Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “digital computing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗
DOE OSTI2023

Implementation and experimental validation of nonlocal damage in a large-strain elasto-viscoplastic FFT-based framework for predicting ductile fracture in 3D polycrystalline materials

Ductile materials, such as metal alloys, can undergo substantial deformation before failure. Additionally, these materials are usually of polycrystalline composition and exhibit strongly anisotropic behavior at small length scales. Previously developed fast Fourier transform (FFT)-based models can model ductile fracture of isotropic materials or the elastic–plastic behavior of anisotropic polycrystalline materials; however, there remains a need to couple both capabilities. This work extends a large-strain FFT-based crystal plasticity model to simulate ductile fracture of polycrystalline materials. In this work, a triaxiality-based continuum damage mechanics (CDM) formulation is incorporated into a large-strain elasto-viscoplastic FFT (LS-EVPFFT) framework. The CDM formulation is augmented with an integral-based nonlocal regularization approach that correctly handles gas-phase material necessary to model unconstrained surfaces. To validate the damage-enabled LS-EVPFFT framework, mesoscale copper tensile coupons were machined using microwire electrical discharge machining and experimentally characterized using electron backscatter diffraction. In-situ optical digital image correlation was performed during uniaxial testing to provide a side-by-side comparison of the experimental and computational strain fields and stress–strain responses. The damage-enabled LS-EVPFFT framework can simulate the complete macroscopic stress–strain response of ductile polycrystals to failure. The model reproduces necking behavior that qualitatively agrees with experimental observations. By leveraging the relatively low computational cost of the damage-enabled LS-EVPFFT framework, the framework presented here allows the ductile fracture response of 3D polycrystalline materials to be tractably predicted.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Toward Magnetic Resonance–Only Simulation: Segmentation of Bone in MR for Radiation Therapy Verification of the Head

Purpose: To develop a practical method to localize bones in magnetic resonance (MR) images, to create “computed tomography–like” MR images (ctMRI) that could be used for radiation therapy verification, and to generate MR-based digitally reconstructed radiographs (DRR). Methods and Materials: Using T1-weighted MR images, an air mask was derived from the manual contouring of all airways within the head and neck region using axial images at 6 anatomic levels. Compact bone, spongy bone, and soft tissue masks were then automatically generated using the statistical data derived from MR intensities and the air mask. ctMRI were then generated by mapping the MR intensities of the voxels within these masks into the CT number ranges of corresponding tissues. MR-based DRRs created from ctMRI were quantitatively evaluated using the co-registered MR and CT head images of 20 stereotactic radiosurgery patients. Ten anatomical points, positioned on the skull segmented using a threshold of 300 HU, in CT and ctMRI, were used to determine the differences in distance between MR-based DRRs and CT-based DRRs, and to evaluate the geometric accuracy of ctMRI and MR-based DRRs. Results: The bony structures were identified on ctMRI and were visible in the MR-based DRRs. From the 20 patient cases, the mean geometric difference and standard deviation between the 10 anatomical points on MR-based and CT-based DRRs was −0.05 ± 0.85 mm, respectively. This included uncertainty in image fusion. The maximum distance difference was 1.88 mm. Conclusions: A practical method was developed to segment bone from MR images. The ctMRI created can be used for radiation treatment verification when MR-only simulation is performed. MR-based DRRs can be used in place of CT-based DRRs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Patterns of Recurrence After Low-Dose-Rate Prostate Brachytherapy: A Population-Based Study of 2223 Consecutive Low- and Intermediate-Risk Patients

Objectives: This study examined patterns of recurrence after low–dose-rate prostate brachytherapy (LDR-PB), estimated local recurrence rate and compared that rate to the estimated local recurrence rate after radical prostatectomy (RP). Methods and Materials: A prospective database was maintained with clinical, dosimetric, and outcome data for all LDR-PB implantation procedures performed at our institution. From 1998 to 2008, 2223 patients with prostate cancer received LDR-PB without supplemental external beam radiation therapy. Patients who developed Phoenix-defined biochemical failure were reviewed for sites of relapse and investigations completed. Results: At a median follow-up of 5 years, 108 of 2223 patients (4.8%) developed biochemical relapse. In 1 additional patient, local relapse was found on transurethral prostate resection, but his prostate-specific antigen concentration was well short of triggering Phoenix-defined failure. Of the 109 patients with disease relapse, 18 of 2223 (0.8%) had a proven local recurrence, and 30 of 2223 (1.3%) had a proven distant recurrence. The remaining 61 of 2223 patients (2.7%) had unidentified sites of recurrence; of these, 57 patients (93%) had digital rectal examinations (DREs), 18 (30%) had post-treatment biopsies, 45 (74%) had bone scans, and 34 (56%) had computed tomography imaging of the abdomen and pelvis. If every biochemical failure were local, the local recurrence rate would be as high as 4.9%; however, by excluding those with proven distant failure and those with both a negative DRE and biopsy, we estimate that the local recurrence rate is 2.7% or less. Conclusions: In the context of limitations of the study design, our population-based analysis indicates that the local recurrence rate after LDR-PB is as low or lower than that after RP in our jurisdiction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2018

Distributed Real-Time Simulation and its Applications to Wind Energy Research

This paper presents a novel approach - distributed Real-Time Simulations (RTS) - for innovative wind energy and grid integration research. Distributed RTS is performed with Digital Real-Time Simulators (DRTS) in different geographical locations and connected over the Internet. Geographically distributed RTS supports - higher computation capability for RTS, leveraging distributed assets, novel testing techniques and hybridized smart grid testbeds. This paper discusses some of the basic aspects of distributed RTS with the communication delay as a probabilistic variable. To leverage hardware resources and enable flexible collaboration of national laboratories, the distributed RTS connection between the simulators at Idaho National Laboratory (INL) and National Wind Test Center (NWTC) at National Renewable Energy Laboratory (NREL) is established. The main objective of this distributed lab setup is to perform wind turbine characterization and assessment of wind integration studies in large scale grids. Several systematic steps based on integrating models are carried out to assess the impact of communication delay on distributed RTS.

17 WIND ENERGY↗
DOE OSTI2022

A Quench Detection and Monitoring System for Superconducting Magnets at Fermilab

A quench detection system was developed for protecting and monitoring the superconducting solenoids for the Muon-to-Electron Conversion Experiment (Mu2e) at Fermilab. The quench system was designed for a high level of dependability and long-term continuous operation. It is based on three tiers: Tier-I, FPGA-based Digital Quench Detection (DQD); Tier-II, Analog Quench Detection (AQD); and Tier-3, the quench controls and data management system. The Tier-I and Tier-II are completely independent and fully redundant systems. The Tier-3 system is based on National Instruments (NI) C-RIO and provides the user interface for quench controls and data management. It is independent from Tiers I & II. The DQD provides both quench detection and quench characterization (monitoring) capability. Both DQD and AQD have built-in high voltage isolation and user programmable gains and attenuations. The DQD and AQD also includes user configured current dependent thresholding and validation times. A 1 st article of the three-tier system was fully implemented on the new Fermilab magnet test stand for the HL-LHC Accelerator Up-grade Project (AUP). It successfully provided quench protection and monitoring (QPM) for a cold superconducting bus test in November 2020. The Mu2e quench detection design has since been implemented for production testing of the AUP magnets. A detailed description of the system along with results from the AUP superconducting bus test will be presented.

monitoring↗
DOE OSTI2018

A Locally Adapting Technique for Edge Detection using Image Segmentation

Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about each boundary. We demonstrate the success of this algorithm at locating boundaries and providing uncertainty bands on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes

Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Parallax-corrected VISST-derived pixel-level products from satellite GOES-16

The NASA Langley group led by William Smith produced GOES-16 satellite cloud retrievals over an approximate 10 by 10 degree region over the CACTI field campaign location. These retrievals are described here: https://www.arm.gov/capabilities/vaps/visst and are available for download here . They use algorithms historically called VISST that are now referred to as SatCORPS. More information can be found in Trepte et al. (2019), Minnis et al. (2021), and Yost et al. (2021). If using this dataset, please cite these references, the CACTI VISST dataset DOI found at the download link above, and this dataset’s DOI. The CACTI VISST pixel-level retrievals are on a 2 km spatial grid and available every 15 minutes (every 10 minutes late in the campaign), producing 21,765 files for the entire field campaign between October 2018 and April 2019. They are not corrected for parallax error, which is an offset in the actual geographical location of a cloud above the surface due to the satellite viewing the cloud partly from the side off nadir. This dataset applies a correction for parallax using the location relative to the satellite and the retrieved cloud top height above the surface, which allows the dataset to be geo-located with surface-based observations. The parallax correction for each location depends on the longitude, latitude and cloud top height above ground level (AGL) for that longitude and latitude in the original VISST files. The cloud top height AGL requires first computing the surface elevation at each VISST grid point. Data from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global Digital Elevation Map Version 3 at 30-m resolution is projected onto the VISST grid using conservative coarsening (conserving surface elevation) in the xESMF Python package. The surface elevation is then subtracted from the VISST-retrieved cloud top height above mean sea level. These cloud top heights AGL are then combined with longitude and latitude to estimate the latitude and longitude corrections. Due to variability in cloud top height, the parallax shifts produce an irregular grid of values since higher cloud tops are shifted further than lower cloud tops. A ball tree-based neighbor search with Haversine distance is performed using the Python-based scikit-learn library to find the nearest VISST grid point to each parallax correction-shifted point. The data value of the shifted point is then assigned to that VISST grid point. In this manner, the irregular geographical shifts to correct for parallax are projected back to the rectilinear VISST grid. Because relatively higher clouds should obscure lower clouds, the variable values for the highest cloud top are preferentially chosen if two or more values are assigned to a grid point. The parallax correction should be viewed as an improved but still imperfect estimation of the cloud top locations, largely because the cloud top height is an imperfect retrieval. Please see the attached README document for further information. Users are encouraged to contact the authors with any additional questions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Vulnerabilities Under the Surface

This paper will describe a practical methodology for understanding the cyber risk of a digital asset. This research attempts to gain a greater understanding of the cyber risk posed by a hardware-based computer asset by considering it as a sum of its hardware and software based sub-components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-CAMPUS-I-05: Internal Dosimetric Calculations for Several Imaging Radiopharmaceuticals in Preclinical Studies and Quantitative Assessment of the Mouse Size Impact On Them. Realistic Monte Carlo Simulations Based On the 4D-MOBY Model

Purpose: Commonly used radiopharmaceuticals were tested to define the most important dosimetric factors in preclinical studies. Dosimetric calculations were applied in two different whole-body mouse models, with varying organ size, so as to determine their impact on absorbed doses and S-values. Organ mass influence was evaluated with computational models and Monte Carlo(MC) simulations. Methods: MC simulations were executed on GATE to determine dose distribution in the 4D digital MOBY mouse phantom. Two mouse models, 28 and 34 g respectively, were constructed based on realistic preclinical exams to calculate the absorbed doses and S-values of five commonly used radionuclides in SPECT/PET studies (18F, 68Ga, 177Lu, 111In and 99mTc).Radionuclide biodistributions were obtained from literature. Realistic statistics (uncertainty lower than 4.5%) were acquired using the standard physical model in Geant4. Comparisons of the dosimetric calculations on the two different phantoms for each radiopharmaceutical are presented. Results: Dose per organ in mGy was calculated for all radiopharmaceuticals. The two models introduced a difference of 0.69% in their brain masses, while the largest differences were observed in the marrow 18.98% and in the thyroid 18.65% masses.Furthermore, S-values of the most important target-organs were calculated for each isotope. Source-organ was selected to be the whole mouse body.Differences on the S-factors were observed in the 6.0–30.0% range. Tables with all the calculations as reference dosimetric data were developed. Conclusion: Accurate dose per organ and the most appropriate S-values are derived for specific preclinical studies. The impact of the mouse model size is rather high (up to 30% for a 17.65% difference in the total mass), and thus accurate definition of the organ mass is a crucial parameter for self-absorbed S values calculation.Our goal is to extent the study for accurate estimations in small animal imaging, whereas it is known that there is a large variety in the anatomy of the organs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Adiabatic quantum support vector machines

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with an increasing number of features and an increasing number of data points in the training dataset. In conclusion, our scalability results show that the quantum approach obtains a 3.5–4.5x speedup over the classical approach on datasets with many (millions of) features.

Computational Complexity↗
DOE OSTI2024

Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin

The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Digital quantum simulation of cavity quantum electrodynamics: insights from superconducting and trapped ion quantum testbeds

We explore the potential for hybrid development of quantum hardware where currently available quantum computers simulate open cavity quantum electrodynamical (CQED) systems for applications in optical quantum communication, simulation and computing. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis–Cummings model containing N atoms coupled to a lossy cavity. We report the results of executing this algorithm on two noisy intermediate-scale quantum computers: a superconducting processor and a trapped ion processor, to simulate the population dynamics of an open CQED system featuring N = 3 atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform, obtaining results which agree closely with the exact solution of the system. These results can be used as a recipe for efficient and platform-specific quantum simulation of cavity–emitter systems on contemporary and future quantum computers.

cavity QED↗
DOE OSTI2017

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7” iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modeling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains dealing with high-dimensional object spaces, such as molecules and complex microstructures. Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop-automated experiment, is emerging as a crucial objective for accelerating scientific research. The critical aspect is to use not only theory but also on-the-fly theory updates during the experiment. Furthermore, we introduce a method for integrating theory into the loop through Bayesian conavigation of theoretical model space and experimentation. Our approach leverages the concurrent development of surrogate models for both simulation and experimental domains at the rates determined by latencies and costs of experiments and computation, alongside the adjustment of control parameters within theoretical models to minimize epistemic uncertainty over the experimental object spaces. This methodology facilitates the creation of digital twins of material structures, encompassing both the surrogate model of behavior that includes the correlative part and the theoretical model itself. While being demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications, such as the exploration of optical properties in nanoclusters, microstructure-dependent properties in complex materials, and properties of molecular systems.

Microscopy↗
DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗