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DOE OSTI2020

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

We present a machine learning framework (GP-NODE) for Bayesian model discovery from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling and Gaussian Process priors over the observed system states. This allows us to exploit temporal correlations in the observed data, and efficiently infer posterior distributions over plausible models with quantified uncertainty. The use of the Finnish Horseshoe as a sparsity-promoting prior for free model parameters also enables the discovery of parsimonious representations for the latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed GP-NODE method including predator–prey systems, systems biology and a 50-dimensional human motion dynamical system. This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.

Science & Technology - Other Topics↗

DOE OSTI2020

Perturbation-Based Diagnosis of False Data Injection Attack Using Distributed Energy Resources

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This paper proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

All-Atom Molecular Dynamics Simulations of the Temperature Response of Densely Grafted Polyelectrolyte Brushes

Water acts a “good” solvent for most polyelectrolyte (PE) systems due to its high dielectric strength. Theoretical and experimental studies have highlighted some remarkably unconventional properties of strongly water-swollen PE brushes such as a reduction in brush height with increase in temperature. However, an understanding of the atomistic-scale response of the grafted PE chains as well as the local arrangement and organization of the brush-supported counterions and water molecules to changes in temperature is missing. Here, we conduct an all-atom molecular dynamics (MD) study to probe the influence of temperature on the “microstructure” of densely grafted, fully ionized polyacrylic acid (PAA) brushes. The atomistic insights obtained from our study shed light on prior experimental observations and elucidate the key role played by changes in hydrogen bonding network in dictating the hydrophilicity of the brushes. In addition to the temperature-mediated reduction in height of the PE brush layer, we study the influence of temperature on several properties of the brush-supported water molecules such as the number distribution and kinetics of water-water and water-PE hydrogen bonds (HBs), mass density, orientational tetrahedral order parameter, and the activation energy associated with the self-diffusion of water. The effect of temperature on the properties of the brush-trapped counterions is also quantified via changes in their solvation structure, counterion-water radial distribution function (RDF), and the translational mobility. Our findings unravel several hitherto unknown phenomena such as the enhanced propensity for attaining PE brush-induced “water-in-salt”-like scenarios at elevated temperatures, and an increase in the activation energy for the self-diffusion of water with increase in the degree of PE brush-induced nanoconfinement (from ‘bulk water’ to ‘sparsely grafted PE brushes’ to ‘densely grafted PE brushes’). Further, we anticipate that this work will be helpful in improving our predictive capabilities of the temperature response of water-swollen PE brushes, which would be instrumental for better design of several PE brush systems used in a multitude of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Dose Summation Strategies for External Beam Radiation Therapy and Brachytherapy in Gynecologic Malignancy: A Review from the NRG Oncology and NCTN Medical Physics Subcommittees

Definitive, nonsurgical management of gynecologic malignancies involves external beam radiation therapy (EBRT) and/or brachytherapy (BT). Summation of the cumulative dose is critical to assess the total biologic effective dose to targets and organs at risk. Cumulative dose calculation from EBRT and BT can be performed with or without image registration (IR) and biologic dose summation. Among these dose summation strategies, linear addition of dose-volume histogram (DVH) parameters without IR is the global standard for composite dose reporting. This approach stems from an era without image guidance and simple external beam and brachytherapy treatment approaches. With technological advances, EBRT and high-dose-rate BT have evolved to allow for volume-based treatment planning and delivery. Modern conformal therapeutic radiation involves volumetric or intensity modulated EBRT, capable of simultaneously treating multiple targets at different specified dose levels. Therefore, given the complexity of modern radiation treatment, the linear addition of DVH parameters from EBRT and high-dose-rate BT is challenging to represent the combined dose distribution. Deformable image registration (DIR) between EBRT and image guided brachytherapy (IGBT) data sets may provide a more nuanced calculation of multimodal dose accumulation. However, DIR is still nascent in this regard, and needs further development for accuracy and efficiency for clinical use. Biologic dose summation can combine physical dose maps from EBRT and each IGBT fraction, thereby generating a composite DVH from the biologic effective dose. However, accurate radiobiologic parameters are tissue-dependent and not well characterized. A combination of voxel-based DIR and biologic weighted dose maps may be the best approximation of dose accumulation but remains invalidated. The purpose of this report is to review dose summation strategies for EBRT and BT, including conventional equivalent dose in 2-Gy fractions dose summation without image registration, physical dose summation using 3-dimensional rigid IR and DIR, and biologic dose summation. We also provide general clinical workflows for IGBT with a focus on cervical cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Derivation of heterogeneous material distributions and their sensitivity to HM-coupled two-phase flow models exemplified with the LASGIT experiment

Abstract Advective gas transport in bentonite, a possible buffer material in repositories for radioactive materials, is difficult to simulate in numerical continuum models, partly due to the complicated microstructure of bentonite. To generate reliable models of repositories nevertheless, spatially distributed heterogeneous material properties can be used to allow localization of gas flow. In this study, a pore-size-dependent stochastic approach of the gas entry pressure is derived from Mercury Intrusion Porosimetry, which is used to replicate measurements from the LASGIT experiment. In addition, three benchmark tests are simulated to investigate the dependence of heterogeneous distributions of material properties on the mesh discretization, the temporal dependence, and the coupling between the processes influenced by the heterogeneous parameters. The numerical modeling results of the LASGIT experiment show that the onset of gas flow into the system and the subsequent increase in pressure and stress can be well reproduced using heterogeneous distributions. Compared to a model with homogeneous material properties, heterogeneous distributions may allow the generation of dilatancy-controlled microfractures—an important feature with regard to the advective gas flow in bentonites. However, it can be observed that the heterogeneous distributions in LASGIT are less significant, as technical gaps or differences in material types could have a greater impact.

Environmental Sciences & Ecology↗

DOE OSTI2022

Bidisperse supension balance model

The suspension balance model (SBM) for viscous Stokes flow has been well studied for the case of identical, monodisperse spherical particles in a channel, however, more work remains to be done to explore the bidisperse and polydisperse SBM. Here, we present a simple extension of the SBM that allows for modeling of suspensions of particles of bidisperse size. The comparison with avaiable experiments and direct simulations is found to be good. Additionally, we present a range of simulations to justify the assumptions made in the polydisperse suspension balance model for non-Brownian, neutrally buoyant particles that vary in size with a bidisperse distribution. The aim is to study the effects of moderate size variation on the rheology of the suspension and the distribution of the particles across the flow, especially in the near-wall regions. It is shown that, at the size ratios considered, bidispersity in the particle size does not significantly affect rheological parameters including the particle volume fraction and relative viscosity in the bulk of a Couette flow. The particle phase stresses are distributed between small and large particles in proportion to the relative volume fractions of each species. In bidisperse suspensions, the large particles in the wall layer develop a spatial structure and form chains in the streamwise direction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Demonstrating Advanced Sensors for In-Situ Monitoring Towards Qualification of Nuclear Relevant Components

The U.S. Department of Energy’s Office of Nuclear Energy Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing qualification of laser powder bed fusion (LPBF) components for nuclear applications. A major focus of this effort is the use of in situ process monitoring and machine learning–based tools to establish real-time quality assurance. The primary objective of this report is to identify and evaluate the most relevant in situ sensor systems for LPBF, and to document the deployment of these systems across platforms critical to the AMMT program. This work demonstrates how in situ monitoring can detect process anomalies, track geometry-dependent flaws, and identify limiting combinations of processing parameters—particularly those related to energy density and complex geometries (e.g., overhanging structures). To support this goal, a diverse suite of sensor modalities was evaluated across LPBF platforms, including visible and near-infrared (NIR) imaging, fringe projection profilometry, long-wavelength infrared (LWIR) thermography, and high-speed photodiode/pyrometry systems. These sensor streams were integrated with Peregrine, a machine-agnostic software platform that, among other capabilities, can generate real-time process anomaly classification. This report documents sensor deployments on multiple AMMT flagship platforms, including the Concept Laser M2 and Renishaw AM400/AM250 systems. Calibration builds with complex, flaw-prone geometries such as unsupported overhangs, stepped features, and thin walls, were used to evaluate how well Peregrine and its associated sensors could detect process anomalies and other instabilities under varied energy densities. It will be shown how Peregrine reliably identifies common process anomalies such as recoater streaking, superelevation, etc., and can be used in post-build analysis for anomaly spatial distributions throughout the build height to better understand the impact of geometry and processing parameter choice on the build. This work demonstrates measurable progress toward the vision that components can be born-qualified by establishing a real-time monitoring framework, identifying limiting process conditions, and laying the foundation for sensor fusion–enabled prediction pipelines that are scalable across platforms and applicable to nuclear-relevant components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

A new technique to retrieve aerosol vertical profiles using micropulse lidar and ground-based aerosol measurements

Accurately characterizing the vertical distribution of aerosols and their cloud-forming properties is crucial for understanding aerosol-cloud interactions and their impact on climate. This study presents a novel technique for retrieving vertical profiles of aerosols, cloud condensation nuclei (CCN), and ice nucleating particles (INP) by combining micropulse lidar, radiosonde, and ground-based aerosol measurements. Herein, the technique is applied to data collected by our team at Texas A&M University during the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) campaign. Ground-based aerosol size distribution and CCN counter data are used to estimate the value of the aerosol hygroscopicity parameter, κ. The derived κ, together with Mie scattering theory and the relative humidity profile from the radiosonde, is used to estimate aerosol size growth and the associated increase in backscatter at each altitude. We then correct the lidar backscatter to dry conditions to produce the dry aerosol backscatter coefficient profile. The dry aerosol backscatter coefficient profile is linearly scaled to collocated surface measurements of aerosols, CCN, and INP to produce corresponding vertical profiles. Combining lidar backscatter profiles with aerosol and cloud nucleation measurements leads to a more realistic representation of vertical distributions of aerosol properties. The method could be readily applied to lidar measurements in future field campaigns.

Chen, Bo [Texas A & M Univ., College Station, TX (↗

DOE OSTI2021

Statistical tools for a better optical model

Background: Modern statistical tools provide the ability to compare the information content of observables and provide a path to explore which experiments would be most useful to give insight into and constrain theoretical models. Purpose: Here we study three such tools in the context of nuclear reactions with the goal of constraining the optical potential. Method: The three statistical tools examined are (i) the principal component analysis, (ii) the sensitivity analysis based on derivatives, and (iii) the Bayesian evidence. We first apply these tools to a toy-model case, comparing the form of the imaginary part of the optical potential. Then we consider two different reaction observables, elastic angular distributions and polarization data for reactions on 48 Ca and 208 Pb at two different beam energies. Results: For the toy-model case, we find significant discrimination power in the sensitivities and the Bayesian evidence, showing clearly that the volume imaginary term is more useful to describe scattering at higher energies. When comparing between elastic cross sections and polarization data using realistic optical models, sensitivity studies indicate that both observables are roughly equally sensitive but the variability of the optical model parameters is strongly angle dependent. The Bayesian evidence shows some variability between the two observables, but the Bayes factor obtained is not sufficient to discriminate between angular distributions and polarization. Conclusions: From the cases considered, we conclude that, in general, elastic scattering angular distributions have similar impact in constraining the optical potential parameters compared with the polarization data. The angular ranges for the optimum experimental constraints can vary significantly with the observable considered.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Radiative natural supersymmetry emergent from the string landscape

In string theory with flux compactifications, anthropic selection for structure formation from a discretuum of vacuum energy values provides at present our only understanding of the tiny yet positive value of the cosmological constant. We apply similar reasoning to a toy model of the multiverse restricted to vacua with the MSSM as the low energy effective theory. Here, one expects a statistical selection favoring large soft SUSY breaking terms leading to a derived value of the weak scale in each pocket universe (with appropriate electroweak symmetry breaking) which differs from the weak scale as measured in our universe. In contrast, the SUSY preserving μ parameter is selected uniformly on a log scale as is consistent with the distribution of SM fermion masses: this favors smaller values of μ. An anthropic selection of the weak scale to within a factor of a few of our measured value — in order to produce complex nuclei as we know them (atomic principle) — provides statistical predictions for Higgs and sparticle masses in accord with LHC measurements. The statistical selection then more often leads to (radiatively-driven) natural SUSY models over the Standard Model or finely-tuned SUSY models such as mSUGRA/CMSSM, split, mini-split, spread, high scale or PeV SUSY. The predicted Higgs and superparticle spectra might be testable at HL-LHC or ILC via higgsino pair production but is certainly testable at higher energy hadron colliders with $ \sqrt{s}$ ~ 30–100 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Experimental approach for development of a powder spreading metric in additive manufacturing

The powder spreading is a vital step of powder-based additive manufacturing (AM) processes. The quality of spread powder can considerably influence the properties of fabricated parts. Poorly packed powder beds with high surface roughness result in printed part layers with large porosity and low dimensional accuracy, leading to poor mechanical properties. Therefore, the powder spreadability and its dependence on process parameters and powder characteristics should be quantified to improve the efficiency of powder-based AM methods. This study proposes a novel dimensionless powder spreadability metric that can be commonly used in different powder-based AM processes. The quality of spread powder in terms of powder bed density and surface roughness was evaluated by adjusting the process parameters including recoating velocity and layer thickness, and powder characteristics including particle size distribution. In addition, the dynamic repose angle was proposed and examined as another powder spreadability metric. Furthermore, the results showed that these two proposed metrics were strongly correlated and lower recoating velocity and larger layer thickness led to higher spreadability and lower dynamic repose angle.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fatigue behavior of low-cost, non-spherical Ti-6Al-4V powder processed via laser powder bed fusion

Hydride-dehydride Ti-6Al-4V powder with particle size distribution of 75–175 μm were used to manufacture fatigue samples. Optimized processing parameters were used to maintain a relative density of >99.5% in the samples. Fatigue tests were carried out in R = σ min /σ max = -1 condition under stresses ranging from 150 to 500 MPa and the results were compared to samples that were additively manufactured using spherical powders. The fatigue lives were similar between the two types of powder; i.e., powder morphology has no effect on fatigue performance. However, the high surface roughness and martensitic microstructure resulting from the laser powder bed fusion process result in poor performance compared to samples machined from conventionally produced materials. In conclusion, crack initiation occurred consistently from surface defects because of high surface roughness of the additively manufactured parts.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Towards High-Speed friction stir welding of 25 mm Thick AA2139-T8: tool innovation and process development

Joining thick plates (≥ 12 mm) of aluminum (Al) alloys is challenging due to high tool forces, uneven material flow, and non-uniform heat distribution through the material’s thickness. Recent advancements in tool design and welding parameters for friction stir welding (FSW) in thick plates butt joining have encountered a developmental plateau, highlighting the need for innovative approaches to overcome existing limitations. Here, this study focuses on the systematic development of single-pass and double-pass FSW processes for high-strength aluminum alloy AA2139-T8, to improve joint efficiency and enable high-speed welding capabilities. Experimental evidence is presented for a novel tool design with opposing pin threads, enabling high-speed (178 mm/min) single-pass friction stir butt welding of 25 mm thick AA2139-T8. A series of FSW trials was conducted both in air and with a trailing water spray, using steel backing plates (BPs) to investigate the impact of quenching and cooling rates on process response and joint performance. A joint efficiency of 83% was attained using the novel tool features and effectively controlling process forces and thermal boundary conditions.

AA2139-T8↗

DOE OSTI2022

Intermetallic particle heterogeneity controls shear localization in high-strength nanostructured Al alloys

The mechanical behavior of two nanocrystalline Al alloys, Al-Mg-Y and Al-Fe-Y, is investigated with in-situ micropillar compression testing. Both alloys were strengthened by a hierarchical microstructure including grain boundary segregation, nanometer-thick amorphous complexions, carbide nanorod precipitates with sizes of a few nanometers, and submicron-scale intermetallic particles. The maximum yield strength of the Al-Mg-Y system is measured to be 950 MPa, exceeding that of the Al-Fe-Y system (680 MPa), primarily due to a combination of more carbide nanorods and more amorphous complexions. Both alloys exhibited yield strengths much higher than those of commercial Al alloys, and therefore have great potential for structural applications. However, some micropillar specimens were observed to plastically soften through shear banding. Post-mortem investigation revealed that intermetallic-free deformation pathways of a few micrometers in length were responsible for this failure. Further characterization showed significant grain growth within the shear band. The coarsened grains maintained the same orientation with each other, pointing to grain boundary mechanisms for plastic flow, specifically grain rotation and/or grain boundary migration. Here, the presence of intermetallic particles makes it difficult for both matrix and intermetallic grains to rotate into the same orientation due to the different lattice parameters and slip systems. Therefore, we are able to conclude that a uniform distribution of intermetallic particles with an average spacing less than the percolation length of shear localization can effectively prevent the maturation of shear bands, offering a design strategy for high-strength nanocrystalline Al alloys with both high strength and stable plastic flow.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A review of technical and regulatory limits for hydrogen blending in natural gas pipelines

We report there is rising interest globally in the use of hydrogen for the provision of electricity or heat to industry, transport, and other applications in low-carbon energy systems. While there is attention to build out dedicated hydrogen infrastructure in the long-term, blending hydrogen into the existing natural gas pipeline network is also thought to be a promising strategy for incorporating hydrogen in the near-term. However, hydrogen injection into the existing gas grid poses additional challenges and considerations related to the ability of current gas infrastructure to operate with blended hydrogen levels. This review paper focuses on analyzing the current understanding of how much hydrogen can be integrated into the gas grid from an operational perspective and identifies areas where more research is needed. The review discusses the technical limits in hydrogen blending for both transmission and distribution networks; facilities in both systems are analyzed with respect to critical operational parameters, such as decrease in energy density, increased flow speed and pressure losses. Safety related challenges such as, embrittlement, leakage and combustion are also discussed. The review also summarizes current regulatory limits to hydrogen blending in different countries, including ongoing or proposed pilot hydrogen blending projects.

08 HYDROGEN↗

DOE OSTI2021

Optimizing $\mathrm{ACRT}$ to reduce inclusion formation during the $\mathrm{VGF}$ growth of cadmium zinc telluride: II. Application to experiments

Here two different accelerated crucible rotation technique (ACRT) rotation schedules are assessed, via theoretical computations and growth experiments, according to their ability to reduce tellurium-rich inclusions during the vertical gradient freeze (VGF) of cadmium zinc telluride (CZT). A vigorous rotation schedule based on classical ACRT guidelines produces a well-mixed melt that is expected to reduce tellurium inclusions compared to growth without crucible rotation. In contrast, a new ACRT schedule, derived from model-based optimization for interface stability, employs slower rotation with longer periods and is predicted to further reduce inclusions. Growth experiments corroborate these expectations. Namely, both ACRT schedules result in crystals with inclusion size and volume significantly decreased from levels found in material grown with no rotation, and material grown using the computationally optimized ACRT parameters exhibits a median inclusion size that is smaller and with a sharper distribution than in material grown via classical ACRT.

36 MATERIALS SCIENCE↗