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DOE OSTI2021

Multi-Dimensional Scaling on Groups

Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental ``frequencies'' (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.

Dimensionality reduction, Representation theory, D↗

DOE OSTI2023

Combined Dimensional and Topology Optimization of Synchronous Machine Rotors Using a Material Density Interpolation Method

This article presents a magneto-structural combined dimensional and topology optimization technique for interior permanent magnet synchronous machine (IPMSM) and wound field synchronous machine (WFSM) rotors. Dimensional changes to the permanent magnet (PM) and rotor winding location or size are accomplished by interpolating or projecting a smoothed Heaviside rectangular function representing the presence of PM material onto the IPMSM rotor design domain mesh, whereas the presence of copper material for WFSM rotor is interpolated in the same manner. A density based Solid Isotropic Material with Penalization (SIMP) topology optimization approach is then used to vary the presence of electrical steel in mesh elements to form flux barriers around the PM. The proposed method enforces a defined shape for the PM without requiring the mesh in the design domain to be deformed. Four examples are presented to demonstrate the technique: Two flat bar IPMSM, one V-shaped IPMSM, and a WFSM. Here, a comparative study is performed on one of the flat bar IPMSMs using Metamodel of Optimal Prognosis (MOP) based method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Spontaneous formation of robust two-dimensional perovskite phases

The two-dimensional on three-dimensional (2D/3D) perovskite bilayer heterostructure can improve the stability and performance of perovskite solar cells. We show that the 2D/3D perovskite stack in a device evolves dynamically during its end-of-life decomposition. Initially phase-pure 2D interlayers can evolve differently, resulting in different device stabilities. We show that a robust 2D interlayer can be formed using mixed solvents to regulate its crystallinity and phase purity. The resulting 2D/3D devices achieved 25.9% efficiency and had good durability, retaining 91% of their initial performance after 1074 hours at 85°C using maximum power point tracking.

14 SOLAR ENERGY↗

DOE OSTI2023

Dynamics of Three-Dimensional Shock-Wave/Boundary-Layer Interactions

Advances in measuring and understanding separated, nominally two-dimensional (2D) shock-wave/turbulent-boundary-layer interactions (STBLI) have triggered recent campaigns focused on three-dimensional (3D) STBLI, which display far greater configuration diversity. Nonetheless, unifying properties emerge for semi-infinite interactions, taking the form of conical asymptotic behavior where shock-generator specifics become insignificant. The contrast between 2D and 3D separation is substantial; the skewed vortical structure of 3D STBLI reflects the essentially 2D influence of the boundary layer on the 3D character of the swept shock. As with 2D STBLI, conical interactions engender prominent spectral content below that of the turbulent boundary layer. However, the uniform separation length scale, which is crucial to normalizing the lowest-frequency dynamics in 2D STBLI, is absent. Comparatively, the spectra of 3D STBLI are more representative of the mid-frequency, convective, shear-layer dynamics in 2D, while phenomena associated with 2D separation-shock breathing are muted. Asymptotic behavior breaks down in many regions important to 3D-STBLI dynamics, occurring in a configuration-dependent manner. Aspects of inceptive regions near shock generators and symmetry planes are reviewed. Focused efforts toward 3D modal and nonmodalanalyses, moving-shock/boundary-layer interactions, fluid/structure interactions, and flow control are suggested as directions for future work.

42 ENGINEERING↗

DOE OSTI2025

Development of a two-zone zero-dimensional mixing model for actively fueled prechamber engines

In this numerical work, a zero-dimensional, two-zone mixing model was developed and validated to capture the mixture preparation process of an actively fueled prechamber engine. The model was developed for ethanol-gasoline fuel blends but is applicable to various fuels of interest relevant to prechamber applications. The model was exercised to demonstrate the effects of boundary conditions, fuel composition, and residual content on the mixture stoichiometry predictions. The 0D model showed reasonable agreement in prechamber equivalence ratio predictions with higher fidelity computational fluid dynamics modeling, but the accuracy of model was constrained to the governing assumptions of the injection process. The modeling assumptions surrounding the non-dimensional injection of mass into the prechamber were scrutinized to identify key areas of model improvement. The results indicate heat transfer effects play a crucial role in the evaporation of the fuel within the confined auxiliary chamber, especially when considering fuels with high latent heat of vaporization. Additionally, the presence of fuel films that collect on the prechamber surfaces complicated the modeled evaporative cooling loses as the modeling suggests the films evaporate primarily by means of conduction from the boundary. Furthermore, the developed model serves as a simplified framework to generate calibration maps of prechamber fueling or run real time as an open loop control strategy such that desired prechamber mixture proportions are effectively targeted.

Zero-dimensional↗

DOE OSTI2021

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗

DOE OSTI2024

Recent progress in realizing novel one-dimensional polymorphs via nanotube encapsulation

Encapsulation of various materials inside nanotubes has emerged as an effective method in nanotechnology that facilitates the formation of novel one-dimensional (1D) structures and enhances their functionality. Because of the effects of geometrical confinement and electronic interactions with host nanotubes, encapsulated materials often exhibit low-dimensional polymorphic structures that differ from their bulk forms. These polymorphs exhibit unique properties, including altered electrical, optical, and magnetic behaviors, making them promising candidates for applications in electronics, energy storage, spintronics, and quantum devices. This review explores recent advancements in the encapsulation of a wide range of materials such as organic molecules, elemental substances, metal halides, metal chalcogenides, and other complex compounds. In particular, we focus on novel polymorphs formed through the geometrical confinement effect within the nanotubes. The atomic structure, other key properties, and potential applications of these encapsulated materials are discussed, highlighting the impact of nanotube encapsulation on their functionalities.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Investigations of Dimensionally-Constrained Fermions

This project dealt with the investigation of bulk condensed matter materials that contain low-dimensional substructures. The main focus was materials with one-dimensional conducting or magnetic chains. This report describes the activities undertaken during the course of this project.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Advanced Multi-Dimensional Capacitance Sensors Based Subsea Multiphase Mass Flow Meter to Measure and Monitor Offshore Enhanced Oil

This innovation is based on advanced multi-dimensional extensions of ECVT sensors that involve ECVT, Displacement Current Phase Tomography (DCPT), Maxwell-Wagner-Sillars polarization effect (MWS), and velocimetry which exploit the variation of electric properties between the oil, water, and gas coming out of the well. Capacitance sensors are embedded on the inside of a pipe spool and placed in-line with the subsea oil line piping on the extraction end of the well. The difference in dielectric, dielectric loss, surface polarization, and velocity of each phase are used in the multi-dimensional algorithm to measure the volume fraction, distribution, velocity, mass flow rate, and flow regime of the mixture moving through the pipe.

02 PETROLEUM↗

DOE OSTI2022

Ru Single Atoms on One-Dimensional CF@g-C 3 N 4 Hierarchy as Highly Stable Catalysts for Aqueous Levulinic Acid Hydrogenation

Herein, we report a stable catalyst with Ru single atoms anchored on a one-dimensional carbon fiber@graphitic carbon nitride hierarchy, by assembling wet wipes composed of fiber-derived carbon fiber (CF), melamine-derived graphitic carbon nitride (g-C3N4) and RuCl3 before NaBH4 reduction. The atomically dispersed Ru species (3.0 wt%) are tightly attached via N-coordination provided by exterior g-C3N4 nanosheets, and further stabilized by the interior mesoporous CF. The obtained CF@g-C3N4–Ru SAs catalyst can be cycled six times without notable leaching of Ru or loss of GVL yield in the acidic media. This catalyst is more stable than Ru nanoparticles supported on CF@g-C3N4, as well as Ru single atoms anchored on CF and g-C3N4, and proves to be one of the most efficient metal catalysts for aqueous LA hydrogenation to γ-valerolactone (GVL). The isolated Ru atoms by strong N-coordination, and their enhanced electron/mass transfer afforded by the one-dimensional hierarchy, can be responsible for the excellent durability of CF@g-C3N4–Ru SAs under harsh reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Chimera Spectrum Diagnostics for Peptides Using Two-Dimensional Partial Covariance Mass Spectrometry

The rate of successful identification of peptide sequences by tandem mass spectrometry (MS/MS) is adversely affected by the common occurrence of co-isolation and co-fragmentation of two or more isobaric or isomeric parent ions. This results in so-called `chimera spectra’, which feature peaks of the fragment ions from more than a single precursor ion. The totality of the fragment ion peaks in chimera spectra cannot be assigned to a single peptide sequence, which contradicts a fundamental assumption of the standard automated MS/MS spectra analysis tools, such as protein database search engines. This calls for a diagnostic method able to identify chimera spectra to single out the cases where this assumption is not valid. Here, we demonstrate that, within the recently developed two-dimensional partial covariance mass spectrometry (2D-PC-MS), it is possible to reliably identify chimera spectra directly from the two-dimensional fragment ion spectrum, irrespective of whether the co-isolated peptide ions are isobaric up to a finite mass accuracy or isomeric. We introduce ‘3-57 chimera tag’ technique for chimera spectrum diagnostics based on 2D-PC-MS and perform numerical simulations to examine its efficiency. We experimentally demonstrate the detection of a mixture of two isomeric parent ions, even under conditions when one isomeric peptide is at one five-hundredth of the molar concentration of the second isomer.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗

DOE OSTI2022

Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression

Abstract The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra, which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep-learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot-Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parameterize the most general family of degeneracies among the input atmospheric parameters that affect the characterization of an exoplanet atmosphere through transit spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Two‐Dimensional Superstructures of Silica Cages

Abstract Despite extensive studies on mesoporous silica since the early 1990s, the synthesis of two‐dimensional (2D) silica nanostructures remains challenging. Here, mesoporous silica is synthesized at an interface between two immiscible solvents under conditions leading to the formation of 2D superstructures of silica cages, the thinnest mesoporous silica films synthesized to date. Orientational correlations between cage units increase with increasing layer number controlled via pH, while swelling with oil and mixed surfactants increase micelle size dispersity, leading to complex clathrate type structures in multilayer superstructures. The results suggest that a three‐dimensional (3D) crystallographic registry within cage‐like superstructures emerges as a result of the concerted 3D co‐assembly of the organic and inorganic components. Mesoporous 2D superstructures can be fabricated over macroscopic film dimensions and stacked on top of each other. The realization of previously inaccessible mesoporous silica heterostructures with separation or catalytic properties unachievable via conventional bulk syntheses is envisioned.

Aubert, Tangi↗

DOE OSTI2020

Visualizing Tailored Spin Phenomena in a Reduced‐Dimensional Topological Superlattice

Abstract Emergent topological insulators (TIs) and their design are in high demand for manipulating and transmitting spin information toward ultralow‐power‐consumption spintronic applications. Here, distinct topological states with tailored spin properties can be achieved in a single reduced‐dimensional TI‐superlattice, (Bi 2 /Bi 2 Se 3 )‐(Bi 2 /Bi 2 Se 3 ) N or (□/Bi 2 Se 3 )‐(Bi 2 /Bi 2 Se 3 ) N ( N is the repeating unit, □ represents an empty layer) by controlling the termination via molecular beam epitaxy. The Bi 2 ‐terminated superlattice exhibits a single Dirac cone with a spin momentum splitting ≈0.5 Å −1 , producing a pronounced inverse Edelstein effect with a coherence length up to 1.26 nm. In contrast, the Bi 2 Se 3 ‐terminated superlattice is identified as a dual TI protected by coexisting time reversal and mirror symmetries, showing an unexpectedly long spin lifetime up to 1 ns. The work elucidates the key role of dimensionality and dual topological phases in selecting desired spin properties, suggesting a promise route for engineering topological superlattices for high‐performance TI‐spintronic devices.

Sun, Rui↗

DOE OSTI2021

Two‐Dimensional (2D) or Quasi‐2D Superstructures from DNA‐Coated Colloidal Particles

Abstract This contribution describes the synthesis of colloidal di‐patch particles functionalized with DNA on the patches and their assembly into colloidal superstructures via cooperative depletion and DNA‐mediated interactions. The assembly into flower‐like Kagome, brick‐wall like monolayer, orthogonal packed single or double layers, wrinkled monolayer, and colloidal honeycomb superstructures can be controlled by tuning the particles’ patch sizes and assembly conditions. Based on these experimental results, we generate an empirical phase diagram. The principles revealed by the phase diagram provide guidance in the design of two‐dimensional (2D) materials with desired superstructures. Our strategy might be translatable to the assembly of three‐dimensional (3D) colloidal structures.

Liu, Mingzhu↗

DOE OSTI2023

Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications

Abstract Metal‐organic frameworks (MOFs) are a class of porous materials with high surface areas, which are acquiring rapid attention on an exponential basis. A significant characteristic of MOFs is their ability to act as adsorbents to selectively separate component mixtures of similar size, thereby addressing the technological need for an alternative approach to conventional distillation methods. Recently, MOFs comprising a 3‐Dimensional (3D) linker have shown outstanding capabilities for difficult separations compared to the parent 2‐Dimensional (2D) analogue. 3D‐linkers with a polycyclic core are underrepresented in the MOF database due to the widespread preferred use of 2D‐linkers and the misconceived high‐cost of 3D linkers. We summarize the recent research of 3D‐linker MOFs and highlight their beneficial employment for selective gas and hydrocarbon adsorption and separation. Furthermore, we outline forecasts in this area to create a platform for widespread adoption of 3D‐linkers in MOF synthesis.

Macreadie, Lauren K.↗

DOE OSTI2023

Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications

Macreadie, Lauren K.↗