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DOE OSTI2025

Experimental data for damage mechanics simulation challenge

While there are many computational approaches for simulating damage in rock and other materials, few have been ground truth tested with either known experimental data or with blind data sets. Here, in this work, we present a bench-mark laboratory data set for a damage mechanics challenge to compare computational approaches on damage evolution in brittle-ductile materials. The samples were fabricated through additive manufacturing to produce repeatable specimens designed to fail in controlled ways. The failure was induced in the samples using a 3-point bending test to produce different Modes such as Mode I and mixed Modes including I-II, I-III and I-II-III Modes to generate a calibration data set and a blind challenge data set. Data collected included spatial and temporal measurements from traditional digital load–displacement sensors, 2D digital image correlation measurement to map surface deformations, 3D X-ray microscopy to ground-truth the crack-failure geometry, and laser profilometry to capture surface roughness. The data sets are available, on a data repository, to the community to advance computational models to improve our ability to predict damage in brittle-ductile materials.

3-point bending↗

DOE OSTI2020

Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.

42 ENGINEERING↗

DOE OSTI2025

High polarization InAlGaAs/AlGaAs photocathodes grown using MBE

Strained superlattices of GaAs/GaAsP grown using molecular beam epitaxy (MBE) have been used for more than 20 years to generate high polarization electron beams for nuclear physics. GaAs/GaAsP superlattices have several manufacturing challenges, including the thick graded layer required for the virtual GaAsP substrate, the significant difference in optimal growth temperatures for GaAs and GaAsP, and the scarcity of MBE systems using phosphorus. InAlGaAs/AlGaAs strained superlattice photocathode are an alternative structure that eliminates many of hurdles to growing GaAs/GaAsP in MBE systems. Measurements of quantum efficiency and polarization from InAlGaAs/AlGaAs will be presented. These include studies on a variety of growth parameters to optimize performance, including digital alloys, growth temperature variations, and variation in structure.

Stutzman, Marcy L↗

DOE OSTI2020

Requirements Document for a Replacement Data Acquisition System

A flight vehicle is in the developmental stages. While in development flight testing is required by both the manufacturer and customer to rate design performance. The performance of the vehicle must fulfill the agreed upon requirements. Testing of a flight vehicle during operation requires various sensors in order to record and monitor performance for evaluation. A Data Acquisition System (DAQ) is needed in order to control and capture data from various sensors placed throughout the vehicle during flight. This DAQ will receive the analog data from the suite of sensors and convert them to a digital signal that can be transmitted to a recording station where the data will later be analyzed by test experts. Some of the key features to be included in the DAQ will be a self-check operation to verify the health of all sensors including the DAQ. The DAQ will also have the capability to store sensor calibration data files loaded during assembly and transmit those files to the recording station during flight. Breakthroughs in new technologies has allowed for higher resolution recording systems and faster sampling rates. Improved sensor data using a new Data Acquisition System (DAQ) can provide important answers to questions discovered during the last few flight tests. These questions arose when the flight system expressed unexpected behaviors. Having the capability of the new DAQ in flight tests would allow for more high-fidelity data to be captured from the system during flight tests and ultimately reducing the number of required flight test. Reducing the number of flight tests also reduce developmental cost and schedule.

42 ENGINEERING↗

DOE OSTI2020

Three-dimensional hierarchical nanoporous copper via direct ink writing and dealloying

Three-dimensional (3D) hierarchical nanoporous Cu (3DHNP-Cu) is synthesized using a combination of direct ink writing (DIW) based 3D printing, thermal sintering, and chemical dealloying of Mn–Cu alloys. Through tuning processing conditions such as ink composition and cooling rate (after sintering), with consideration of Mn loss during sintering, 3DHNP-Cu with fully bicontinuous nanostructures and negligible residual Mn can be produced after dealloying. The 3DHNP-Cu is comprised of structural features that span seven orders of magnitude, where DIW digitally controls macroscale porous features, thermal sintering and degradation of the binding polymer determines microscale porous features, and dealloying gives rise to nanoscale pores.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Object-space optimization of tomographic reconstructions for additive manufacturing

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Characterizing the Thermal-Induced Distortion of Large-Scale Polymer Composite Printed Structures

The Big Area Additive Manufacturing (BAAM) system has been used to print large-scale parts, such as automotive structures and molds for tooling, with fiber-reinforced polymer composites. Incorporating reinforcing fibers in printed parts is commonly used to increase stiffness and strength, but it also introduces significant anisotropy in the thermomechanical performance, which can lead to distortion and warping during thermal cycling. Characterizing and understanding how a printed tool distorts is crucial to maintaining tolerances and avoiding part failure. This study uses digital image correlation (DIC) to measure the coefficient of thermal expansion (CTE) of a printed part from room temperature and to a known steady state temperature. The samples were printed with carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS). Various nozzle geometries were evaluated in this study with the intent of minimizing the thermal- induced distortion experienced by printed parts.

Corum, Tyler↗

DOE OSTI2026

Powder metallurgy hot isostatic pressing - workshop report

The findings of the powder metallurgy (PM) hot isostatic pressing (HIP) workshop confirmed that the accelerated adoption of this technology requires the following action items: 1. By 2035, the HIP infrastructure must be scaled to sizes larger than 4 .0 meters for manufacturing components relevant to energy and defense industries at large. 2. The community must develop cost-effective and agile standards, qualifications, and certification pathways for these HIP parts. 3. At the same time, domestic powder production should be expanded to a wide range of metallic alloys, including Cr-Mo steels, stainless steels, and titanium- and nickel-based superalloys, as well as to emerging alloy classes in the refractory space. 4. The above initiatives can be enhanced by implementing digital modeling and controls, including AI-driven simulations, in situ monitoring, and digital twins.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Wafer-Scale Synthesis of 2D Materials by an Amorphous Phase-Mediated Crystallization Approach

The interest in the wafer-scale growth of two-dimensional (2D) materials, including transition metal dichalcogenides (TMDCs), has been rising for transitioning from lab-scale devices to commercial-scale systems. Among various synthesis techniques, physical vapor deposition, such as pulsed laser deposition (PLD), has shown promise for the wafer-scale growth of 2D materials. However, due to the high volatility of chalcogen atoms (e.g., S and Se), films deposited by PLD usually suffer from a lack of stoichiometry and chalcogen deficiency. To mitigate this issue, excess chalcogen is necessary during the deposition, which results in problems like uniformity or not being repeatable. This study demonstrates a condensed-phase or amorphous phase-mediated crystallization (APMC) approach for the wafer-scale synthesis of 2D materials. This method uses a room-temperature PLD process for the deposition and formation of amorphous precursors with controlled thicknesses, followed by a post-deposition crystallization process to convert the amorphous materials to crystalline structures. This approach maintains the stoichiometry of the deposited materials throughout the deposition and crystallization process and enables the large-scale synthesis of crystalline 2D materials (e.g., MoS 2 and WSe 2 ) on Si/SiO 2 substrates, which is critical for future wafer-scale electronics. We show that the thickness of the layers can be digitally controlled by the number of laser pulses during the PLD phase. Optical spectroscopy is used to monitor the crystallization dynamics of amorphous layers as a function of annealing temperature. The crystalline quality, domain sizes, and the number of layers were explored using nanoscale and atomistic characterization (e.g., AFM, STEM, and EDS) along with electrical characterization to explore process–structure–performance relationships. Finally, this growth technique is a promising method that could potentially be adopted in conventional semiconductor industries for wafer-scale manufacturing of next-generation electronic and optoelectronic devices.

2D electronics↗

DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗

DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Characterization of edge damage induced on REBCO superconducting tape by mechanical slitting

Abstract Rare-earth barium-copper-oxide (REBCO) superconductors are high-field superconductors fabricated in a tape geometry that can be utilized in magnet applications well in excess of 20 T. Due to the multilayer architecture of the tape, delamination is one cause of mechanical failure in REBCO tapes. During a mechanical slitting step in the manufacturing process, edge cracks can be introduced into the tape. These cracks are thought to be potential initiation sites for crack propagation in the tapes when subjected to stresses in the fabrication and operation of magnet systems. We sought to understand which layers were the mechanically weakest by locating the crack initiation layer and identifying the geometrical conditions of the slitter that promoted or suppressed crack formation. The described cracking was investigated by selectively etching and characterizing each layer with scanning electron microscopy, laser confocal microscopy, and digital image analysis. Our analysis showed that the average crack lengths in the REBCO, LaMnO 3 (LMO) and Al 2 O 3 layers were 34 μ m, 28 μ m, and 15 μ m, respectively. The total number of cracks measured in 30 mm of wire length was between 3000 and 5700 depending on the layer and their crack densities were 102 cracks mm −1 for REBCO, 108 cracks mm −1 for LMO, and 183 cracks mm −1 for Al 2 O 3 . These results indicated that there are separate crack initiation mechanisms for the REBCO and the LMO layers, as detailed in the paper. With a better understanding of the crack growth behavior exhibited by REBCO tapes, the fabrication process can be improved to provide a more mechanically stable and cost-effective superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

RD53A: A large-scale prototype chip for the phase II upgrade in the serially powered HL-LHC pixel detectors

The phase II upgrade of the HL-LHC experiments within the LHC intends to deepen the studies of the Higgs boson and to allow the discovery of further particles by adding an integrated luminosity of about 4000 fb -1 over 10 years of operation. This upgrade would overwhelm the installed pixel detector readout chips with higher hit rates and radiation levels than ever before. To match these extreme requirements the RD53 collaboration, a joint effort between ATLAS and CMS, developed RD53A, a new generation pixel detector readout chip prototype manufactured in a 65 nm CMOS technology. It is half the size of the final pixel chips and designed to meet requirements in the face of 3 GHz/cm 2 hit rate after irradiation to 500 Mrad. The detector is able to use 50×50 μm 2 or 25×100 μm 2 pixels with high readout speed of up to 4 links per chip with 1.28 Gbit/s each. Shunt–LDO regulators integrated on the bottom of the chip provide the required voltages to the two power domains, analog and digital. These regulators enable serial powering of the pixel modules, which is the only feasible, radiation hard scheme to ensure acceptable power cable losses and to stay within the material budget for the future pixel detectors. Finally, an overview of the status and challenges of serial powering and the Shunt–LDO regulator development will be given.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Dynamic fracture of glass fiber-reinforced ductile polymer matrix composites and loading rate effect

Here, the dynamic fracture of S-2 glass fiber-reinforced polymer matrix composites (FRPMCs) was investigated in this study. The matrix ductility was improved by a recently developed network topology modification technique via mixing partially reacted substructures (mPRS). The composite material was manufactured and characterized by micro-CT scanning and scanning electron microscopy (SEM). Dynamic single-edge notched bending (d-SENB) experiments were performed on the composites by using a modified split-Hopkinson pressure bar. Each specimen’s fracture process was visualized by ultrafast X-ray imaging. Such in-situ radiography enabled identifying the damage initiation below 50-micron scale and inspecting its propagation through the internal structures of opaque composites, thereby accurately quantifying the composites’ mechanical properties. Furthermore, the identical d-SENB experiments were designed and the digital image correlation (DIC) was employed to monitor the stress wave propagation on the composite specimens. The force and deflection measurements were modified and correlated to the physical damage processes. Besides, quasi-static SENB experiments were conducted to identify the loading rate effects on the composites’ fracture behaviors. The force and deflection history, bending stiffness, energy dissipation, and fracture toughness at different loading rates were quantified and compared. Finally, post-fracture analysis by micro-CT scanning and SEM provided physical observations on the variation of the fracture morphology by different loading rates.

42 ENGINEERING↗

DOE OSTI2025

Subvoxel Control of Fiber Orientation via Multidirectional Shearing in 3D Printing

Anisotropy, the characteristic of materials exhibiting different properties based on their direction, is widespread in nature. Conventional manufacturing techniques often fall short in recreating such complex anisotropy. While 3D printing allows for precise fiber deposition in anisotropic composites, previous studies have only achieved bulk reorientation of fibers at the voxel level in two dimensions. This limits the replication of localized 3D anisotropies found in natural materials. To address this, a novel 3D printing technique is presented that enables subvoxel control of fiber orientation in all three directions using multidirectional shearing via nozzle rotation and inclination. The fiber orientation control in these experimental tests is driven by a numerical model, enabling a fully digital approach to program microstructures within a strand. This programmability is demonstrated through mechanical and thermal tests, illustrating a localized and controllable response to external stimuli. Achieving such complex anisotropy holds potential across several fields, including wearables and biomedical implants, lightweight composite structures, and energy storage technologies such as batteries and supercapacitors.

3D printing↗

DOE OSTI2026

Energy Systems Integration Facility Stewardship Summary: Fiscal Year 2025

A summary of NLR's stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, capability upgrades, and examples of R&D impact. 2025 brought a national focus on energy and ESIF is meeting the moment. All eyes are on data centers and domestic manufacturing and bringing the benefits of artificial intelligence to power system planning and operations. In step with national priorities, ESIF is building out capabilities that advance secure, reliable, and affordable power. ESIF hosted 190 multidisciplinary research projects, 855 high-performance computer users, and collaborated with 81 partners from industry, academia, research, and federal agencies. These research projects resulted in an AI method for detecting high-impedance faults with 90% accuracy, a grid controls demonstration in Connecticut, power quality validation of CorePower's flagship inductor, and a cybersecurity assessment of potential rogue capabilities in digitally connected energy devices. Facility infrastructure improvements enhanced the thermal research network, the SCADA system, the cyber range, power hardware-in-the-loop testing, and more. With support from the U.S Department of Energy (DOE), the ESIF laboratories continue to deliver leading solutions for secure, reliable, and affordable power.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

A critical review on additive manufacturing of refractory alloys from a data analytics perspective- beyond nickel-based superalloys

Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.

Additive manufacturing↗

DOE OSTI2024

Component pose reconstruction using a single robotic total station for panelized building envelopes

The construction industry can benefit greatly from increased automation in construction processes in terms of precision, time, and labor costs. This is especially true for prefabricated construction for either new buildings or envelope retrofits. Here, prefabricated components are manufactured offsite according to design specifications and installed onsite. Accurate information on the components position and orientation (pose) is needed to achieve this. A tool named Real-Time Evaluator (RTE), designed to autonomously track prefabricated components as they are being installed and provide real-time installation instructions, is currently under development using a single robotic total station. This is challenging since at least three points are needed to determine the pose of an object in space. To achieve this goal with a single robotic total station, two key algorithms were developed: the resection algorithm aligns the digital twin with the physical twin regardless of the location of the total station, and the transformation algorithm gives instructions of translations and rotations to installers to achieve the desired installation pose. The algorithms were evaluated experimentally on a lab-scale demonstration. Results show that the resection algorithm achieved an average error of < 3.1 mm, while the transformation algorithm predicted the rotation angle along a single axis with an error of < 0.5◦.

Tang, Mengjia↗