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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrated frequency-modulated optical parametric oscillator

Optical frequency combs have revolutionized precision measurement, time-keeping, and molecular spectroscopy. A substantial effort has developed around "microcombs": integrating comb-generating technologies into compact, reliable photonic platforms. Current approaches for generating these microcombs involve either the electro-optic (EO) or Kerr mechanisms. Despite rapid progress, maintaining high efficiency and wide bandwidth remains challenging. Here, we introduce a new class of microcomb -- an integrated optical frequency comb generator that combines electro-optics and parametric amplification to yield a frequency-modulated optical parametric oscillator (FM-OPO). In stark contrast to EO and Kerr combs, the FM-OPO microcomb does not form pulses but maintains operational simplicity and highly efficient pump power utilization with an output resembling a frequency-modulated laser. We outline the working principles of FM-OPO and demonstrate them by fabricating the complete optical system in thin-film lithium niobate (LNOI). We measure pump to comb internal conversion efficiency exceeding 93% (34% out-coupled) over a nearly flat-top spectral distribution spanning approximately 1,000 modes (approximately 6 THz). Compared to an EO comb, the cavity dispersion rather than loss determines the FM-OPO bandwidth, enabling broadband combs with a smaller RF modulation power. The FM-OPO microcomb, with its robust operational dynamics, high efficiency, and large bandwidth, contributes a new approach to the field of microcombs and promises to herald an era of miniaturized precision measurement, and spectroscopy tools to accelerate advancements in metrology, spectroscopy, telecommunications, sensing, and computing.

42 ENGINEERING↗
DOE OSTI2021

Considerations for testing full-scale wind turbine nacelles with hardware-in-the-loop

In this paper, pole placement control techniques are utilized to compensate for variations in the torsional dynamics resulting from the different boundary conditions experienced by a wind turbine nacelle when mounted on a test bench. Simulation case studies are used to understand the baseline torsional characteristics of the nacelle coupled to the test bench versus that coupled to the rotor. It was found that the frequency shifts by up to 200% and 400% for the first and second torsional modes, respectively. The feasibility of utilizing pole placement technique to match the torsional characteristics of the turbine on the test bench is investigated using PI and PID controllers. Here, the performance of the tuned controller is then verified under two test scenarios: Low Voltage Ride Through (LVRT), and a highly dynamic turbulent wind input. It is demonstrated that tuning the test bench speed controller can effectively shift the poles of the closed-loop system to match the desired first and second modes of the full turbine and replicate transient field events on the test bench.

17 WIND ENERGY↗
DOE OSTI2021

A pushered capsule implosion as an alternate approach to the ignition regime for inertial confinement fusion

We report in inertial confinement fusion, the threshold for ignition is a highly dynamic quantity as the sources and sinks of power in the hot spot can vary rapidly. In this article, we consider the ignition condition as a race between heating and disassembly rates and make use of a prior solution to the fusion hot-spot thermodynamics to develop a Lawson-like ignition criteria for pressure × confinement time (p-τ) vs temperature. Low-Z capsule designs reach the temperature for this threshold using as much of the shell as feasible as ablator but then are limited in τ by low stagnated mass. An alternate approach, the pushered single shell (PSS) design [D. D.-M. Ho, S. MacLaren, and Y. Wang, “High-yield implosions via radiation trapping and high rho-R,” paper presented at the 60th Annual Meeting of the APS Division of Plasma Physics, 2018], introduces a dense inner layer of Mo-Be alloy that is smoothly graded outward to pure Be, increasing the confinement time at stagnation and lowering the temperature requirement at the ignition threshold. Here, we describe a PSS ignition design for the National Ignition Facility and use the theory as well as simulations to compare it with the low-Z capsule approach. Additionally, we show how an adjustment to the design is used to anticipate the effects of mixing at the fuel–ablator interface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2020

Hydrodynamic instabilities and heat transfer characteristics in the duct flow of a fluid in the supercritical thermodynamic regime

The behavior of fluids at supercritical thermodynamic conditions is inherently complex due to large variations in thermodynamic and transport properties. Recent numerical and experimental investigations illustrate ongoing interest for these fluids, especially supercritical CO 2 and supercritical water, for a variety of applications. For example, supercritical water reactors (SCWR) operate in this extreme condition of high-pressure and temperature, resulting in highly dynamic flow fields and unexpected heat transfer regimes. The potential heat transfer benefits in this regime are directly associated with the extreme variations in thermodynamic and transport properties, which occur at, and above, the critical point. This work characterizes the hydrodynamic instabilities that arise for fluids at supercritical thermodynamic conditions when buoyancy forces are significant. Two specific configurations are considered, a natural convection cavity flow, and a mixed convection, heated, horizontal channel flow. Natural convection flow in a cavity is a classical configuration with expected behavior below the critical point. This configuration aids in characterizing the effect of the variable properties in the supercritical thermodynamic regime. Further, limited studies in the existing literature have been conducted for low-Reynolds and intermediate-Rayleigh numbers, mixed-convection channel flows for supercritical water, which is the focus of the channel flow configuration. To investigate the thermally driven hydrodynamic instabilities in this regime, a high-order fully-implicit numerical method is used. Such strong variations in thermophysical properties (in particular, density) are difficult to simulate and an altogether compressible framework is needed. Therefore, the compressible Navier-Stokes equations are solved without any additional assumptions. The fully implicit, high-order in space and time, reconstructed discontinuous Galerkin method as implemented within the multi-physics code called ALE3D (Arbitrary Lagrangian and Eulerian in 2D and 3D), developed at Lawrence Livermore National Laboratory (LLNL), is used. This fully implicit, L-stable method accurately captures the compressible nature of the ow in the limit of very low Mach number. It has been widely accepted that above the critical point, only one phase is observed. However, recent research has indicated the existence of the distinct gas-like and liquid-like regions separated by the Widom line, the locus of the maxima of the specific heat. Along the Widom line, density decreases 6-fold, viscosity drops by a factor of 2, while specific heat spikes by an order of magnitude. These variations, specifically in density and viscosity, produce a thick pseudo-interface and flow dynamics behavior akin to film boiling. A pseudo-film at the heated wall of the cavity and the horizontal channel is observed where buoyancy forces induce mixing through the specific configurations. Further the local Rayleigh and Richardson numbers provide maps of the flow field and the buoyancy forces driving the microscopic mixing. In the first chapter, I describe a background of supercritical fluid and the various applications. The second chapter focuses on the mathematical model and numerical method used for simulations, where a description of the equation of state for supercritical water is described. The third chapter focuses on the natural convection cavity with a heated bottom wall. In this cavity a gas-like and a liquid-like flow within the supercritical thermodynamic regime are observed. The fourth chapter focuses on a forced convection, horizontal channel, distinguishing between the gas-like, liquid-like, and mixed flow regimes. Mixed convection flow, with the addition of gravitational forces in the horizontal channel show the influence of variable properties on the hydrodynamic development, heat transfer, and rising instabilities. The last chapter of this research focuses on characterizing the unstable hydrodynamics through time-averaging processes and analysis of the movement of energy through the developing plumes.

42 ENGINEERING↗
DOE OSTI2021

Ab-Initio Simulation Studies of Cr Solvation in Fluoride Molten Salts

Understanding molten salt chemistry is essential in ongoing research of the molten salt nuclear reactor (MSR). In this context, detailed understanding of the mechanisms underlying selective oxidation of metal species, such as Cr, is required to guide the design of effective corrosion mitigation strategies in molten salts. An important starting point for such mechanistic understanding is knowledge of the solvation structure and its role in controlling metal speciation. In this work, we use ab initio molecular dynamics simulations to study the short-range (on the scale of the nearest-neighbor bond lengths) and medium-range (over length scales of several neighbor spacings) structure in three different fluoride melts with and without Cr addition; namely, 2KF-NaF, 2LiF-BeF 2 , and 3LiF-AlF3. We find that Cr 0 ,Cr 2+ ,Cr 3+ can each be coordinated by different numbers of F-, with the variance in coordination number decreasing as oxidation state increases, and that these coordination geometries are largely independent of solvent. The manner by which Cr changes the medium-range structure, however, is found to be solvent-dependent. While 2KF-NaF melts show short and medium range order that is highly dynamic, 2LiF-BeF 2 and 3LiF-AlF 3 are characterized by molecular associates that are relatively long-lived that organize into oligomer structures on larger length scales. Rather than being solvated by F- ions alone, we find that Cr can incorporate into and be solvated within this oligomer structure. Fluoroacidity, alone, may therefore prove too simple a metric for assessing the corrosivity of molten fluorides. As our work suggests, the ability of Cr to solvate must be understood in the context of the short- and medium-range structure of the solvent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Spectroscopy-based isotopic (δ 13 C) analysis for high spatial resolution of carbon exchange in the rhizosphere

The rhizosphere is a highly dynamic zone bridging plant roots with needed nutrient resources in soil. While the rhizosphere may be small, it has a disproportionally large impact on plant success and biomass production. A suite of rhizosphere-hosted microbial and geochemical interactions facilitate nutrient acquisition by plant roots, and, in turn, the roots stimulate these processes by supplying organic carbon into the rhizosphere. The small physical dimensions of the rhizosphere, however, can constrain efforts to elucidate key carbon exchange processes and their spatial extent and localization. We present a method for spatially resolved δ 13 C analysis of rhizosphere samples by coupling laser ablation (LA) sampling with isotopic analysis using capillary absorption spectroscopy (CAS) which differs from conventional mass spectrometer (MS) approaches. The CAS system has high sensitivity (requires fewer nanomoles of CO 2 per analysis) than comparable MS systems, which enables reduced sample size requirements to thereby improve spatial resolution (from 25 μm to as low as a projected 5 μm spatial resolution). We demonstrate the utility of CAS using rhizosphere samples from switchgrass plants exposed to 13 CO 2 . As a result, this technique will provide a capability for tracking the extent and spatial distribution of root exudate into the rhizosphere at highly detailed spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Contact patterns in wild pigs ( Sus scrofa ) from GPS tracking reveal spatial and temporal dynamics of social behaviour

Wild pig ( Sus scrofa Linnaeus, 1758) social behaviour affects disease transmission and landscape-level population management. Recent research has incorporated analysis of social structure to better understand the risk of disease transmission in wild pigs, although the relationship between overall social structure of wild pigs remains unclear. Here, we seek to improve understanding of the spatial and temporal dynamics of wild pig social structure and contact rates to better inform management strategies. Using GPS tracking data, we measured home range overlap, and estimated contact rates of wild pigs at four study sites in the southern USA to identify pairwise social associations (i.e., contacts) based on synchronous movement. Contact rate was strongly associated with home range overlap, but exhibited substantial variation, especially at moderate levels of home range overlap. We found that female–female dyads had higher contact rates and longer duration phases of social association compared to female–male and male–male dyads. We found male–male dyads tended to experience social associations farther from their home range centers than female–female or female–male dyads. Social associations between wild pig dyads are highly dynamic in their spatial and temporal structure. Further, dyads with strong social associations still experience substantial time apart. Our findings highlight the challenges of predicting spatial and social associations in wild pig social pairs due to their dynamic social structure over space and time.

McIlraith, Jack R.↗
DOE OSTI2021

Unexpected inverse correlations and cooperativity in ion-pair phase transfer

Liquid/liquid extraction is one of the most widely used separation and purification methods, where a forefront of research is the study of transport mechanisms for solute partitioning and the relationships that these have to solution structure at the phase boundary. To date, organized surface features that include protrusions, water-fingers, and molecular hinges have been reported. Many of these equilibrium studies have focused upon small-molecule transport – yet the extent to which the complexity of the solute, and the competition between different solutes, influence transport mechanisms have not been explored. Here we report molecular dynamics simulations that demonstrate that a metal salt (LiNO 3 ) can be transported via a protrusion mechanism that is remarkably similar to that reported for H 2 O by tri-butyl phosphate (TBP), a process that involves dimeric assemblies. Yet the LiNO 3 out-competes H 2 O for a bridging position between the extracting TBP dimer, which in-turn changes the preferred transport pathway of H 2 O. Examining the electrolyte concentration dependence on ion-pair transport unexpectedly reveals an inverse correlation with the extracting surfactant concentration. As [LiNO 3 ] increases, surface adsorbed TBP becomes a limiting reactant in correlation with an increased negative surface charge induced by excess interfacial NO 3 – , however the rate of transport is enhanced. Within the highly dynamic interfacial environment, we hypothesize that this unique cooperative effect may be due to perturbed surface organization that either decreases the energy of formation of transporting protrusion motifs or makes it easier for these self-assembled species to disengage from the surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Many-Exciton Quantum Dynamics in a Ruddlesden–Popper Tin Iodide

We present a study on the many-body exciton interactions in a Ruddlesden–Popper tin halide, namely, (PEA) 2 SnI 4 (PEA = phenylethylammonium), using coherent two-dimensional electronic spectroscopy. The optical dephasing times of the third-order polarization observed in these systems are determined by exciton many-body interactions and lattice fluctuations. We investigate the excitation-induced dephasing (EID) and observe a significant reduction of the dephasing time with increasing excitation density as compared to its lead counterpart (PEA) 2 PbI 4 , which we have previously reported in a separate publication [J. Chem. Phys. 2020, 153, 164706]. Surprisingly, we find that the EID interaction parameter is four orders of magnitude higher in (PEA) 2 SnI 4 than that in (PEA) 2 PbI 4 . This increase in the EID rate may be due to exciton localization arising from a more statically disordered lattice in the tin derivative. This is supported by the observation of multiple closely spaced exciton states and the broadening of the linewidth with increasing population time (spectral diffusion), which suggests a static disordered structure relative to the highly dynamic lead-halide. Additionally, we find that the exciton nonlinear coherent lineshape shows evidence of a biexcitonic state with low binding energy (<10 meV) not observed in the lead system. We model the lineshapes based on a stochastic scattering theory that accounts for the interaction with a nonstationary population of dark background excitations. Our study provides evidence of differences in the exciton quantum dynamics between tin- and lead-based Ruddlesden–Popper metal halides (RPMHs) and links them to the exciton–exciton interaction strength and the static disorder aspect of the crystalline structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Muconic Acid Production from P. putida Using High Protein Algae Hydrolysate

The composition of algal biomass is highly dynamic, with protein, lipid, and carbohydrate contents varying in response to nutrient and environmental conditions during cultivation. Because shifts in biomass composition are often associated with reduced biomass productivity, production costs can often increase if targeting higher biomass compositional quality (enriched in carbohydrates or lipids at reduced protein content) as input for the algal biorefinery. The optimal algal biorefinery configuration is thus a function of many factors. One of the key strengths of the Combined Algal Processing (CAP) process is the versatility of feedstocks and products produced. The concept has been demonstrated with ethanol and a variety of carboxylic acids (succinic, butyric, muconic) as coproducts along with lipid upgrading to biofuel. Modification of the approaches, processes and downstream upgrading to fuels has allowed the CAP process to reduce costs and improve efficiency. Muconic acid is a high-value, potential fermentation coproduct of interest because it can be easily converted to adipic acid, a high-volume monomer for the production of nylon and other valuable consumer plastics. As such, the production of muconic acid through CAP was explored to expand the suite of products from algal biomass and to begin exploring the valorization of high protein content biomass from rapidly grown algae biomass. We have established initial performance parameters and shown that the range of substrates consumed by the muconic acid-producing microbe, Pseudomonas putida, includes at least glucose, mannose, glycerol, and lactic acid. We achieved complete utilization of these four major hydrolysate substrates achieving productivities of 0.037 (g/L/h) from Scenedesmus obliquus and 0.029 (g/L/h) from Monoraphidium minutum hydrolysates. Final titer and process yield (mol of muconic acid per mol of substrate (molP/molS)) were 0.99 g/L and 0.42 molP/molS from S. obliquus hydrolysate and 0.75 g/L and 0.23 molP/molS from M. minutum hydrolysate, respectively.

BIOMASS FUELS↗
DOE OSTI2023

Changes in nitrate binding with lanthanides in BLPhen complexes

Preorganized ligands such as bis-lactam-1,10-phenanthroline (BLPhen) offer unique selectivity trends in solvent extraction of rare-earth ions, particularly from aqueous nitrate solutions. However, due to the ligand’s lipophilicity it is experimentally challenging to obtain crystal structures of the corresponding lanthanide (Ln):ligand complexes to describe the local structure of the first coordination shell and the role of the nitrate ions in the organic phase. In this work, we report first principles molecular dynamics (FPMD) simulations of the complexation of trivalent lanthanide ions (La, Nd, and Eu) with the BLPhen ligand in the presence of nitrate ions in the dichloroethane (DCE) solvent. We find that two nitrate anions are present in the first solvation shell and the third nitrate anion is far from the first solvation shell in all three [Ln(BLPhen) 2 ] 3+ complexes examined. Moving along the lanthanide series from La to Nd to Eu, the binding pocket formed by the two BLPhen ligands shrinks in size; as a result, the two nitrate configuration changes from one-monodentate/one-bidentate binding mode for La and Nd to all monodentate for Eu. Meanwhile, the total coordination number drops from 11 for La and Nd to 10 for Eu. More interestingly, the bidentate binding in [La(BLPhen) 2 ] 3+ and [Nd(BLPhen) 2 ] 3+ complexes is highly dynamic, frequently switching to monodentate and back. The FPMD insights into the differing modes and dynamics of nitrates in the first coordination shell will be useful for further atomistic understanding of the complex structure, formation, and stability in the organic phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Twisted A-D-A Type Acceptors with Thermally-Activated Delayed Crystallization Behavior for Efficient Nonfullerene Organic Solar Cells

We report that molecular aggregation and crystallization during film coating play a crucial role in the realization of high-performing organic photovoltaics. Strong intermolecular interactions and high solid-state crystallinity are beneficial for charge transport. However, fast crystallization during thin-film drying often limits the formation of the finely phase-separated morphology required for efficient charge generation. Herein, the authors show that twisted acceptor-donor-acceptor (A-D-A) type compounds, containing an indacenodithiophene (IDT) electron-rich core and two naphthalenediimide (NDI) electron-poor units, leads to formation of mostly amorphous phases in the as-cast film, which can be readily converted into more crystalline domains by means of thermal annealing. This design strategy solves the aforementioned conundrum, leading to an optimal morphology in terms of reduced donor/acceptor domain-separation sizes (ca. 13 nm) and increased packing order. Solar cells based on these acceptors with a PBDB-T polymer donor show a power conversion efficiency over 10% and stable morphology, which results from the combined properties of desirable excited-state dynamics, high charge mobility, and optimal aggregation/crystallization characteristics. These results demonstrate that the twisted A-D-A motif featuring thermally-induced crystallization behavior is indeed a promising alternative design approach toward more morphologically robust materials for efficient organic photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2022

Enabling Production of Algal Biofuels by Techno-Economic Optimization of Co-Product Suites

Recent techno-economic analysis (TEA) has underscored that for algal biofuels to be cost competitive with petroleum fuels, co-products are necessary to offset the cost of fuel production. The co-product suite must scale with fuel production while also maximizing value from the non-fuel precursor components. The co-product suite also depends on algal biomass composition, which is highly dynamic and depends on environmental conditions during cultivation. Intentional shifts in composition during cultivation are often associated with reduced biomass productivity, which can increase feedstock production costs for the algae-based biorefinery. The optimal algae-based biorefinery configuration is thus a function of many factors. We have found that comprehensive TEA, which requires the construction of process models with detailed mass and energy balances, along with a complete accounting of capital and operating expenditures for a commercial-scale production facility, provides invaluable insight into the viability of a proposed biorefinery configuration. This insight is reflected in improved viability for one biorefining approach that we have developed over the last 10 years, namely, the Combined Algal Processing (CAP) approach. This approach fractionates algal biomass into carbohydrate-, lipid-, and protein-rich fractions, and tailors upgrading chemistry to the composition of each fraction. In particular, transitioning from valorization of only the lipids to a co-product suite from multiple components of high-carbohydrate algal biomass can reduce the minimum fuel selling price (MFSP) from more than $8/gallon of gasoline equivalent (GGE) to $2.50/GGE. This paper summarizes that progress and discusses several surprising implications in this optimization approach.

09 BIOMASS FUELS↗
DOE OSTI2021

Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors

This study presents magnetic resonance velocimetry (MRV) Reynolds Stress measurements in a periodic hill channel with a hill Reynolds number of Re = 29,500. The velocity encoding scheme is based on the ICOSA6 method with six icosahedral encoding directions and multiple encoding values are measured to increase the dynamic range. The full Reynolds stress tensor is obtained from a voxel-wise three-dimensional Gaussian fit using the magnitude data of all acquisitions. The MRV results are compared to a wall-resolved large eddy simulation and laser Doppler velocimetry measurements conducted in the same channel. It is shown that the MRV Reynolds stress data have excellent precision and agree qualitatively with the reference data. However, there are apparent systematic deviations. One of the most prominent error contributions is the signal attenuation caused by higher orders of motion, which leads to an overestimation of the turbulence level. Another fundamental error is identified in the assumption that the turbulence is Gaussian distributed. With the presented reconstruction technique, the MRV data are fitted to a statistical model, and depending on the examined flow setup, the Gaussian model can lead to considerable errors. Possible ways of how to reduce all identified errors are presented. In summary, this technique enables Reynolds stress tensor measurements in complex internal flows with high dynamic range and excellent precision. However, several issues need to be resolved to make the turbulence quantification more accurate.

42 ENGINEERING↗
DOE OSTI2023

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. In conclusion, the potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

42 ENGINEERING↗