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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Experimental and Computational Characterization of a Modified Sioutas Cascade Impactor for Respirable Radioactive Aerosols

Oak Ridge National Laboratory is collecting and characterizing aerosols released when spent nuclear fuel (SNF) rods are fractured in bending. An aerosol collection system was designed and tested to collect respirable sized (<10 μm aerodynamic diameter [AED]) particulates inside a hot cell facility. The setup is a modified version of the commercially available Sioutas cascade impactor, to which additional stages were added to expand the aerosol collection range from 2.5 to ~15 μm AED. To accommodate the additional stages and specific test conditions, the operating flow rate for aerosol collection was reduced, and testing was conducted by using pressure drop measurements, surrogate dust collection, and particle size characterization. The fluid flow distribution within the cascade and its stages was simulated in STAR-CCM+, and the stage-wise pressure drops obtained using the computational fluid dynamics model were then compared to experimental data. Lagrangian particle simulations were also performed, and stage-wise collection statistics were obtained from the simulation for comparison with the experimental data obtained using SNF-surrogate dust particles. The results provide valuable insights into the stage-wise particle collection characteristics of the modified cascade impactor and can also be used to improve the prediction accuracy of the manufacturer-determined analytical correlations.

aerosol modeling↗
DOE OSTI2025

BlueCRAB Domain Overlapping Coupling: Theory & Verification

Coupling low- and high-fidelity codes is a useful way to model complex engineering systems. Thanks to the high-fidelity code, complex phenomena can be resolved in areas of the system where this is required, yet the efficiency of the lower-fidelity code is still retained in modeling the rest of the system. This document details the theory and implementation of coupling two different thermal-hydraulic codes: the system thermal-hydraulics (STH) code System Analysis Module (SAM) and the coarse-mesh computational fluid dynamics (CFD) code Pronghorn. Both applications are included in the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite and are based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. The domain overlapping (DO) coupling approach was adopted, as it offers proven advantages over more conventional domain decomposition methods. In the DO coupling, SAM provides Pronghorn with boundary conditions that depend on the system-level simulation of the entire plant. In return, the overlapping coupled SAM components–termed “surrogate components”–receive friction factors and source terms computed online based on the Pronghorn simulation. The framework is designed to be generic and enable coupling regardless of geometry and the number of inlet/outlet boundaries in the DO coupled domain. The developed method leads to consistent pressure drops, enthalpies, and scalar concentrations when comparing the coupled SAM and Pronghorn simulations. This document presents the DO coupling approach, along with two verification and two demonstration cases. The proposed problems explore different physical aspects relevant to nuclear reactor analysis, including buoyancy-driven flows, complex flow patterns, and multiple inlets and outlets. Periodically, new versions of this “BlueCRAB Domain Overlapping Coupling: Theory & Verification” report will be issued to reflect future developments and verification tests.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2011

Radiation-Spray Coupling for Realistic Flow Configurations

Three Large Eddy Simulations (LES) for a lean-direct injection (LDI) combustor are performed and compared. In addition to the cold flow simulation, the effect of radiation coupling with the multi-physics reactive flow is analyzed. The flame let progress variable approach is used as a subgrid combustion model combined with a stochastic subgrid model for spray atomization and an optically thin radiation model. For accurate chemistry modeling, a detailed Jet-A surrogate mechanism is utilized. To achieve realistic inflow, a simple recycling technique is performed at the inflow section upstream of the swirler. Good comparison is shown with the experimental data mean and root mean square profiles. The effect of combustion is found to change the shape and size of the central recirculation zone. Radiation is found to change the spray dynamics and atomization by changing the heat release distribution and the local temperature values impacting the evaporation process. The simulation with radiation modeling shows wider range of droplet size distribution by altering the evaporation rate. The current study proves the importance of radiation modeling for accurate prediction in realistic spray combustion configurations, even for low pressure systems.

El-Asrag, Hossam↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗
DOE OSTI2020

Probing the low-temperature chemistry of methyl hexanoate: Insights from oxygenate intermediates

Understanding the combustion of methyl esters is crucial to elucidate kinetic pathways and predict combustion parameters, soot yields, and fuel performance of biodiesel, however most kinetic studies of methyl esters have focused on smaller, surrogate model esters. Methyl hexanoate is a larger methyl ester approaching the chain length of methyl esters found in biodiesel and has not received as much research attention as other smaller esters. The purpose of this work is to present the first atmospheric pressure combustion data of methyl hexanoate, CH 3 CH 2 CH 2 CH 2 CH 2 COOCH 3 . Mixtures of 2% methyl hexanoate in O 2 and N 2 are studied using a plug flow reactor at atmospheric pressure, wall temperatures from 573 to 973 K, residence times from roughly 1-2 s., and fuel equivalence ratios of 1, 1.5, and 2. Exhaust gases are analyzed by a gas chromatograph-mass spectrometer system and species mole fractions are presented. Here, the literature model shows satisfactory agreement with the experimental species profiles and improvements for future mechanistic studies are suggested. In particular, this work proposes new unimolecular decomposition pathways of methyl hexanoate to form methanol or methyl acetate. Furthermore, the experiment detected three unsaturated esters that are direct products of the low temperature oxidation chemistry and it provides more insight into branching ratios for the formation of methyl hexanoate radicals and for the decomposition of hydroperoxyalkyl radicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Risk-Adaptive Experimental Design for High-Consequence Systems: LDRD Final Report

Constructing accurate statistical models of critical system responses typically requires an enormous amount of data from physical experiments or numerical simulations. Unfortunately, data generation is often expensive and time consuming. To streamline the data generation process, optimal experimental design determines the 'best' allocation of experiments with respect to a criterion that measures the ability to estimate some important aspect of an assumed statistical model. While optimal design has a vast literature, few researchers have developed design paradigms targeting tail statistics, such as quantiles. In this project, we tailored and extended traditional design paradigms to target distribution tails. Our approach included (i) the development of new optimality criteria to shape the distribution of prediction variances, (ii) the development of novel risk-adapted surrogate models that provably overestimate certain statistics including the probability of exceeding a threshold, and (iii) the asymptotic analysis of regression approaches that target tail statistics such as superquantile regression. To accompany our theoretical contributions, we released implementations of our methods for surrogate modeling and design of experiments in two complementary open source software packages, the ROL/OED Toolkit and PyApprox.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Characterizing the impact of water uptake on light absorption by aerosol particles

Absorbing aerosols (AA) have an important impact on the global radiation budget. The composition and properties of AA can vary substantially throughout the atmosphere, depending on the particle source and the influence of chemical aging. Uncertainties associated with the radiative effects of AA remain substantial. A key contributor to this uncertainty is understanding the extent to which water uptake alters absorption by AA particles and how this depends on particle composition. We have used new and existing experimental tools to systematically characterize the relationship(s) between AA chemical and physical characteristics, relative humidity, and mixing of AA with other aerosol components through a combination of laboratory and field measurements. We have developed fundamental understanding through laboratory experiments that will enable interpretation of field observations at DOE ARM sites and facilitate process-based improvements in the simulation of absorption by carbonaceous aerosols in climate models. In the laboratory experiments we have considered a variety of different AA types, including fullerene soot (a surrogate for refractory black carbon, or BC) and nigrosine (a surrogate for partially soluble brown carbon, or BrC). By examining a range of AA types we will develop insight into how the chemical and physical properties of AA particles (e.g. whether they are refractory or soluble) impact the influence of water uptake on absorption. These AA particles will also be mixed—both internally and externally—with compounds having a wide range of hygroscopic properties to establish how the impact of water uptake on absorption will vary with photochemical processing. These laboratory observations have been compared with commonly used theoretical models, enabling development of empirical models that will facilitate improved representation of absorption by AA particles in regional and global climate models. Overall, our study has contributed to the mission of the Atmospheric System Research program by quantifying how interactions between aerosols and radiation depend on relative humidity. Through this, our work will improve understanding and model representation of aerosol processes as they affect the Earth’s radiation budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bridging microscale to macroscale mechanical property measurements of FeCrAl alloys by crystal plasticity modeling

FeCrAl alloys are candidates for accident tolerant fuel cladding of light water reactors. In this work, a microstructure- and temperature-dependent crystal plasticity model is employed to bridge microscale to macroscale mechanical property measurements of FeCrAl alloys. With the visco-plastic self-consistent (VPSC) polycrystal plasticity framework, a mechanism-based single crystal plasticity (MSCP) model adopts the Arrhenius type rate equation to describe the dependence of the critical resolved shear stress for dislocation slips on their temperature-dependent intrinsic frictional resistance and the microstructure-dependent irradiation hardening. The intrinsic frictional resistance associated with {110}<111> and {112}<111> slip systems were measured by in-situ micromechanical testing on unirradiated/irradiated samples at 25-500 °C. The irradiation hardening is estimated by the Bacon-Kocks-Scattergood (BKS) model with density and size of radiation-induced defects measured from microstructural characterization. Several features associated with thermo-mechanical behavior of unirradiated/irradiated polycrystalline FeCrAl alloys are captured. High density of deformation-induced dislocations and radiation-induced defects results in obvious hardening at room temperature, which is weakened at high temperature, and facilitates damage evolution during deformation. Moreover, both high temperature and radiation-induced defects, which facilitate dislocation multiplication, trigger large hardening rate. Finally, the proposed method together with application of accelerator-based ion irradiation technique is a surrogate approach to simulate neutron damage, improving the efficiency associated with evaluation of mechanical properties of FeCrAl alloys exposed to temperature, stress and radiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Symplectic neural network and its application to charged particle dynamics in electromagnetic fields

Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

PELICAN Design, Test Planning, and Commissioning Results

To support design efforts for the Versatile Test Reactor (VTR) core assemblies, an experimental facility has been designed and constructed at Argonne National Laboratory to match the hydraulic flow conditions within the VTR’s primary heat transport system (PHTS). This facility, the Pressure drop Experimental Loop for Investigations of Core Assemblies in advanced Nuclear reactors, PELICAN, provides the ability to measure pressure drop across a full-scale fuel assembly containing prototypic axial reflectors, fuel, and plena components. The PELICAN facility was designed and built to offer maximum flexibility, allowing testing from short sub-sections all the way to the full-length core fuel assemblies. The report presents the high-level program objectives, a summary of the facility design, instrumentation, and control systems, and the testing procedure. The outcomes from facility characterization efforts and first test matrix results are then presented and then, finally, the conclusion contains a summary of the future work to be performed. This test facility was designed to match the hydraulic conditions of the flowing sodium in the VTR using water as a surrogate fluid. To do so, the water is elevated to a temperature of 110°C where its viscosity matches that of sodium, and with a 50-HP centrifugal pump, is capable of generating full scale flow rates to achieve prototypic Reynolds and Euler number flow conditions. To prevent boiling, the system is maintained at elevated pressure of at least 2.7-3.0 bar (40-44 psig). A set of 15 tests have been used to perform checkout activities in order to commission the device, survey the capabilities of PELICAN, and generate experimental data at isothermal conditions and over a range of flow rates up to 44 kg/s (700 GPM) to produce datasets used in the verification and validation of VTR design and modeling efforts. Initial testing focused on facility characterization, which assessed the operational capabilities of various control systems. The thermal control system was qualified, including the thermal response of the loop to the heat added by self- and auxiliary heaters, the chiller and heat exchanger systems for removing excess heat, and their coupled ability to maintain steady-state isothermal conditions as desired. Additionally, the pressure control system was verified to ensure necessary operating environments that promote pump health and prevent boiling of loop inventory at high temperatures could be met. With these systems in place, the first set of matrix tests have been carried out using orifice plates with inner diameters of 2.25-inches and 2.5-inches. Orifice plate flow behavior is well covered in scientific literature, and the results find good agreement with the measured pressure drop vs flow rate and those of theoretical predictions. This initial testing not only helps to validate the experiment, but it also produces a high quality data set for a geometry that is easily reproducible for simulations. Finally, the report is concluded with a discussion of the work to come and provides a snapshot of the test articles in the experimental pipeline whose designs are being inspired by the current designs from the VTR.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2002

Left ventricular outflow tract mean systolic acceleration as a surrogate for the slope of the left ventricular end-systolic pressure-volume relationship

OBJECTIVE: The goal of this study was to analyze left ventricular outflow tract systolic acceleration (LVOT(Acc)) during alterations in left ventricular (LV) contractility and LV filling. BACKGROUND: Most indexes described to quantify LV systolic function, such as LV ejection fraction and cardiac output, are dependent on loading conditions. METHODS: In 18 sheep (4 normal, 6 with aortic regurgitation, and 8 with old myocardial infarction), blood flow velocities through the LVOT were recorded using conventional pulsed Doppler. The LVOT(Acc) was calculated as the aortic peak velocity divided by the time to peak flow; LVOT(Acc) was compared with LV maximal elastance (E(m)) acquired by conductance catheter under different loading conditions, including volume and pressure overload during an acute coronary occlusion (n = 10). In addition, a clinically validated lumped-parameter numerical model of the cardiovascular system was used to support our findings. RESULTS: Left ventricular E(m) and LVOT(Acc) decreased during ischemia (1.67 +/- 0.67 mm Hg.ml(-1) before vs. 0.93 +/- 0.41 mm Hg.ml(-1) during acute coronary occlusion [p < 0.05] and 7.9 +/- 3.1 m.s(-2) before vs. 4.4 +/- 1.0 m.s(-2) during coronary occlusion [p < 0.05], respectively). Left ventricular outflow tract systolic acceleration showed a strong linear correlation with LV E(m) (y = 3.84x + 1.87, r = 0.85, p < 0.001). Similar findings were obtained with the numerical modeling, which demonstrated a strong correlation between predicted and actual LV E(m) (predicted = 0.98 [actual] -0.01, r = 0.86). By analysis of variance, there was no statistically significant difference in LVOT(Acc) under different loading conditions. CONCLUSIONS: For a variety of hemodynamic conditions, LVOT(Acc) was linearly related to the LV contractility index LV E(m) and was independent of loading conditions. These findings were consistent with numerical modeling. Thus, this Doppler index may serve as a good noninvasive index of LV contractility.

Non-NASA Center↗