperature and RH of the environment, is achieved to study the Cl− transport. Additionally, Cl− binding isotherms, which are a function of the pH of the concrete pore solution, Cl− concentration, and weight fraction of mono-sulfate aluminate (AFm) and calcium-silicate-hydrate (C-S-H), obtained from an experimental study by the same authors are utilized to account for the Cl− binding of cement hydration products. The temperature dependent RH diffusion was considered to account for the transport of Cl− with moisture transport. The temperature and RH diffusion in the concrete domain, composite theory, and Cl− binding and water adsorption isotherms are used in combination, to estimate the ensuing Cl− diffusion field within the concrete. The coupled transport process of heat, RH, Cl−, and O2 is implemented in the Multiphysics Object-Oriented Simulation Environment (MOOSE) developed by the U.S. Idaho National Laboratory (INL). The model was verified and validated using data from multiple experimental studies with different concrete mixture proportions, curing durations, and environmental conditions. Additionally, a sensitivity analysis was performed to identify that the water-to-cement (w/c) ratio, the exposure duration, the boundary conditions: temperature, RH, surface Cl− concentration, Cl− diffusion coefficient in the capillary water, and the critical RH are the important parameters that govern the Cl− transport in RC structures. In a case study, the capabilities of the developed numerical model are demonstrated by studying the complex 2D diffusion of Cl− in a RC beam located in two different climatic regions: warm and humid weather in Galveston, Texas, and cold and dry weather in North Minnesota, Minnesota, subjected to time varying temperature, RH, and surface Cl− concentrations.

composite theory↗
DOE OSTI2016

An a priori DNS study of the shadow-position mixing model

In this study, the modeling of mixing by molecular diffusion is a central aspect for transported probability density function (tPDF) methods. In this paper, the newly-proposed shadow position mixing model (SPMM) is examined, using a DNS database for a temporally evolving di-methyl ether slot jet flame. Two methods that invoke different levels of approximation are proposed to extract the shadow displacement (equivalent to shadow position) from the DNS database. An approach for a priori analysis of the mixing-model performance is developed. The shadow displacement is highly correlated with both mixture fraction and velocity, and the peak correlation coefficient of the shadow displacement and mixture fraction is higher than that of the shadow displacement and velocity. This suggests that the composition-space localness is reasonably well enforced by the model, with appropriate choices of model constants. The conditional diffusion of mixture fraction and major species from DNS and from SPMM are then compared, using mixing rates that are derived by matching the mixture fraction scalar dissipation rates. Good qualitative agreement is found, for the prediction of the locations of zero and maximum/minimum conditional diffusion locations for mixture fraction and individual species. Similar comparisons are performed for DNS and the IECM (interaction by exchange with the conditional mean) model. The agreement between SPMM and DNS is better than that between IECM and DNS, in terms of conditional diffusion iso-contour similarities and global normalized residual levels. It is found that a suitable value for the model constant c that controls the mixing frequency can be derived using the local normalized scalar variance, and that the model constant a controls the localness of the model. A higher-Reynolds-number test case is anticipated to be more appropriate to evaluate the mixing models, and stand-alone transported PDF simulations are required to more fully enforce localness and to assess model performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Block Lanczos algorithm for lattice QCD spectroscopy and matrix elements

Recent work introduced a new framework for analyzing correlation functions with improved convergence and signal-to-noise properties, as well as rigorous quantification of excited-state effects, based on the Lanczos algorithm and spurious eigenvalue filtering with the Cullum-Willoughby test. Here, we extend this framework to the analysis of correlation-function matrices built from multiple interpolating operators in lattice quantum chromodynamics (QCD) by constructing an oblique generalization of the block Lanczos algorithm, as well as a new physically motivated reformulation of the Cullum-Willoughby test that generalizes to block Lanczos straightforwardly. The resulting block Lanczos method directly extends generalized eigenvalue problem (GEVP) methods, which can be viewed as applying a single iteration of block Lanczos. Block Lanczos provides qualitative and quantitative advantages over GEVP methods analogous to the benefits of Lanczos over the standard effective mass, including faster convergence to ground- and excited-state energies, explicitly computable two-sided error bounds, straightforward extraction of matrix elements of external currents, and asymptotically constant signal-to-noise. No fits or statistical inference are required. Proof-of-principle calculations are performed for noiseless mock-data examples as well as two-by-two proton correlation-function matrices in lattice QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Phased Array Ultrasonic Examination of Reactor Coolant System (Carbon Steel-to-CASS) Dissimilar Metal Weld Mockup Specimen

In the summer of 2009, Pacific Northwest National Laboratory (PNNL) staff traveled to the Electric Power Research Institute (EPRI) NDE Center in Charlotte, North Carolina, to conduct phased-array ultrasonic testing on a large bore, reactor coolant pump nozzle-to-safe-end mockup. This mockup was fabricated by FlawTech, Inc. and the configuration originated from the Port St. Lucie nuclear power plant. These plants are Combustion Engineering-designed reactors. This mockup consists of a carbon steel elbow with stainless steel cladding joined to a cast austenitic stainless steel (CASS) safe-end with a dissimilar metal weld and is owned by Florida Power & Light. The objective of this study, and the data acquisition exercise held at the EPRI NDE Center, were focused on evaluating the capabilities of advanced, low-frequency phased-array ultrasonic testing (PA-UT) examination techniques for detection and characterization of implanted circumferential flaws and machined reflectors in a thick-section CASS dissimilar metal weld component. This work was limited to PA-UT assessments using 500 kHz and 800 kHz probes on circumferential flaws only, and evaluated detection and characterization of these flaws and machined reflectors from the CASS safe-end side only. All data were obtained using spatially encoded, manual scanning techniques. The effects of such factors as line-scan versus raster-scan examination approaches were evaluated, and PA-UT detection and characterization performance as a function of inspection frequency/wavelength, were also assessed. A comparative assessment of the data is provided, using length-sizing root-mean-square-error and position/localization results (flaw start/stop information) as the key criteria for flaw characterization performance. In addition, flaw signal-to-noise ratio was identified as the key criterion for detection performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Quantum efficiency investigations of type-II InAs/GaSb midwave infrared superlattice photodetectors

We present in this paper a comparison between different type-II InAs/GaSb superlattice (T2SL) photodiodes and focal plane array (FPA) in the mid-wavelength infrared domain to understand which phenomenon drives the performances of the T2SL structure in terms of quantum efficiency (QE). Our measurements on test photodiodes suggest low minority carrier diffusion length in the “InAs-rich” design, which penalizes carriers' collection in this structure for low bias voltage and front side illumination. This analysis is completed by a comparison of the experimental data with a fully analytic model, which allows to infer a hole diffusion length shorter than 100 nm. In addition, measurements on a FPA with backside illumination are finally presented. Results show an average QE in the 3–4.7 μm window equal to 42% for U{sub bias} = −0.1 V, 77 K operating temperature and no anti-reflection coating. These measurements, completed by modulation transfer function and noise measurements, reveal that the InAs-rich design, despite a low hole diffusion length, is promising for high performance infrared imaging applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

TU-F-18C-03: X-Ray Scatter Correction in Breast CT: Advances and Patient Testing

Purpose: To further develop and perform patient testing of an x-ray scatter correction algorithm for dedicated breast computed tomography (BCT). Methods: A previously proposed algorithm for x-ray scatter signal reduction in BCT imaging was modified and tested with a phantom and on patients. A wireless electronic positioner system was designed and added to the BCT system that positions a tungsten plate in and out of the x-ray beam. The interpolation used by the algorithm was replaced with a radial basis function-based algorithm, with automated exclusion of non-valid sampled points due to patient motion or other factors. A 3D adaptive noise reduction filter was also introduced to reduce the impact of scatter quantum noise post-reconstruction. The impact on image quality of the improved algorithm was evaluated using a breast phantom and seven patient breasts, using quantitative metrics such signal difference (SD) and signal difference-to-noise ratios (SDNR) and qualitatively using image profiles. Results: The improvements in the algorithm resulted in a more robust interpolation step, with no introduction of image artifacts, especially at the imaged object boundaries, which was an issue in the previous implementation. Qualitative evaluation of the reconstructed slices and corresponding profiles show excellent homogeneity of both the background and the higher density features throughout the whole imaged object, as well as increased accuracy in the Hounsfield Units (HU) values of the tissues. Profiles also demonstrate substantial increase in both SD and SDNR between glandular and adipose regions compared to both the uncorrected and system-corrected images. Conclusion: The improved scatter correction algorithm can be reliably used during patient BCT acquisitions with no introduction of artifacts, resulting in substantial improvement in image quality. Its impact on actual clinical performance needs to be evaluated in the future. Research Agreement, Koning Corp., Hologic Inc., Barco Corp Consultant Agreement, Fujifilm Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

ZIF‐8 Membrane Separation Performance Tuning by Vapor Phase Ligand Treatment

Abstract Vapor phase ligand treatment (VPLT) of 2‐aminobenzimidazole (2abIm) for 2‐methylimidazole (2mIm) in ZIF‐8 membranes prepared by two different methods (LIPS: ligand induced permselectivation and RTD: rapid thermal deposition) results in a notable shift of the molecular level cut‐off to smaller molecules establishing selectivity improvements from ca. 1.8 to 5 for O 2 /N 2 ; 2.2 to 32 for CO 2 /CH 4 ; 2.4 to 24 for CO 2 /N 2 ; 4.8 to 140 for H 2 /CH 4 and 5.2 to 126 for H 2 /N 2 . Stable (based on a one‐week test) oxygen‐selective air separation performance at ambient temperature, 7 bar(a) feed, and 1 bar(a) sweep‐free permeate with a mixture separation factor of 4.5 and oxygen flux of 2.6×10 −3 mol m −2 s −1 is established. LIPS and RTD membranes exhibit fast and gradual evolution upon a 2abIm‐VPLT, respectively, reflecting differences in their thickness and microstructure. Functional reversibility is demonstrated by showing that the original permeation properties of the VPLT‐LIPS membranes can be recovered upon 2mIm‐VPLT.

Eum, Kiwon↗
DOE OSTI2015

Irreversible Electroporation (IRE) Fails to Demonstrate Efficacy in a Prospective Multicenter Phase II Trial on Lung Malignancies: The ALICE Trial

PurposeTo assess safety and efficacy of irreversible electroporation (IRE) of lung malignancies.Materials and MethodsPatients with primary and secondary lung malignancies and preserved lung function were included in this prospective single arm trial. Primary and secondary endpoints were safety and efficacy. Recruitment goal was 36 subjects in 2 centers. Patients underwent IRE under general anesthesia with probe placement performed in Fluoroscopy-CT. The IRE system employed was NanoKnife{sup ®} (Angiodynamics). System settings for the ablation procedure followed the manufacturer’s recommendations. The Mann–Whitney U test was used to evaluate the correlation of nine technical parameters with local tumor control. Median follow up was 12 months.ResultsThe expected efficacy was not met at interim analysis and the trial was stopped prematurely after inclusion of 23 patients (13/10 between both centers). The dominant tumor entity was colorectal (n = 13). The median tumor diameter was 16 mm (8–27 mm). Pneumothoraces were observed in 11 of 23 patients with chest tubes required in 8 (35 %). Frequently observed alveolar hemorrhage never led to significant hemoptysis. 14/23 showed progressive disease (61 %). Stable disease was found in 1 (4 %), partial remission in 1 (4 %) and complete remission in 7 (30 %) patients. The relative increase of the current during ablation was significantly higher in the group treated successfully as compared to the group presenting local recurrence (p < 0.05). Needle tract seeding was found in three cases (13 %).ConclusionsIRE is not effective for the treatment of lung malignancies. We hypothesize that the energy deposition with current IRE probes is highly sensitive to air exposure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Tritium Module for Calculating the Behavior of Tritium in a Loop of a Reactor Installation with Sodium Coolant

A description of the models and the results of tests of the Tritium module, which is intended for modeling the transport of tritium in the loops of fast reactors, are presented. The characteristics features of the module are alienability, functional relation with other modules of the code, and calculation of model coefficients as a function of the temperature of the coolant and surface of the loops. The module makes it possible to perform calculations of the behavior of tritium, including transport along loops, penetrability through the channel walls, run-off in a cold trap, and flow through leaks in the reactor installation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗