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DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2025

Reducing module soiling with scalable and robust photocatalytic coatings

The air-glass interface at the front of a photovoltaic (PV) module reflects approximately 4% of incident light, decreasing the potential power output of the module by the same amount. Today’s modules reduce this loss by adding a low-refractive-index (1.25-1.30) SiO2 coating to the sunward side of the module glass; this antireflection coating recovers approximately 3% of the 4% light that would otherwise be lost. While such antireflection coatings work very well on clean, new modules, they do not inhibit soiling—the accumulation of soilants such as dust, pollen, soot, or other foreign material—on the module glass, and soilants reflect and scatter incident light. An improved coating would serve provide not only an antireflection effect, but also an anti-soiling effect. The goal of this project was to develop such a coating and provide a path for it to be manufactured in the U.S. The project successfully designed and fabricated coatings that provided >3% transmittance gain compared to bare glass (matching the performance of commercial antireflection coatings) and displayed anti-soiling behavior in standard laboratory soiling effects. This was achieved by using a Swift Coat proprietary coating deposition technique, aerosol impact-driven assembly (AIDA), to control the porosity and thus refractive index of coatings of photocatalytic materials—such as TiO2—that would otherwise increase (instead of decrease) reflection. These combined antireflection/anti-soiling coatings passed PV industry standard module reliability tests as well as coating-specific abrasion tests, showing that they have the durability needed for decades in the field. Swift Coat scaled the AIDA hardware and deposition process to make mini-modules that were monitored for nearly two years during field tests administered by a third party, as well as demonstrated scaling to the widths of full-sized modules. The fielded mini-modules outperformed reference modules (with commercial antireflection coatings) in two locations, providing a 1% absolute average performance boost and larger increases during periods of heavier soiling. Swift Coat’s cost analysis indicated a coating manufacturing cost below the sales price of today’s antireflection coatings. More than five module manufacturers sampled and assessed the coatings, and three provided letters of support. The coating developed in this project increases the energy output of PV modules, thereby decreasing the cost per kilowatt-hour of solar energy generated. Cheaper solar electricity benefits the public by accelerating the transition to a stable, affordable, carbon-free energy economy. In addition, for select applications in which PV modules are highly visible—such as on residential rooftops—the coating provides an aesthetic benefit because it stays cleaner than today’s modules. Finally, Swift Coat and its prospective customers are U.S. companies, and successful commercialization of this technology will provide U.S. jobs and a secure solar supply chain.

14 SOLAR ENERGY↗

DOE OSTI2023

Ground Motion Models (GMMs) Improvements Using Earthquake Simulations on High Performance Computers

A computationally efficient simulation platform was developed that can provide representative synthetic ground motions from crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using 3D modeling and high-performance computing. The main objective was to use synthetic ground motion to provide constrains to refinements of exiting ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-5Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. As part of a strategy for selecting a reginal velocity model and validation of developed rupture modeling technique, ground motions from the moment magnitude Mw5.0 November 7, 2016, Cushing Oklahoma, and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes were simulated. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model. Saikia’s model demonstrated better performance in modelling high frequency regional wave propagation for CEUS region. The proposed 3D model includes lateral variations added to the 1D background model using the stochastic scheme of Pitarka and Mellors. Comparisons of the simulations with recordings of both earthquakes demonstrated the reliability of our deterministic simulation approach while emphasizing the importance of including small-scale variability in the regional velocity model needed to reproduce the observed high-frequency wave scattering effects. As part of validation analysis, comparisons with different GMMs for a Mw6.5 earthquake in the CESUS region resulted in a very good match between the simulated and empirical ground motion models. Initial investigations of within-event and between-event ground motion variabilities for Mw6.5 scenario earthquakes on a strike-slip fault, suggest that they are strongly related to spatial slip and slip rate variations, average rupture velocity, rupture area and rupture initiation location. For certain scenarios we found that the ground motion variability observed at near-fault distances (< 5 km) also persists at longer distances. Regardless of the rupture scenario, the simulated ground motion tends to fully saturate at short distances and for all periods. The near-fault saturation has to do with the attenuation of waves propagating along the fault and local rupture radiation pattern that also contribute to stronger ground motion variation at such distances. Analysis of effects of rupture initiation location suggest that the peak ground motion (PGV) and spectral acceleration (SA) can be quite variable due to rupture directivity effects. Such effects are stronger at periods longer than 1s. The effect of the 1D velocity models and surface topography on simulated ground motion were investigated by comparing three component synthetic seismograms computed at selected sites. Effect of surface topography was considered using the ratio between spectral accelerations simulated for two 1D models with flat surface topography and realistic model with surface topography. Overall, the topography slightly amplifies (by ~30%) the ground motion amplitude in the frequency range 1-3Hz. The effect of topography is more visible in the surface and coda waves portion of the seismograms.

58 GEOSCIENCES↗

DOE OSTI2023

The Low-Yield Nuclear Monitoring (LYNM) Experimental Science Plan

The Low-Yield Nuclear Monitoring (LYNM) Program is a long-term NNSA research and development effort designed to improve the United States’ explosion monitoring capabilities, particularly with respect to low-yield and potentially evasive underground nuclear testing. The LYNM Program focuses on researching, discovering, and exploiting unique and useful signatures, from all available technologies and sensors (e.g., seismic, acoustic, electromagnetic, gases, and particulates (both stable and radioactive)). Four Department of Energy laboratories participate in LYNM and together are referred to as the ‘quad-lab’. The R&D program execution is performed under NNSA defined structures known as “ventures”. Four science ventures were established: 1) Explosion Source Functions; 2) Containment of Low-Yield Underground Tests; 3) Local Signatures; 4) Dynamic Monitoring Networks. To organize and execute the large field scale experiment a fifth venture was established: 5) Physics Experiment One (PE-1). As part of the LYNM Program, a series of experiments are planned at various scales and levels of venture involvement. These vary from those that involve a subset of labs and/or LYNM ventures (e.g., small experiments), to full quad-lab LYNM Program field-scale integrated experiments. Such experiments may involve chemical explosions with tracer materials or other means of simulating the expected signals from a nuclear explosion. The LYNM Program does not conduct actual nuclear explosions. Since 1992, the U.S. has observed a moratorium on underground nuclear explosions. This document is intended to provide the underlying scientific basis for the LYNM planned experimental work. Each specific LYNM experiment will develop a goals, objectives, and requirements (GOR) plan following the guidance in this document. The LYNM technical staff will define the numbers and types of experiments required over the course of the Program based on technical needs and within funding constraints. As with any scientific experiment series, the number and types of experiments may change based upon the experimental results obtained. An experiment that agrees with models/codes/software signature predictions may need fewer repetitions/variations, depending upon the level of statistical rigor desired, as compared to one in which the predictions and experimental data do not match. The large LYNM field-scale integrated experiments require the longest lead-time for planning, and these are discussed in more detail near the end of this document.

58 GEOSCIENCES↗

NASA NTRS2012

Late-glacial and Holocene Vegetation and Climate Variability, Including Major Droughts, in the Sky Lakes Region of Southeastern New York State

Sediment cores from Lakes Minnewaska and Mohonk in the Shawangunk Mountains of southeastern New York were analyzed for pollen, plantmacrofossils, macroscopic charcoal, organic carbon content, carbon isotopic composition, carbon/nitrogen ratio, and lithologic changes to determine the vegetation and landscape history of the greater Catskill Mountain region since deglaciation. Pollen stratigraphy generally matches the New England pollen zones identified by Deevey (1939) and Davis (1969), with boreal genera (Picea, Abies) present during the late Pleistocene yielding to a mixed Pinus, Quercus and Tsuga forest in the early Holocene. Lake Minnewaska sediments record the Younger Dryas and possibly the 8.2 cal kyr BP climatic events in pollen and sediment chemistry along with an ~1400 cal yr interval of wet conditions (increasing Tsuga and declining Quercus) centered about 6400 cal yr BP. BothMinnewaska andMohonk reveal a protracted drought interval in themiddle Holocene, ~5700-4100 cal yr BP, during which Pinus rigida colonized the watershed, lake levels fell, and frequent fires led to enhanced hillslope erosion. Together, the records show at least three wet-dry cycles throughout the Holocene and both similarities and differences to climate records in New England and central New York. Drought intervals raise concerns for water resources in the New York City metropolitan area and may reflect a combination of enhanced La Niña, negative phase NAO, and positive phase PNA climatic patterns and/or northward shifts of storm tracks.

Drought↗

DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗

DOE OSTI2022

Pauling’s rules for oxide-based minerals: A re-examination based on quantum mechanical constraints and modern applications of bond-valence theory to Earth materials

Since their introduction in 1929, Pauling’s five rules have been used by scientists from many disciplines to rationalize and predict stable arrangements of atoms and coordination polyhedra in crystalline solids; amorphous materials such as silicate glasses and melts; nanomaterials, poorly crystalline solids; aqueous cation and anion complexes; and sorption complexes at mineral-aqueous solution interfaces. The predictive power of these simple yet powerful rules was challenged recently by George et al. (2020), who performed a statistical analysis of the performance of Pauling’s five rules for about 5000 oxide crystal structures. They concluded that only 13% of the oxides satisfy the last four rules simultaneously and that the second rule has the most exceptions. They also found that Pauling’s first rule is satisfied for only 66% of the coordination environments tested and concluded that no simple rule linking ionic radius to coordination environment will be predictive due to the variable quality of univalent radii. We address these concerns and discuss quantum mechanical calculations that complement Pauling’s rules, particularly his first (radius sum and radius ratio rule) and second (electrostatic valence rule) rules. We also present a more realistic view of the bonded radii of atoms, derived by determining the local minimum in the electron density distribution measured along trajectories between bonded atoms known as bond paths, i.e., the bond critical point (rc). Electron density at the bond critical point is a quantum mechanical observable that correlates well with Pauling bond strength. Moreover, a metal atom in a polyhedron has as many bonded radii as it has bonded interactions, resulting in metal and O atoms that may not be spherical. O atoms, for example, are not spherical in many oxide-based crystal structures. Instead, the electron density of a bonded oxygen is often highly distorted or polarized, with its bonded radius decreasing systematically from ~1.38 Å when bonded to highly electropositive atoms like sodium to 0.64 Å when bonded to highly electronegative atoms like nitrogen. Bonded radii determined for metal atoms match the Shannon (1976) radii for more electropositive atoms, but the match decreases systematically as the electronegativities of the M atoms increase. As a result, significant departures from the radius ratio rule in the analysis by George et al. (2020) is not surprising. We offer a modified, more fundamental version of Pauling’s first rule and demonstrate that the second rule has a one-to-one connection between the electron density accumulated between the bonded atoms at the bond critical point and the Pauling bond strength of the bonded interaction. Pauling’s second rule implicitly assumes that bond strength is invariant with bond length for a given pair of bonded atoms. Many studies have since shown that this is not the case, and Brown and Shannon (1973) developed an equation and a set of parameters to describe the relation between bond length and bond strength, now redefined as bond valence to avoid confusion with Pauling bond-strength. Brown (1980) used the valence-sum rule, together with the path rule and the valence-matching principle, as the three axioms of bond-valence theory (BVT), a powerful method for understanding many otherwise elusive aspects of crystals and also their participation in dynamic processes. We show how a priori bond-valence calculations can predict unstrained bond-lengths and how bond-valence mapping can locate low-Z atoms in a crystal structure (e.g., Li) or examine possible diffusion pathways for atoms through crystal structures. In addition, we briefly discuss Pauling’s third, fourth, and fifth rules, the first two of which concern the sharing of polyhedron elements (edges and faces) and the common instability associated with structures in which a polyhedron shares an edge or face with another polyhedron and contains high-valence cations. The olivine [α-(MgxFe1–x)2SiO4] crystal structure is used to illustrate the distortions from hexagonal close-packing of O atoms caused by metal-metal repulsion across shared polyhedron edges. We conclude by discussing several applications of BVT to Earth materials, including the use of BVT to: (1) locate H+ ions in crystal structures, including the location of protons in the crystal structures of nominally anhydrous minerals in Earth’s mantle; (2) determine how strongly bonded (usually anionic) structural units interact with weakly bonded (usually cationic) interstitial complexes in complex uranyl-oxide and uranyl-oxysalt minerals using the valence-matching principle; (3) calculate Lewis acid strengths of cations and Lewis base strengths of anions; (4) determine how (H2O) groups can function as bond-valence transformers by dividing one bond into two bonds of half the bond valence; (5) help characterize products of sorption reactions of aqueous cations (e.g., Co2+ and Pb2+) and oxyanions [e.g., selenate (Se6+O4)2- and selenite (Se4+O3)2-] at mineral-aqueous solution interfaces and the important role of protons in these reactions; and (6) help characterize the local coordination environments of highly charged cations (e.g., Zr4+, Ti4+, U4+, U5+, and U6+) in silicate glasses and melts.

Geochemistry & Geophysics↗

DOE OSTI2025

Computational Fluid Dynamics Simulations of Glass Vitrification Refractory Coupon Tests

The Waste Treatment and Immobilization Plant (WTP) at the Hanford site is nearing the start of the Direct-Feed Low-Activity Waste (DFLAW) operations. DFLAW is destined to convert a pretreated low activity waste portion of the 56 million gallons of tank waste into a stable solid glass. In the subsequent decade completion of the high-level waste (HLW) facility is anticipated. Sustained operational missions of both LAW and HLW melter facilities are expected over multiple decades. In high-temperature glass melters, the refractory lining corrodes over time, which could potentially be an issue for longer term operations, this refractory corrosion is higher at the level of the glass-air interface due to surface tension driven flow. The glass viscosity, melt pool temperature, and glass chemical composition can impact the rate at which the refractory corrodes. This rate is important to quantify for the various waste glasses to be produced at the WTP since the integrity of the refractory should not be a limiting factor affecting the lifetime of the melter. To this end, a series of glasses representative of the first batches of waste glass produced by the WTP will be melted in small-scale crucibles with Monofrax® K-3 coupons inserted. The corrosion of the K-3 will be measured in the melt and at the meltline (or neckline). A model for the corrosion rate will be constructed and implemented into a previously developed framework for a computational fluid dynamics (CFD) model of the full-scale WTP. To assist with experimental design and validate the implementation of the model in the full-scale melter, CFD simulations of the small-scale crucible tests were performed. The bubbling that occurs in the small-scale crucible is initially validated here with a model that uses silicone oil at room temperature. The viscosity of the oil ranges from 1 to 100 Pa•s, which corresponds to operating glass pool temperatures near 1150 °C down to idling temperatures near 950 °C. The simulation results show good agreement with the bubble sizes that form during experiments. CFD modeling of the crucible setup was used to determine bubbling characteristics to match the range of near-wall velocities expected in the full-scale WTP. This study presents the initial CFD modeling results, corrosion testing plan, and some preliminary corrosion samples with an outline for the next steps for the development of the corrosion model.

Abboud, Alexander W. [Idaho National Lab]↗

DOE OSTI2022

Medium controlled aggregative growth as a key step in mesoporous silica nanoparticle formation

Near monodisperse mesoporous silica nanoparticles (MSN) represent a promising and rapidly developing type of mesoporous silica materials; however, the vast data on their synthesis remains unorganized and ill-understood. We systematically studied the formation of MSN under basic and neutral conditions using various temperatures, CTAB concentrations, hydrolyzing agents (triethanolamine, ammonia, phosphate buffers), and media with different colloidal stabilization properties (with ethanol as a cosolvent and monovalent salts). In the typical conditions for the preparation of stable MSN colloids, the particle size was controlled by colloidal stabilization by the medium (solvent type, ionic strength, and surfactant concentration) in agreement with the “aggregative growth” mechanism, rather than by solely the hydrolysis and condensation rates conventionally used for data interpretation in the classical nucleation theory. Medium properties (pH, ion types and concentration, polarity) also defined the efficiency of silica-surfactant cooperative self-assembly, which directly affected the porosity, mesopore size and pore wall thickness. Interestingly, this traditional silica-surfactant route showed a limited effect on the particle size, emphasizing the dominating role of colloidal stabilization in the studied reaction conditions. In situ pH measurements showed that every reaction medium has unique pH evolution profiles depending on the buffer capacity, hydrolysis and condensation rates. Reaction systems that fail to maintain the working pH can lead to non-porous products or undesired particle morphology and size distribution. The established particle formation mechanism allowed us to formulate comprehensive guidelines for preparing relatively concentrated colloids of near monodisperse (PDI 5–15%) mesoporous 30–700 nm silica spheres with variable porosity and mesopore size. Here, these findings will be particularly useful in designing new mesoporous silica-containing materials for biomedical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Large Primtive Asteroids: Thermal and Dynamical Context

Primitiveasteroids, most of which are located in the outer belt and Jupiter’s Trojan clouds, provide information related to the origin and evolution of the solar system and the conditions in which the solar nebula was formed. These asteroids are widely thought to be the origin of the least-altered car-bonaceous chondrite that allow us to put crucial con-straints on the current dynamical and thermal theories of the formation and evolution of the early solar sys-tem. The nature of surface composition of large and low-albedo asteroids, like (1) Ceres, (10) Hygiea and (52) Europa, is still under intense debate and different interpretations have beenput forth to explain the ab-sorption features in these objects (e.g.,[1, 2, 3,4,5,6]). Laboratory and spectroscopic experiments on me-teorites that represent all nine carbonaceous chondrite types also found no spectral matches for these large asteroids (e.g.,[7]). Previous studies of asteroid Ceres (the largest primitive asteroid in the solar system) have been conducted to constrain and estimate its surface composition (e.g., [2, 5]). Using linear mixing,[2]found hydroxide brucite, serpentines, and carbonates, to be consistent with Ceres’ ground-based spectra.[5]estimated the surface composition of Ceres and found evidence of widespread NH3-phyllosilicates across its surface using best-fit solutions to Dawn’s NIR spectra. The presence of NH3-phyllosilicates implies that mate-rial from the outer solar system was incorporated into large primitive asteroids, either during their formation at great heliocentric distance or by incorporation of material transported into the Main Belt region. Here we presentnew largeand primitive asteroidsthat share the same spectral similaritieswith the largest asteroid in the solar system, Ceres.We also present the context of these new observations in terms oftheirthermal and dynamicalevolution.Large Primitive Asteroids: Dynamical and Thermal Context:Orbital Distribution of LargePrimitiveAsteroids: Constraining the mineralogy and surface composition of large primitive asteroids will place crucial con-straints on existing dynamical and thermal theories of the formation of the early solar system. We identified several additional asteroids in the Europa-like group in addition to asteroids Europa, Euphrosyne, and Patien-tia, that wereidentified in [8]using NASA IRTFtele-scope (e.g.,Figure 1). These new asteroids along with the already-observed Ceres-and Europa-like group members are localized in the 2.8 < a < 3.4 AU region and characterized by larger sizes, showing an interest-ing orbital distribution (Figure 2). Ceres-likeand Eu-ropa-like groups, which include the largest asteroids in the solar system, show an interesting orbital distribu-tion. These groups are located in the 2.6 < a < 3.6 AU region that contains the snow-line. The snow-line’s location may have been driftedinwards due to the disk’s cooling and evolution[9,10]. Recent dynamical models[11,12]suggested that a substantial fraction of primitive asteroids originated between or beyond the giant planets (a > 5 AU), where water ice would have been stable, and then implanted in the outer Main Belt region because of the giant planets’ growth.Figure 1. Two asteroids, 94 Aurora and 423 Diotima, showing spectra similar to asteroids (1)Ceres and (52)Europa.Thermal modeling and evolution of primitive aster-oids:Primitive water-rich asteroids are thought to be originally composed of mixtures of anhydrous materi-als and water ice that waslater melted by heating sources such as the decay of 26Al, reacting with anhy-drous materials to form H2O/OH-rich minerals. Calcu-lations of the evolution of the temperature and struc-ture of icy planetesimals were performedusing a 1D finite differences thermal evolution model[13, 14] for 26Al-heated planetesimals. In particular, thermally activated compaction due to hot pressing of bodies with an initially unconsolidated porous structure is included. An ice-rich initial composition that leads to a material dominated by phyllosilicates upon aqueous alteration (with 25 vol% H2O and a rock fraction that contains 85 vol% phyllosilicates and 15 vol% olivine upon aque-ous alteration, similar to CIand CM chondrites) was assumed. Atypical initial porosity of 40%[15] is re-duced following the change of the strain rate that is calculated as Voigt approximation from the strain rates of components[16]. Material properties (thermal con-ductivity, density, heat capacity, etc.) correspond to the composition assumed and are adjusted with tempera-ture and porosity. Melting of the water ice as well as water-rock separation are included[14]. Both short-and long-lived radionuclides are consideredas heat sources. Figure 3 shows the maximum temperature calculated as a function of radius and accretion time. A variety of internal structures is obtained, ranging from primordial (no melting of water ice) over partially melted or partially differentiated (melting of water ice, hydration, formation of a rocky core and water ocean below an undifferentiated layer) to completely differ-entiated ones (rocky core, water mantle, Enceladus-like case). The heating and differentiation of planetesi-mals is determined by the availability of 26Al, i.e., by the accretion time t0 relative to the formation of the calcium-aluminum-rich inclusions (CAIs), such that maximum temperatures and structures vary stronglyfor t0< 6 Myr rel. to CAIs. However, for a later accre-tion only the size of the body determines its maximum tempera-ture and structure due to the nearly constant heating by long-lived radionuclides. Average densities of Ceres-and Europa-like group membersimply high-ly porous interiors and, consequently, relatively late accretion at t0> 3 Myr rel. to CAIs with a maximum temperature of < 600 K (Figure3)

D Takir↗

NASA NTRS1999

Chain Dynamics in Magnetorheological Suspensions

Magnetorheological (MR) suspensions are composed of colloidal particles which acquire dipole moments when subjected to an external magnetic field. At sufficient field strengths and concentrations, the dipolar particles rapidly aggregate to form long chains. Subsequent lateral cross-linking of the dipolar chains is responsible for a rapid liquid-to-solid-like rheological transition. The unique, magnetically-activated rheological properties of MR suspensions make them ideal for interfacing mechanical systems to electronic controls. Additionally, the ability to experimentally probe colloidal suspensions interacting through tunable anisotropic potentials is of fundamental interest. Our current experimental work has focused on understanding the fluctuations of dipolar chains. It has been proposed by Halsey and Toor (HT) that the strong Landau-Peierls thermal fluctuations of dipolar chains could be responsible for long-range attractions between chains. Such interactions will govern the long-time relaxation of MR suspensions. We have synthesized monodisperse neutrally buoyant MR suspensions by density matching stabilized ferrofluid emulsion droplets with D2O. This allows us to probe the dynamics of the dipolar chains using light scattering without gravitational, interfacial, and polydispersity effects to resolve the short-wavelength dynamics of the dipolar chains. We used diffusing wave spectroscopy to measure these dynamics. The particle displacements at short times that show an independence to the field strength, but at long times exhibit a constrained, sub-diffusive motion that slows as the dipole strength is increased. The experiments are in good qualitative agreement with Brownian dynamics simulations of dipolar chains. Although there have been several important and detailed studies of the structure and interactions in MR suspensions, there has not been conclusive evidence that supports or contradicts the HT model prediction that long-range interactions exist between fluctuating chains of dipolar particles. Resolving this issue would contribute greatly to the understanding of these interesting and important materials. We have begun to test the predictions of the HT model by both examining the dynamics of individual chains and by measuring the forces between dipolar chains directly to accurately and quantitatively assess the interactions that they experience. To do so, we employ optical trapping techniques and video-microscopy to manipulate and observe our samples on the microscopic level. With these techniques, it is possible to observe chains that are fluctuating freely in three-dimensions, independent of interfacial effects. More importantly, we are able to controllably observe the interactions of two chains at various separations to measure the force-distance profile. The techniques also allow us to study the mechanical properties of individual chains and chain clusters. Our work to this point has focused on reversibly-formed dipolar chains due to field induced dipoles where the combination of this chaining, the dipolar forces, and the hydrodynamic interactions that dictate the rheology of the suspensions. One can envision, however, many situations where optical, electronic, or rheological behavior may be optimized with magneto-responsive anisotropic particles. Chains of polarizable particles may have the best properties as they can coil and flex in the absence of a field and stiffen and orient when a field is applied. We have recently demonstrated a synthesis of stable, permanent paramagnetic chains by both covalently and physically linking paramagnetic colloidal particles. The method employed allows us to create monodisperse chains of controlled length. We observed the stability, field-alignment, and rigidity of this new class of materials. The chains may exhibit unique rheological properties in an applied magnetic field over isotropic suspensions of paramagnetic particles. They are also useful rheological models as bead-spring systems. These chains form the basis for our current experiments with optical traps.

Gast, A. P.↗

DOE OSTI2021

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

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