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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reinforcement induced microcracking during the conversion of polymer-derived ceramics

This study investigates the role of discontinuous reinforcement on the microcracking of a preceramic polymer matrix during polymer to ceramic conversion. The material system comprised a carbosilane-based, preceramic polymer reinforced with mullite particles. The preceramic resin slurry was formulated for photopolymerization on a digital light projection 3D printer. Micro X-ray computed tomography, using a synchrotron light source, monitored a printed composite part during pyrolysis. The conversion of the carbosilane matrix into Si(O)C generated microcracks in the matrix that radially extended from particles. The likelihood of microcrack formation positively correlated with particle size. The largest particles (>10 4 µm 3 volume or >60 µm side length) always abutted microcracks, while the matrix surrounding smaller particles (<5 µm) was free of microcracks. Numerical calculations showed the particles resist the shrinking matrix during its conversion. This created tensile stresses within the matrix. The driving force for microcrack growth was also analyzed with regard to the influence of matrix material parameters, particle form-factor and crack length. These results reveal the need to consider the form factor of discontinuous reinforcements, with regard to the material properties of the polymer-derived ceramic, in order to reduce or eliminate defects in the final part.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Towards intelligent diagnostic system employing integration of mathematical and engineering model

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.

42 ENGINEERING↗
DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗
DOE OSTI2024

Miniaturized Magnetoelastic Sensor System

This article describes the design, assembly, and implementation of a hand-held, magnetic-field-based sensor system that can be adapted for a variety of sensing applications. The miniaturized system is based on Chemical Identification by Magneto-Elastic Sensing (ChIMES) technology, which uses three concentric solenoid coils to wirelessly interrogate a sensor body comprised of a response material coupled to a magnetoelastic wire. The response material expands when it encounters a target, imposing mechanical stress on the wire and altering its magnetic permeability. The sensor bodies are passive, requiring no external power source, and they are small, measuring about 15 mm in length and 3.0 mm in diameter. Up to four sensor bodies can be configured as an evenly-spaced linear array. The sensor system operates by applying a low-frequency, current-stabilized, filtered triangle wave to a uniform-density excitation coil to switch the magnetic domains within the wire. Further, the responses from the sensors are picked up by a detection coil as stress-induced changes in the Faraday voltage, and the strong magnetic field induced by the excitation coil in the detection coil is nullified by a cancellation coil reverse-wound in series with the detection coil. The responses of the sensors in an array are separated in time by a linear gradient dc biasing coil. The sensors can be interrogated through metallic and nonmetallic barriers. The signals from the detection coil and the excitation coil are digitized by a pair of bipolar analog-to-digital converters (ADCs). A Raspberry Pi single-board computer (SBC) and associated software perform data acquisition and control all aspects of the sensor system hardware. The program allows the user to select the number of sensors in the array, the type of signal that is being collected, and the number of samples to take. The program also allows for signal processing of the sensor data, such as baseline correction. The program can differentiate sensor peaks from each other and calculate the magnitude of each sensor response with less than 1% error. The data are then displayed along with a graph of the signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

EDX ClaiMM

EDX ClaiMM is a centralized data & analytical platform designed to revolutionize U.S. critical minerals and materials (CMM) activities. By providing a robust digital infrastructure, ClaiMM will accelerate the combination, leveraging, and rapid utilization of vital data, advanced tools, and cutting-edge research advancements in CMM. This adaptive digital research hub connects the CMM community to essential knowledge products and offers access to interoperable datasets, databases, models, software, and tools from the National Energy Technology’s (NETL’s) Energy Data eXchange (EDX) and other authoritative sources, serving both public and private sectors. EDX ClaiMM delivers AI-informed solutions to address fundamental knowledge gaps and fosters the innovation of new techniques for enhanced characterization and recovery of CMMs within the U.S. By leveraging cloud-hosted, scalable digital infrastructure, ClaiMM meets public–private applied energy needs. It equips the CMM community with priority digital resources that harness on-site and cloud compute capabilities, enabling big data storage, advanced processing, analytics, and visualization.

Critical Materials; Critical Minerals; Rare Earth ↗
DOE OSTI2018

Adiabatic Quantum Computation Applied to Deep Learning Networks

Training deep learning networks is a difficult task due to computational complexity, and this is traditionally handled by simplifying network topology to enable parallel computation on graphical processing units (GPUs). However, the emergence of quantum devices allows reconsideration of complex topologies. We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum computation known as quantum annealing performed by a D-Wave processor. We provide a brief description of the hardware and how it solves Ising models, how we translate our data into the corresponding Ising models, and how we use available expanded topology options to explore potential performance improvements. Although we focus on the application of quantum annealing in this article, the work discussed here is just one of three approaches we explored as part of a larger project that considers alternative means for training deep learning networks. The other approaches involve using a high performance computing (HPC) environment to automatically find network topologies with good performance and using neuromorphic computing to find a low-power solution for training deep learning networks. Our results show that our quantum approach can find good network parameters in a reasonable time despite increased network topology complexity; that HPC can find good parameters for traditional, simplified network topologies; and that neuromorphic computers can use low power memristive hardware to represent complex topologies and parameters derived from other architecture choices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Implementation and Analysis for APR1400 Soft Control System

Due to the rapid advancement of digital technology, the definite technical advantages of digital control system compared to analog control system are accelerating the implementation of advanced distributed digital control system in the nuclear power plant. One of the major advantages of digital control system is the capability of Soft Control System. The design of Soft Control System for Advanced Power Reactor 1400 (APR1400) plant of Man-Machine Interface System (MMIS) is based on full digital technologies to enhance reliability, operability and maintainability. Computer-based compact workstation has been adopted in the APR1400 Main Control Room (MCR) to provide convenient working environment. This paper introduces the approaches and methodologies of Soft Control System for the Advanced Control Room (ACR). This paper also explains major design features for operation and display of the Soft Control System and its implementation to cope with regulatory requirements. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Improving Ligand Geometry in Protein Data Bank Structures Computationally

Information about the three‐dimensional structures of complex biological molecules is an indispensable research tool for many fields of study, including biology, chemistry, pharmacology, and computer science. The Protein Data Bank (PDB) is an public digital archive providing access to structures of nucleic acids, proteins, and large macromolecular machines determined experimentally by X‐ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and 3‐dimensional electron microscopy (3DEM). PDB structures undergo validation for structural plausibility upon their deposition into the PDB using the program Mogul, which cross references each deposited structure against similar data in the Cambridge Structural Database (CSD). Ligands associated with PDB structures are a common source of structural inaccuracy. In this study, a programmatic pipeline was developed to determine if ligand geometric accuracy could be improved using known computational tools. Ligands with dubious geometry were identified through a Python mining code. A sample set of the identified structures was remodeled into minimum energy conformations using the molecular dynamics program AMBER and then fit to the original experimental density maps using the molecular modeling program Coot. The refined models were compared to the original models in the PDB as well as to similar structures in the CSD using the deposition statistical validation server. Preliminary results suggest that the pipeline effectively identifies and improves ligand structures that possess inaccurate geometry. This method can be used to evaluate and improve the accuracy of specific ligands in structures both archived and pending deposition. Support or Funding Information This work was supported by an NSF REU and the RCSB PDB [NSF (DBI‐1338415), NIH, and DOE.] This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Miller, Beck↗
DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗
DOE OSTI2016

A digital matched filter for reverse time chaos

The use of reverse time chaos allows the realization of hardware chaotic systems that can operate at speeds equivalent to existing state of the art while requiring significantly less complex circuitry. Matched filter decoding is possible for the reverse time system since it exhibits a closed form solution formed partially by a linear basis pulse. Coefficients have been calculated and are used to realize the matched filter digitally as a finite impulse response filter. Numerical simulations confirm that this correctly implements a matched filter that can be used for detection of the chaotic signal. In addition, the direct form of the filter has been implemented in hardware description language and demonstrates performance in agreement with numerical results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Review of Computer-Aided Manufacturing (CAM) strategies for hybrid directed energy deposition

Hybrid additive manufacturing intertwines both additive and subtractive manufacturing layer by layer to digitally fabricate parts with complex geometries, improved surface finish and tight dimensional accuracies, the sum of which is difficult to obtain with any single process. Computer-Aided Manufacturing (CAM) software is required to orchestrate the machine toolpathing for both the deposition as well as the machining processes and is crucial for the successful fabrication of high quality structures. Additionally, CAM requires substantial operator input to account for challenging aspects of each fabricated structure. For example, deciding at which layer of deposition will the machining process continue to maintain access to complex cavities for finishing - internal features that would otherwise be unfinishable due to reach limitations or obstructions. Moreover, of the many commercially-available hybrid systems, each has a unique kinematic environment which can benefit from specific optimization of toolpath planning and substantial research has directly correlated toolpathing with the microstructure evolution, mechanical properties, porosity and residual stress state of the final fabricated part. This review explores the available strategies for CAM in the context of hybrid direct energy deposition, discusses the advantages and disadvantages of each and considers future CAM trends for this transformational digital manufacturing technology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emerging Technologies for Privacy Preservation in Energy Systems

This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.

Cali, Umit↗
DOE OSTI2025

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗
DOE OSTI2017

Operating displays - new concept: high performance displays - 118

New control rooms in Nuclear Power Plants using digital Distributed Control System (DCS) operated with software displays, are providing thousands of signals to the operating crew. When an event happens, the Flood of information provided to operators could obscure the initiator event increasing the operator response time, work load and complicating the selection of the actions to be performed. There are some initiatives that are trying to minimize the 'avalanche effect' as the alarm engineering filtering and prioritization or diagnostic displays. Likewise, new control rooms are lost the availability to know the plant status just with a quick glance to the panel indicators. Regarding Small Modular Reactors, where in some cases four operators should monitor and control twelve reactors, these problems are even more significant. The challenge is to find a solution, based on current digital systems, that supply information to operators in order to prevent events and take early actions to minimize or avoid any unexpected incident. In order to minimize human errors, Tecnatom has developed a specific set of displays, called 'High Performance Displays' (HPD) based on objectives, work environment limitation, and non-anticipated events. These displays are being design following the HFE standards: applying the NUREG-0711 elements to identify the information that they have to content and the NUREG-0700 to design the interface. The function of these displays is monitoring the plant situation in a way that the operators are able to recognize any potential event at a simple glance but controlling thousands of signals through pattern recognition, symmetries, regular polygons and colors. The objective of the HPD is to provide an integrated function minimizing the tunnel effect and reduce the workload during the diagnosis phase. Moreover, HPD enhance operators to take the right actions to anticipate the response to any event, even avoiding it, if possible This paper analyzes how HPD's have been design as well as presents the results of a preliminary validation of their capabilities. The preliminary validation is a reduce validation in a full scope simulator running two representative scenarios of abnormal and emergency operation. The analysis is focused on the first stage of the event, i.e. the detection phase, HPD's have been installed in a generic full scope simulator of a nuclear power plant, digital controlled. Both scenarios have been performed by two different operator crews. The first operator shift has worked with the conventional human machine interface, that is, displays similar to the ones currently installed in existing nuclear power plant with digital control. The second crew, reproduce the same scenarios but with HPD available. The studio collects preliminary data in order to compare results, draw lesson learned from the experience to improve HPD design, and find solutions in terms of reducing human errors, human workload, and increase the availability of the plant. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins

Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Sandy Soil Microaggregates: Rethinking Our Understanding of Hydraulic Function

Core Ideas Sandy soil microaggregates are comprised of a solid sand core encrusted with fines. Thick films control a significant proportion of water retention in coarse sandy soil. Three water pools drain sequentially: capillary water, thick films, and adsorptive films. Film seepage rates should be considered when irrigating sandy soil. This study investigated the peculiar structure of microaggregates in coarse sandy soils that exhibit only external porosity and investigated their control on soil hydrology. The microstructure underpins a hydrologic existence that differs from finer textured soils where aggregates have internal porosity. Understanding the impact of these microaggregates on soil hydrology will permit improved agricultural irrigation management and estimates associated with ecosystem capacity and resiliency. Microstructure was investigated using a digital microscope, and aspects of the structure were quantified by sedimentation and computed microtomography. Sandy soil microaggregates were observed to be comprised of a solid sand‐grain core that is coated with fines, presumably cemented by organic media. This microstructure leads to three distinct water pools during drainage: capillary water, followed by thick films (1–20 μm) enveloping the outer surfaces of the crusted microaggregates, followed by adsorbed thin films (<1 μm). The characteristics of the thick films were investigated using an analytical model. These films may provide as much as 10 to 40% saturation in the range of plant‐available water. Using lubrication theory, it was predicted that thick film drainage follows a power law function with an exponent of 2. Thick films may also have a role in the geochemical evolution of soils and in ecosystem function because they provide contiguous water and gas phases at relatively high moisture contents. And, because the rough outer crust of these microaggregates can provide good niches for microbial activity, biofilm physics will dominate thick film processes, and consequently hydrologic, biologic, and geochemical functions for coarse sandy soils.

Paradiś, Ashley↗
DOE OSTI2023

A state-of-the-art review of experimental and computational studies of granular materials: Properties, advances, challenges, and future directions

Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.

36 MATERIALS SCIENCE↗