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At least 109 records · Page 6

Measurements of the thermal conductivity of reference liquids using a modified transient hot-wire needle probe

Many power generation processes feature some type of working fluid that plays a critical role in the transfer of heat throughout the system. To optimize thermal efficiencies, an accurate characterization of the temperature-dependent thermal conductivity of the fluid is needed. Herein, this study develops a time-domain Modified Transient Hot-wire Needle Probe to overcome phenomena that have impaired traditional measurement techniques when the fluid is both corrosive and at an elevated temperature. First, the physical design of the instrument is presented to demonstrate features that limit interference from corrosion, electrical conduction, and convective and radiative heat transfer. Next, a governing equation for 1D (radial) heat conduction is fully developed using the thermal quadrupoles technique. A finite-element study is performed for validation. A sensitivity analysis is done to assess the time-dependent influence of critical parameters in the governing equation for several samples. Finally, thermal conductivity measurements are presented for seven different liquid substances by fitting the thermal model to experimental data across a range of temperatures. Reference correlations are included for comparison. The sample thermal conductivity and ambient temperature of the experimental conditions range from 0.125–16.5$\frac{W}{m·K}$ and 10–400°C, respectively. The total uncertainty, comprised of both the experimental design and fitting process, of all measurements is quantified.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The beam test of the lead tungstate calorimeter prototype with SiPM readout at Jefferson Lab

The lead tungstate crystals (PWO4) are a well-known radiator material for precise homogeneous electromagnetic calorimetry. In nuclear physics experiments, the typical way to read out the crystals is using conventional photomultiplier tubes (PMTs). In the case of the presence of a magnetic field, such a method has some complications and requires a passive PMT shielding design. The recent development of Silicon Photomultipliers (SiPMs) with small pixel pitch sizes and relatively small noise levels opens an opportunity avenue to use such a device as an alternative to conventional PMTs. The SiPM readout method may be beneficial for future nuclear physics and collider experiments. We are presenting the beam test results of the calorimeter prototype instrumented with SiPM readout performed at Jefferson Lab. The SiPM readout performance was compared with PMT readout measured at the same experimental conditions. The prototype was successfully tested using beam of photons in the energy range between 3 GeV and 11 GeV in experimental Hall D. The detector performance was also studied using a secondary beam of positrons provided by Hall D pair spectrometer.

Berdnikov, Vladimir↗

Evaluating bloom potential of the green-tide forming alga Ulva ohnoi under ocean acidification and warming

Herein we discuss the occurrence of green-tides, whose bloom potential may be increased by various human activities and biogeochemical process, results in enormous economic losses and ecosystem collapse. In this study, we investigated the ecophysiology of the subtropical green-tide forming alga, Ulva ohnoi complex (hereafter: U. ohnoi), under simulated future ocean conditions in order to predict its bloom potential using photosynthesis and growth measurements, and stable isotope analyses. Our mesocosm system included four experimental conditions that simulated the individual and combined effects of elevated CO 2 and temperature, namely control (450 μatm CO 2 & 20 °C), acidification (900 μatm CO 2 & 20 °C), warming (450 μatm CO 2 & 25 °C), and greenhouse (900 μatm CO 2 & 25 °C). Photosynthetic electron transport rates (rETR) increased significantly under acidification conditions, but net photosynthesis and growth were not affected. In contrast, rETR, net photosynthesis, and growth all decreased significantly under elevated temperature conditions (i.e. both warming and greenhouse). These results represent the imbalance of energy metabolism between electron transport and O 2 production that may be expected under ocean acidification conditions. This imbalance appears to be related to carbon and nitrogen assimilation by U. ohnoi. In particular, 13 C and 15 N discrimination data suggest U. ohnoi prefers CO 2 and NH 4 + over HCO 3 - and NO 3 - as sources of carbon and nitrogen, respectively, and this results in increased N content in the thallus under ocean acidification conditions. Together, our results suggest a trade-off in which the bloom potential of U. ohnoi could increase under ocean acidification due to greater N accumulation and through the saving of energy during carbon and nitrogen metabolism, but that elevated temperatures could decrease U. ohnoi's bloom potential through a decrease in photosynthesis and growth.

54 ENVIRONMENTAL SCIENCES↗

Dry in-pile fracture test (DRIFT) for separate-effects validation of ceramic fuel fracture models

Fracture is an important component of nuclear fuel behavior, and significant efforts have been invested into developing fuel performance models that are capable of accurately representing fracture. Usable data on the process of fracture propagation in nuclear fuel under realistic service conditions are very limited. To address this need, a series of separate-effects experiments were developed and performed at Idaho National Laboratory's Transient Reactor Test (TREAT) facility. These experiments employ a heat sink to radially remove heat from the fuel in a manner that approximates the effect of coolant in an operating light-water reactor (LWR). The test holder for these experiments is known as the Dry In-pile Fracture Test (DRIFT). A series of experiments employing DRIFT and TREAT were performed to provide data on the extent and nature of fracture in fresh fuel at various points during a ramp to full power. Novel aspects of these experiments include the way they employ a heat sink to replicate steady-state LWR conditions, as well as the use of fiber optic sensors for in-reactor thermal instrumentation. Details on the development of this experiment, experimental conditions, and resulting data (including in situ thermal measurements and post-irradiation imaging of fracture) are provided in this work. LWR-equivalent powers ranging from 10 to 25 kW/m were tested using this apparatus. Cracking was visible at all power levels, with increasing cracking extent as the power level increased, although there was little difference in the cracking between the two highest-power tests, which had LWR-equivalent powers of 20 and 25 kW/m.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Optimized pulsed sideband cooling and enhanced thermometry of trapped ions

Resolved sideband cooling is a standard technique for cooling trapped ions below the Doppler limit to near their motional ground state. Yet the most common methods for sideband cooling implicitly rely on low Doppler-cooled temperatures and tightly confined ions and they cannot be optimized for different experimental conditions. Here we introduce a framework which calculates the fastest possible pulsed sideband cooling sequence for a given number of pulses and set of experimental parameters and we verify its improvement compared to traditional methods using a trapped 171 Yb + ion. After extensive cooling, we find that the ion motional distribution is distinctly nonthermal and thus not amenable to standard thermometry techniques. We therefore develop and experimentally validate an improved method to measure ion temperatures after sideband cooling. These techniques will enable more efficient cooling and thermometry within trapped-ion systems, especially those with high initial temperatures or spatially extended ion wave packets.

74 ATOMIC AND MOLECULAR PHYSICS↗

Triplet Photosensitized para -Hydrogen Induced Polarization

Despite its enormous utility in structural characterization, nuclear magnetic resonance (NMR) spectroscopy is inherently limited by low spin polarization. One method to address the low polarization is para-hydrogen (p-H 2 ) induced polarization (PHIP) which uses the singlet spin isomer of H 2 to generate disparate nuclear spin populations to amplify the associated NMR signals. PHIP often relies on thermal catalysis or, more infrequently, UV-activated catalytic hydrogenation. Light-activated hydrogenation enables direct and timed control over the hyperpolarization of target substrates, critical for identifying short-lived intermediates. Here, we use an established Ir(III) triplet photosensitizer (PS) to visible light sensitize the triplet ligand-field states in the d6-transition metal dihydride Ru(CO)(PPh 3 ) 3 (H) 2 (1). Excitation inside a 9.4 T NMR spectrometer with the PS and a 420 nm blue LED, under 3 atm of p-H 2 , successfully photosensitized hyperpolarization in 1 and in a range of unsaturated substrates at and below room temperature, up to 1630-fold. In otherwise identical experimental conditions without light activation, no polarization was realized in 1 or the substrates evaluated. We believe triplet-sensitized PHIP (Trip- PHIP) represents a facile experimental means for probing triplet sensitized light activation in transition metal catalysts possessing low-lying triplet ligand-field states, providing mechanistic insight of potentially tremendous value in chemical catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Kinetic simulations of piston-driven collisionless shock formation in magnetized laboratory plasmas

Laboratory laser experiments offer a novel approach to studying magnetized collisionless shocks, and a common method in recent experiments is to drive shocks using a laser-ablated piston plasma. However, current experimental capabilities are still limited to spatio-temporal scales on the order of shock formation, making it challenging to distinguish piston and shock dynamics. We present quasi-1D particle-in-cell simulations of piston-driven, magnetized collisionless shock formation using the code psc , which includes a model of laser-driven plasmas that can be well-matched to experimental conditions. The simulations cover a range of upstream and ablation parameters and yield several robust signatures of shock formation that can provide a reference for experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗

Molecular Modeling to Increase Kraft Pulp Yield

Kraft pulping is an important component of the pulp and paper industry and is the predominant technology for removing lignin from wood carbohydrates. However, kraft pulping is energy-intensive, expensive, and is limited by the degradation of cellulose and hemicellulose. Pretreatment increases yield by stabilizing cellulose against degradation. However, protection of galactoglucomannan (GGM), the primary hemicellulose component of softwood, is minimal when conventional pretreatments are used. Here we investigate the effectiveness of new pretreatment methods on southern pine wood chips under a range of experimental conditions. If successful, improved pretreatment methods will increase carbohydrate yield, reduce waste, reduce energy use, lower the cost of bleaching, and decrease the cost of air emission controls. The purpose of this CRADA was to combine industrial expertise in wood pulping with national laboratory expertise in high-performance computing, leading to improved understanding of molecular-scale processes that limit carbohydrate yield during pretreatment and pulping. A combined computational and experimental approach was used to investigate pretreatment effectiveness under relevant pulping conditions and then use molecular simulation techniques to provide complementary insight into structural and chemical factors that govern the observed behavior. In this report we summarize the accomplishments of the project.

59 BASIC BIOLOGICAL SCIENCES↗

Smart Preprocessing & Robust Integration Emulator

To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.

Xia, Yidong [Idaho National Laboratory (INL), Idah↗

Revisiting Experimental Signatures of the Ponderomotive Force

The classical theory of single-electron dynamics in focused laser pulses is the foundation of both the relativistic ponderomotive force (RPF), which underlies models of laser-collective-plasma dynamics, and the discovery of novel strong-field radiation dynamics. Despite this bedrock importance, consensus eludes the community as to whether acceleration of single electrons in vacuum has been observed in experimental conditions. We analyze an early experiment on the RPF with respect to several features that were neglected in modeling and that can restore consistency between theory predictions and experimental data. The right or wrong pulse profile function, laser parameters, or initial electron distribution can each make or break the agreement between predictions and data. The laser phase at which the electron’s interaction with the pulse begins has a large effect, explaining why much larger energies are achieved by electrons liberated in the focal region by photoionization from high-Z atoms and by electrons ejected from a plasma mirror. Finally, we compute the difference in a typical electron spectrum arising from fluctuating focal spot size in state-of-the-art ultra-relativistic laser facilities. Our results emphasize the importance of thoroughly characterizing laser parameters in order to achieve quantitatively accurate predictions and the precision required for discovery science.

Hegelich, Bjorn Manuel↗

Experimental and theoretical study of the restrained shrinkage cracking of early age well cement

The shrinkage cracking occurring in oil & gas well cement material is a major factor that reduces the cement sheath integrity and impairs the durability of an underground well. Shrinkage mainly takes place in the early hydration period of the cement, and the resulting restrained shrinkage cracking can form a leaking network for oil & gas or carbon dioxide stored in an underground reservoir. It is thus important to understand the early age behavior of well cement and the leaking pathway formation in the well. This study describes the application of the digital image correlation (DIC) method as a tool to measure the strain development and crack distributions of well cement in the lab during early phase changes. A theoretical model is presented to calculate the stress distribution built up in the cement sheath during early ages of well cement hydration, which helps to interpret the experimental observations and determine the major factors influencing the cracking sequences. This study presents the whole hydration – drying shrinkage – and cracking mechanism of early age well cement under restrained ring conditions experimentally and theoretically. Finally, the results offer useful insights on the key material and geometric parameters which control the fracture of a well cementing structure.

36 MATERIALS SCIENCE↗

Implementation of a Drift Flux Model into SAM with Development of a Verification and Validation Test Suite for Modeling of Noncondensable Gas Mixtures

The advanced thermal-hydraulic system code, System Analysis Module (SAM), was originally developed for the modeling of single-phase flow in advanced reactors. It has since been expanded to include a four-equation drift flux model for the modeling of two-phase flows containing a noncondensable gas. The model was expanded to support the modeling of molten salt reactor (MSR) designs in which the fuel is directly dissolved in the circulating coolant. These designs have shown that circulating gas bubbles can play an important role in the management of fission products and the operational behavior of the reactor. A drift flux model was implemented to more accurately capture the localized behavior of the void in the core and its impact on the mass transfer of fission products. A thorough assessment of the new model was performed by developing a verification and validation test suite. Verification problems were designed to test all major terms in the new governing equations. The new model converged to the correct solution at the expected order of accuracy for all verification cases. The validation cases included a wide range of flow and void conditions in different pipe geometries. Although higher void experiments show a slight underprediction of void by the drift flux model, experiments that aim to reproduce Molten Salt Reactor Experiment (MSRE) experimental conditions show good agreement with the model. The gas transport model was activated for a SAM model of the MSRE to demonstrate that it can be used in a more complex model. Finally, this gas transport model will be used along with an interfacial area transport equation being implemented in SAM for the prediction of mass transport behavior in MSR conditions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Review of Swift Heavy Ion Irradiation Effects in CeO2

Cerium dioxide (CeO2) exhibits complex behavior when irradiated with swift heavy ions. Modifications to this material originate from the production of atomic-scale defects, which accumulate and induce changes to the microstructure, chemistry, and material properties. As such, characterizing its radiation response requires a wide range of complementary characterization techniques to elucidate the defect formation and stability over multiple length scales, such as X-ray and neutron scattering, optical spectroscopy, and electron microscopy. In this article, recent experimental efforts are reviewed in order to holistically assess the current understanding and knowledge gaps regarding the underlying physical mechanisms that dictate the response of CeO2 and related materials to irradiation with swift heavy ions. The recent application of novel experimental techniques has provided additional insight into the structural and chemical behavior of irradiation-induced defects, from the local, atomic-scale arrangement to the long-range structure. However, future work must carefully account for the influence of experimental conditions, with respect to both sample properties (e.g., grain size and impurity content) and ion-beam parameters (e.g., ion mass and energy), to facilitate a more direct comparison of experimental results.

42 ENGINEERING↗

Enhancing positron production using front surface target structures

We report a target design which produced a substantial gain in relativistic electron-positron pair production using high-intensity lasers and targets with large-scale micro-structures on their surface. Comparing to an unstructured target, a selected Si microwire array target yielded a near 100% increase in the laser-to-positron conversion efficiency and produced a 10MeV increase in the average emitted positron energy under nominally the same experimental conditions. We had established a multi-scale particle-in-cell simulation scheme to simulate both the laser absorption and the subsequent pair productions in a thick metal target. Here, the experimental results are supported by the simulations demonstrating the performance increase is due to a higher conversion efficiency of laser energy into electrons with kinetic energies greater than 10 MeV due to enhanced direct laser acceleration of electrons enabled by the microwire array.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Visualization of R410A Flow Boiling Inside Horizontal Smooth Tubes under Diabatic Conditions

This article presents a novel approach to visualize the flow boiling in round tubes under “diabatic conditions”. In the visualization test section, a 3D printed round tube is placed inside a glass tube with secondary fluid at a higher temperature flowing in the annulus between the two tubes thus providing diabatic conditions. Experimental work was conducted for evaporating R410A flow in a horizontal smooth round tube with the inner diameter of 6.3 mm. Compared to the traditional adiabatic visualization technique, the more realistic evaporative condition is simulated and the developing evaporation process is captured. New insights for the flow behavior at low vapor quality are brought by this diabatic method. The vapor plug occurs at very low vapor quality, and this is attributed to the coalescence of bubbles generated on the heated tube wall. For the intermittent flow, liquid slug triggered by Kelvin–Helmholtz instability affects the nucleation on the tube wall. It brings the fresh liquid to rewet the dry surface and activate the nucleation sites. Finally, heat flux condition affects the heat transfer mechanisms during the flow boiling, and the heat transfer through the nucleate boiling mechanism increases as the heat flux increases.

3D printed↗

Insight into premixed diethoxymethane flames: Laminar burning velocities, temperatures, and emissions behaviour

Diethoxymethane ((CH 3 CH 2 O) 2 CH 2 , DEM) is a promising carbon-neutral fuel. DEM is a diether or acetal with a molecular structure similar to oxymethylene ethers (CH 3 O–(CH 2 O)n–CH 3 , OME n ). Thus, DEM can be expected to have a similar combustion behavior to OMEs, reducing harmful emissions such as NO x and particulate matter (PM) in internal combustion engines. From both experimental and kinetic modeling, fundamental studies on DEM are scarce in the literature. More studies are required to gain a detailed insight into the oxidation kinetics of DEM. Laminar burning velocity (LBV) is a critical property that allows a detailed assessment of the potential application of DEM in combustion devices. Unfortunately, the literature on the LBV of DEM is limited. Therefore, in this study we have investigated the LBV of DEM using two reactors for the first time, namely a heat flux burner and a combustion chamber. The experimental data is reported for equivalence ratio between 0.7 and 1.7, initial temperatures of 368–423 K, and initial pressure of 1–5 bar. In addition, we developed a detailed kinetic model extending our recent work of Shrestha et al. (Combust. Flame. 246 (2022) 112,426) to characterize the combustion behavior of DEM utilizing the new experimental data from this work and the literature data. Our model performs remarkably well in capturing the newly measured LBV experimental data over various experimental conditions. We found that DEM and dimethoxy methane (DMM) have similar values of LBVs (within ±1.5 cm/s) for a given condition, which indicates that intermediate chemistry governs the flame chemistry. Despite DEM being a larger molecule that is expected to have slightly lower LBVs than DMM, its effect on the measured values of LBVs is negligible. Finally, we experimentally measured NO x formation in DEM flame for the first time. The stochiometric flame has the highest NO x formation. The proposed model predicted the equivalence ratio dependence of NO x nicely. However, it overestimates the NO x formation for stoichiometric DEM/air mixtures by ~30 %. The model suggests that the thermal NO formation route is favored at lean and stochiometric conditions. In contrast, the prompt NO formation route is enhanced for rich mixtures.

10 SYNTHETIC FUELS↗

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗