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DOE OSTI2019

Atomically Defined Edge-Doping of Graphene Nanoribbons for Mesoscale Electronics

The outstanding transformative potential of graphene, an infinite two-dimensional sheet of carbon atoms tightly packed into a honeycomb lattice, has been recognized mostly due to its exceptionally high electric conductivity, thermal conductivity, and tensile strength. These undeniably very desirable properties, however, represent only a very small facet of the true potential of all-sp2 carbon materials and its promise to revolutionize the field of molecular electronics. Graphene's most unusual characteristics emerge when the infinite macroscopic sheet is scaled down to nanometer dimensions. The exploration, realization, and implementation of these truly exotic magnetic and electronic properties rely on the development of innovative synthetic strategies that provide atomically precise control over the self-assembly of mesoscale objects. While traditional device architectures based on inorganic semiconductors have been fabricated using a top-down approach, we pursued a diametrically opposite strategy. Our design of a new generation of high-performance electronic materials relies on a modular bottom-up strategy. This highly multidisciplinary research program has been rooted in the synthesis of functional organic materials with precisely defined properties, their controlled assembly into hierarchical structures, and the evaluation of their performance at the molecular and the macroscopic scale. A part of this research program we have developed the chemical tools required to synthesize and to fine-tune the physical properties of graphene nanoribbons (GNR) with atomic precision. We further demonstrated that the innovative techniques and the expanded knowledge can be used to rationally tailor desired physical properties and function into nanometer-scale carbon-based electronic devices; e.g. transistors as logic gates in computing, data storage media based on electrically gated spin valves, or molecular amplifiers, all functionally integrated in atomically defined GNRs. We demonstrated the design and experimental realization of a rationally engineered graphene nanoribbon superlattice that hosts a 1D array of symmetry-protected topological states. We developed a hierarchical bottom-up fabrication strategy for graphene nanoribbons that preferentially exhibit a single heterojunction interface rather than a random statistical sequence of junctions along the ribbon. We reported the bottom-up synthesis of a series of atomically precise graphene nanoribbons featuring trigonal planar heteroatom dopants (nitrogen, oxygen, and sulfur) along the edges that induce a narrowing of the intrinsic band gap by ~0.2–0.3 eV per dopant atom. We reported the bottom-up synthesis of a graphene nanoribbon heterostructure featuring a highly tunable porphyrin quantum dot embedded at the center of a solution processable graphene nanoribbon. We reported a new technique for fabricating bottom-up graphene nanoribbon heterojunctions through a late-stage chemical transformation that results in atomically sharp heterojunction structures from a single precursor. We demonstrated the intrinsic performance enhancement of a composite material comprised of gold nanoparticles (AuNPs) embedded in a bottom-up synthesized graphene nanoribbon matrix for the electrocatalytic reduction of CO 2 . We developed a scalable, solution-based approach toward the synthesis of an exotic class of polycyclic aromatic hydrocarbons that exhibit unusual electronic and magnetic properties in the ground state. We reported the bottom-up synthesis and characterization of atomically-defined graphene nanoribbons featuring a regioregular pattern of group III dopant atoms (boron) along the backbone of the ribbon.

36 MATERIALS SCIENCE↗

DOE OSTI2021

New insight into strain and composition of BaZrO 3 nanorods in REBCO superconductor

We report on strain and composition effects associated with growth of self-assembled BaZrO 3 (BZO) nanorods in REBa 2 Cu 3 O 7–δ (REBCO) superconductors (RE = rare earth = Y and Gd), which have a profound effect on flux pinning and in-field critical current performance. The a-b plane mismatch between BZO and REBCO is never fully coherently accommodated. Instead, the nanorods always assume a size at least one unit cell smaller than the corresponding 'hole' in the REBCO matrix, thus providing deep minima of in-plane mismatch strain. Next, we show that the nominal BZO nanorods are in fact solid solution Ba 2+ (Zr 4+ 1–z RE 3+ z )O 3–δ perovskite, thus strongly affecting the stoichiometry and relative amounts of REBCO and BZO. We demonstrate that by varying only the Ba content in the nominal composition of 15 mol.% BZO + REBCO, the unit cell density of BZO can be tuned from 5% to 23%, and the linear density of RE 2 O 3 (REO) precipitates from 18 to 1 μm –1 . Furthermore, the results explain the wide range of pinning performances observed for the same nominal amount of Zr addition and provide insight into the mechanisms behind the complex phenomenon of growth of nanorods by self-assembly in REBCO superconductors.

2G-HTS↗

DOE OSTI2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

17 WIND ENERGY↗

NASA NTRS1998

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

Maly, K.↗

DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗

NASA NTRS1993

Control system design for flexible structures using data models

The dynamics and control of flexible aerospace structures exercises many of the engineering disciplines. In recent years there has been considerable research in the developing and tailoring of control system design techniques for these structures. This problem involves designing a control system for a multi-input, multi-output (MIMO) system that satisfies various performance criteria, such as vibration suppression, disturbance and noise rejection, attitude control and slewing control. Considerable progress has been made and demonstrated in control system design techniques for these structures. The key to designing control systems for these structures that meet stringent performance requirements is an accurate model. It has become apparent that theoretically and finite-element generated models do not provide the needed accuracy; almost all successful demonstrations of control system design techniques have involved using test results for fine-tuning a model or for extracting a model using system ID techniques. This paper describes past and ongoing efforts at Ohio University and NASA MSFC to design controllers using 'data models.' The basic philosophy of this approach is to start with a stabilizing controller and frequency response data that describes the plant; then, iteratively vary the free parameters of the controller so that performance measures become closer to satisfying design specifications. The frequency response data can be either experimentally derived or analytically derived. One 'design-with-data' algorithm presented in this paper is called the Compensator Improvement Program (CIP). The current CIP designs controllers for MIMO systems so that classical gain, phase, and attenuation margins are achieved. The center-piece of the CIP algorithm is the constraint improvement technique which is used to calculate a parameter change vector that guarantees an improvement in all unsatisfied, feasible performance metrics from iteration to iteration. The paper also presents a recently demonstrated CIP-type algorithm, called the Model and Data Oriented Computer-Aided Design System (MADCADS), developed for achieving H(sub infinity) type design specifications using data models. Control system design for the NASA/MSFC Single Structure Control Facility are demonstrated for both CIP and MADCADS. Advantages of design-with-data algorithms over techniques that require analytical plant models are also presented.

Irwin, R. Dennis↗

DOE OSTI2019

Reference Natural Gas Flames at Nominally Autoignitive Engine-Relevant Conditions

Laminar natural gas flames are investigated at engine-relevant thermochemical conditions where the ignition delay time r is short due to very high ambient temperatures and pressures. At these conditions, it is not possible to measure or calculate well-defined values for the laminar flame speed S-l, laminar flame thickness delta(l), and laminar flame time scale tau(l) = delta(l)/S-l due to the explosive thermochemical state. Here, the corresponding reference values, S-R, delta(R), and tau(R) = S-R/S-R, that account for the effects of autoignition, are numerically estimated to investigate the enhancement of flame propagation, and the competition with autoignition that arises under nominally autoignitive conditions (characterised here by the number tau/tau(R)). Large values of tau/tau(R) indicate that autoignition is unimportant, values near or below unity indicate that flame propagation is not possible, and intermediate values indicate that a combination of both flame propagation and autoignition may be important, depending upon factors such as device geometry, turbulence, stratification, et cetera. The reference quantities are presented for a wide range of temperatures, equivalence ratios, pressures, and hydrogen concentrations, which includes conditions relevant to stationary gas turbine reheat burners and boosted spark ignition engines. It is demonstrated that the transition from flame propagation to autoignition is only dependent on residence time, when the results are non-dimensionalised by the reference values. The temporal evolution of the reference values are also reported for a modelled boosted SI engine. It is shown that the nominally autoignitive conditions enhance flame propagation, which may be an ameliorating factor for the onset of engine knock. The calculations are performed using a recently-developed, detailed 177 species mechanism for C0-C3 chemistry that is derived from theoretical chemistry and is suitable for a wide range of thermochemical conditions as it is not tuned or optimised for a particular operating condition. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

autoignition↗

DOE OSTI2019

Enhanced π–π Interactions of Nonfullerene Acceptors by Volatilizable Solid Additives in Efficient Polymer Solar Cells

Abstract Fine‐tuning of the nanoscale morphologies of the active layers in polymer solar cells (PSCs) through various techniques plays a vital role in improving the photovoltaic performance. However, for emerging nonfullerene (NF) PSCs, the morphology optimization of the active‐layer films empirically follows the methods originally developed in fullerene‐based blends and lacks systematic studies. In this work, two solid additives with different volatilities, SA‐4 and SA‐7, are applied to investigate their influence on the morphologies and photovoltaic performances of NF‐PSCs. Although both solid additives effectively promote the molecular packing of the NF acceptors, due to the higher volatility of SA‐4, the devices processed with SA‐4 exhibit a power conversion efficiency of 13.5%, higher than that of the control devices, and the devices processed with SA‐7 exhibit poor performances. Through a series of detailed morphological analyses, it is found that the volatilization of SA‐4 after thermal annealing is beneficial for the self‐assembly packing of acceptors, while the residuals due to the incomplete volatilization of SA‐7 have a negative effect on the film morphology. The results delineate the feasibility of applying volatilizable solid additives and provide deeper insights into the working mechanism, establishing guidelines for further material design of solid additives.

Yu, Runnan↗

DOE OSTI2020

Colloidal Synthesis Path to 2D Crystalline Quantum Dot Superlattices

By combining colloidal nanocrystal synthesis, self-assembly, and solution phase epitaxial growth techniques, we developed a general method for preparing single dot thick atomically attached quantum dot (QD) superlattices with high-quality translational and crystallographic orientational order along with state-of-the-art uniformity in the attachment thickness. The procedure begins with colloidal synthesis of hexagonal prism shaped core/shell QDs (e.g., CdSe/CdS), followed by liquid subphase self-assembly and immobilization of superlattices on a substrate. Solution phase epitaxial growth of additional semiconductor material fills in the voids between the particles, resulting in a QD-in-matrix structure. The photoluminescence emission spectra of the QD-in-matrix structure retains characteristic 0D electronic confinement. Importantly, annealing of the resulting structures removes inhomogeneities in the QD–QD inorganic bridges, which our atomistic electronic structure calculations demonstrate would otherwise lead to Anderson-type localization. The piecewise nature of this procedure allows one to independently tune the size and material of the QD core, shell, QD–QD distance, and the matrix material. These four choices can be tuned to control many properties (degree of quantum confinement, quantum coupling, band alignments, etc.) depending on the specific applications. Lastly, cation exchange reactions can be performed on the final QD-in-matrix, as demonstrated herein with a CdSe/CdS to HgSe/HgS conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Modulation of the Electronic and Vibrational Landscape in Lead Organic Chalcogenides

Metal organic chalcogenides (MOCs) are an emerging class of two-dimensional (2D) materials featuring tunable band gaps and strong light-matter interactions with great potential for optical and polaritonic applications. Lead organic chalcogenides (LOCs) stand out among MOCs for sustaining long-lived coherent optical phonons despite their distorted lattices. However, the strong electron–phonon coupling also leads to rapid charge carrier self-trapping, hindering carrier transport. Herein, we demonstrate that charge carrier self-trapping is suppressed in Se- and Te-based LOCs. Specifically, the expansive Se and Te orbitals give rise to more dispersive conduction bands and reduced electron effective masses, thereby mitigating carrier self-trapping. As a result of reduced carrier self-trapping, the Se- and Te-based LOCs exhibit enhanced band-to-band photoluminescence and improved charge transport performance. Furthermore, our results provide a synthetic route to Se- and Te-based LOC single crystals and demonstrate the potential of orbital engineering to tune their electronic and phononic properties.

Chalcogenides↗

DOE OSTI2022

Engineering metal–metal oxide surfaces for high-performance oxygen reduction on Ag–Mn electrocatalysts

Understanding fundamental material–property relationships in mixed-element catalyst systems is crucial to advancing the viability of renewable electrochemical energy technologies, an important part of creating a more sustainable future. Herein, we report our insight on the nature and dynamics of highly active silver–manganese oxide (Ag–MnOx) catalyst surfaces for the oxygen reduction reaction (ORR) via a combined experimental–theoretical approach. Experimentally, we synthesize well-mixed Ag–Mn co-deposited thin films that are measurably flat and smooth, despite Mn surface migration and oxidation upon air exposure and electrochemical measurements. Cyclic voltammetry in 0.1 M KOH demonstrates up to 10-fold specific activity enhancements over pure Ag at 0.8 V vs. RHE for Ag-rich films (70–95% Ag in bulk). To further understand the Ag–Mn system, separate samples were synthesized with small amounts of Mn sequentially deposited onto the surface of a pure Ag thin film (Mn@Ag), ranging from partial to full surface coverage (down to 0.3 nm Mn $cm^{-2}_{geo}$ ~ 0.2 μg Mn $cm^{-2}_{geo}$). These sequentially deposited Mn@Ag films show analogous performance to their co-deposited counterparts indicating similar enhanced active sites. With density functional theory (DFT), we calculate that this enhancement arises from the tuned d-band of these material surfaces owing to the optimal hybridization of the electronic structures in specific Ag and MnO x geometries. Together, electrochemical measurements, DFT calculations, X-ray absorption spectroscopy, and valence-band X-ray photoelectron spectroscopy suggest synergistic electronic interactions between Ag and MnOx yield enhanced oxygen adsorption, and thus ORR activity, with DFT highlighting the Ag–MnO x interface sites as the most enhanced. This work demonstrates how combined experimental–theoretical methods can help design electrocatalysts with enhanced electrocatalytic properties and understand the nature of complex mixed metal–metal oxide surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Continuum-scale modeling of helium bubble bursting under plasma-exposed tungsten surfaces

We present a comparison between a continuum-scale drift-diffusion-reaction cluster dynamics prediction of helium retention in low-energy helium plasma exposed tungsten and experimental measurements, in a temperature regime that did not produce tungsten fuzz. Our cluster dynamics model, Xolotl, has been successfully benchmarked to high helium implantation flux MD simulations at relatively low implanted fluence. In this article, we also describe the extension of the Xolotl DDR model to incorporate the effect of bubble bursting, which is observed in very high rate MD simulations, as well as MD simulations at longer times than simulated in our prior benchmarking comparison. The bursting model parameters have been tuned by comparing to MD simulations at a flux of 5.0 × 10 27 m –2 s –1 , and also compared to lower implanted fluence simulations performed at ~4.0 × 10 25 m –2 s –1 . This article then reports on the consistency of the Xolotl predictions with respect to the size of the simulated cluster phase space (i.e. the maximum cluster size), initial vacancy concentration, and bubble growth trajectory (maximum number of helium atoms per vacancy). Finally, our simulation results are compared to helium plasma experiments that did not produce fuzz. While the Xolotl predictions including bubble bursting are in quantitative agreement with high-flux MD simulations, the initial comparison to plasma exposure experiments at a flux on the order of 10 21 m –2 s –1 disagree by more than an order of magnitude, and in fact cannot reproduce the trends in helium retention with varying exposure temperature. Modifying the initial vacancy concentrations and helium cluster diffusion behavior in Xolotl leads to a reasonable agreement with the experimental observations, although the underlying physical explanation for these modifications remains unclear. The predicted helium content at experimentally relevant fluxes has been shown to be relatively insensitive to the parameters used in the bubble bursting model implemented in Xolotl, although these parameters have a larger influence at higher flux. As a result, more systematic comparisons between the modeling predictions with both experiments and MD simulation results is expected to improve the bubble bursting model in Xolotl in the future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

We present Data-Driven Invertible Neural Surrogates of Atmospheric transmission, or DINSAT. DINSAT is a novel framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned -- by virtue of autodifferentiation and differentiable programming -- to construct a surrogate atmospheric profile to model the observed data. The framework has utility in (i) performing atmospheric correction, (ii) recasting spectral data between various modalities (e.g. radiance and reflectance at the surface and at the sensor), and (iii) inferring atmospheric transmission profiles, such as absorbing bands and their relative magnitudes. We demonstrate the utility of these methods by performing a canonical atmospheric correction task for the purposes of further analysis - in this case, target detection within a scene.

Koch, James V.↗

DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗

DOE OSTI2022

Estimation of High-Altitude Electromagnetic Pulse Signal Leakage into Power Generation Facilities: Simulations and Measurements

The coupling of early-time high-altitude electromagnetic pulse signals into a power generation facility was investigated. This study encompasses both electromagnetic simulation and on-site experimental efforts that are relevant to shielding effectiveness estimation with a passive technique. As an initial step in understanding the attenuation models involved, simulations were carried out as a function of frequency, building properties, and signal incidence angle. Testing was then performed at the US Department of Energy’s Oak Ridge National Laboratory campus to identify the equipment needed for signal collection and to fine-tune the experimental procedure. Finally, as an application of the models and methods presented, measurements were conducted at a power generation plant to derive the field transfer functions and coupled high-altitude electromagnetic pulse waveforms for various locations within the facility.

42 ENGINEERING↗

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↗

NASA NTRS1984

A liquid helium cooled Fabry-Perot infrared spectrometer

A liquid-helium-cooled Fabry-Perot infrared spectrometer offering excellent performance in the middle infrared range has been constructed and tested. The spectrometer is based on newly developed superconducting actuators which control the alignment and tuning at 4.2 K with virtually no power dissipation within the instrument. Depending on the availability of high-quality etalons, this type of spectrometer should be useful in a wide range of infrared astronomical spectroscopy applications.

Nishimura, T.↗

NASA NTRS1985

Structural tailoring of engine blades (STAEBL) user's manual

This User's Manual contains instructions and demonstration case to prepare input data, run, and modify the Structural Tailoring of Engine Blades (STAEBL) computer code. STAEBL was developed to perform engine fan and compressor blade numerical optimizations. This blade optimization seeks a minimum weight or cost design that satisfies realistic blade design constraints, by tuning one to twenty design variables. The STAEBL constraint analyses include blade stresses, vibratory response, flutter, and foreign object damage. Blade design variables include airfoil thickness at several locations, blade chord, and construction variables: hole size for hollow blades, and composite material layup for composite blades.

Brown, K. W.↗