Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data fusion”

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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2014

Simulating x-ray Thomson scattering signals from high-density, millimetre-scale plasmas at the National Ignition Facility

We have developed a model for analysing x-ray Thomson scattering data from high-density, millimetre-scale inhomogeneous plasmas created during ultra-high pressure implosions at the National Ignition Facility in a spherically convergent geometry. The density weighting of the scattered signal and attenuation of the incident and scattered x-rays throughout the target are included using radial profiles of the density, opacity, ionization state, and temperature provided by radiation-hydrodynamics simulations. These simulations show that the scattered signal is strongly weighted toward the bulk of the shocked plasma and the Fermi degenerate material near the ablation front. We show that the scattered signal provides a good representation of the temperature of this highly nonuniform bulk plasma and can be determined to an accuracy of ca. 15% using typical data analysis techniques with simple 0D calculations. On the other hand, the mean ionization of the carbon in the bulk is underestimated. We suggest that this discrepancy is due to the convolution of scattering profiles from different regions of the target. Subsequently, we discuss modifications to the current platform to minimise the impact of inhomogeneities, as well as opacity, and also to enable probing of conditions more strongly weighted toward the compressed core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

In this work, we present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Corrections to official ENDF/B Evaluation Releases for SCALE Nuclear Data

The official ENDF/B nuclear data evaluation releases in the past two decades have incrementally incorporated more detailed information, new nuclide evaluations, and very often have improved the accuracy of radiation transport codes when modeling shielding, fission reactors, criticality benchmarks, and fusion systems. However, like any large collaborative data compilation, these releases have all included small errors. This report documents the small corrections to ENDF/B releases ENDF/B-VII.1, ENDF/B-VIII.0, and ENDF/B-VIII.1 that have been applied during nuclear data processing to produce data libraries for the SCALE code system.

Brown, Jesse M. [Oak Ridge National Laboratory (OR↗
NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A massively parallel and scalable multi-CPU material point method

Harnessing the power of modern multi-GPU architectures, we present a massively parallel simulation system based on the Material Point Method (MPM) for simulating physical behaviors of materials undergoing complex topological changes, self-collision, and large deformations. Our system makes three critical contributions. First, we introduce a new particle data structure that promotes coalesced memory access patterns on the GPU and eliminates the need for complex atomic operations on the memory hierarchy when writing particle data to the grid. Second, we propose a kernel fusion approach using a new Grid-to-Particles-to-Grid (G2P2G) scheme, which efficiently reduces GPU kernel launches, improves latency, and significantly reduces the amount of global memory needed to store particle data. Finally, we introduce optimized algorithmic designs that allow for efficient sparse grids in a shared memory context, enabling us to best utilize modern multi-GPU computational platforms for hybrid Lagrangian-Eulerian computational patterns. We demonstrate the effectiveness of our method with extensive benchmarks, evaluations, and dynamic simulations with elastoplasticity, granular media, and fluid dynamics. In comparisons against an open-source and heavily optimized CPU-based MPM codebase [Fang et al. 2019] on an elastic sphere colliding scene with particle counts ranging from 5 to 40 million, our GPU MPM achieves over 100x per-time-step speedup on a workstation with an Intel 8086K CPU and a single Quadro P6000 GPU, exposing exciting possibilities for future MPM simulations in computer graphics and computational science. Moreover, compared to the state-of-the-art GPU MPM method [Hu et al. 2019a], we not only achieve 2x acceleration on a single GPU but our kernel fusion strategy and Array-of-Structs-of-Array (AoSoA) data structure design also generalizes to multi-GPU systems. Our multi-GPU MPM exhibits near-perfect weak and strong scaling with 4 GPUs, enabling performant and large-scale simulations on a 10243 grid with close to 100 million particles with less than 4 minutes per frame on a single 4-GPU workstation and 134 million particles with less than 1 minute per frame on an 8-GPU workstation.

Wang, Xinlei↗
DOE OSTI2025

Evaluations for medium- and high-mass nuclei for FUSION applications

There is a renewed attention to nuclear fusion as a commercial source of carbon-free energy, however there are many scientific needs that must be addressed to enable the future success of fusion as an economical energy option. Among these is the proper description of the impact of radiation produced in the fusion vessel chamber and all other components of the reactor. In this work we will focus on the nuclear data needs to describe the interaction between primary and secondary neutron radiation and the medium- and high-mass nuclei commonly present in structural (such as stainless steel) and superconducting (e.g., electromagnets) materials.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Iterative sampling of expensive simulations for faster deep surrogate training

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Understanding the role of segmentation on process-structure–property predictions made via machine learning

Here, the present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Kolmogorov flow in two dimensional strongly coupled dusty plasma

Undriven, incompressible Kolmogorov flow in two dimensional doubly periodic strongly coupled dusty plasma is modelled using generalised hydrodynamics, both in linear and nonlinear regime. A complete stability diagram is obtained for low Reynolds numbers R and for a range of viscoelastic relaxation time τ{sub m} [0 < τ{sub m} < 10]. For the system size considered, using a linear stability analysis, similar to Navier Stokes fluid (τ{sub m} = 0), it is found that for Reynolds number beyond a critical R, say R{sub c}, the Kolmogorov flow becomes unstable. Importantly, it is found that R{sub c} is strongly reduced for increasing values of τ{sub m}. A critical τ{sub m}{sup c} is found above which Kolmogorov flow is unconditionally unstable and becomes independent of Reynolds number. For R < R{sub c}, the neutral stability regime found in Navier Stokes fluid (τ{sub m} = 0) is now found to be a damped regime in viscoelastic fluids, thus changing the fundamental nature of transition of Kolmogorov flow as function of Reynolds number R. A new parallelized nonlinear pseudo spectral code has been developed and is benchmarked against eigen values for Kolmogorov flow obtained from linear analysis. Nonlinear states obtained from the pseudo spectral code exhibit cyclicity and pattern formation in vorticity and viscoelastic oscillations in energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Measurement of the shock front velocity produced in a T-tube

A set of shock front velocity measurements is described in this paper. The shock waves were produced in a small electromagnetically driven shock T-tube. Most of the measurements were performed in hydrogen. The shock front velocity measurements in other gases and the velocity of the gas behind the shock front were also analyzed, as well as the velocity dependence on applied input energy. Some measurements with an applied external magnetic field were also performed. The used method of shock front velocity is simple and was shown to be very reliable. Measured values were compared with the calculated ones for the incident and reflected shock waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

DISCOVERY OF A RICH CLUSTER AT z = 1.63 USING THE REST-FRAME 1.6 {mu}m 'STELLAR BUMP SEQUENCE' METHOD

We present a new two-color algorithm, the 'Stellar Bump Sequence' (SBS), that is optimized for robustly identifying candidate high-redshift galaxy clusters in combined wide-field optical and mid-infrared (MIR) data. The SBS algorithm is a fusion of the well-tested cluster red-sequence method of Gladders and Yee with the MIR 3.6 {mu}m-4.5 {mu}m cluster detection method developed by Papovich. As with the cluster red-sequence method, the SBS identifies candidate overdensities within 3.6 {mu}m-4.5 {mu}m color slices, which are the equivalent of a rest-frame 1.6 {mu}m stellar bump 'red-sequence'. In addition to employing the MIR colors of galaxies, the SBS algorithm incorporates an optical/MIR (z'-3.6 {mu}m) color cut. This cut effectively eliminates foreground 0.2 1.0 galaxies and add noise when searching for high-redshift galaxy overdensities. We demonstrate using the z {approx} 1 GCLASS cluster sample that similar to the red sequence, the stellar bump sequence appears to be a ubiquitous feature of high-redshift clusters, and that within that sample the color of the stellar bump sequence increases monotonically with redshift and provides photometric redshifts accurate to {Delta}z = 0.05. We apply the SBS method in the XMM-LSS SWIRE field and show that it robustly recovers the majority of confirmed optical, MIR, and X-ray-selected clusters at z > 1.0 in that field. Lastly, we present confirmation of SpARCS J022427-032354 at z = 1.63, a new cluster detected with the method and confirmed with 12 high-confidence spectroscopic redshifts obtained using FORS2 on the Very Large Telescope. We conclude with a discussion of future prospects for using the algorithm.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Search for dark matter and supersymmetry in the vector boson fusion topology in proton-proton collisions at CMS

A search for pair production of dark matter candidates and supersymmetry (SUSY) production with two jets in vector-boson fusion (VBF) topology is presented using data collected by the Compact Muon Solenoid (CMS) detector in proton-proton collisions at the Large Hadron Collider (LHC). Final states with no leptons are expected in pair production of dark matter particles or scalar quarks in SUSY compressed mass-spectra scenarios. Final states with low-energy leptons are expected in the production of charginos and neutralinos in SUSY compressed mass-spectra scenarios. Results for both zero and two lepton final states at 8 TeV are presented with brief prospects at 13 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Welding Process Development for Spent Nuclear Fuel Canister Repair

The potential for stress corrosion cracking (SCC) of welded stainless-steel interim storage containers for spent nuclear fuel (SNF) has been identified as a high priority data gap. This paper presents a fusion welding process that was developed for SNF canister repair. Submerged arc welding (SAW) was developed to weld 12.7 mm (0.5 in.) thick 304L stainless steel plates to simulate the initial welds on SNF canisters. The SAW procedure was qualified following ASME Boiler and Pressure Vessel Code requirements. During SAW, the welding temperature was recorded at various locations by using thermocouples. After SAW, weld microstructures were characterized, joint mechanical properties were tested, and the maximum tensile residual stress direction was identified. After SAW procedure qualification, artificial cracks were excavated perpendicular to the maximum tensile residual stress direction in the SAW heat affected zone. Machine cold-wire gas tungsten arc welding (CW-GTAW) was developed and used for repair welding at cracked locations.

Tang, Wei↗
DOE OSTI2014

SU-E-J-269: Assessing the Precision of Dose Delivery in CBCT-Guided Stereotactic Body Radiation Therapy for Lung and Soft Tissue Metastatic Lesions

Purpose: Ensuring reproducibility of target localization is critical to accurate stereotactic body radiation treatment (SBRT) for lung and soft tissue metastatic lesions. To characterize interfraction variability in set-up and evaluate PTV margins utilized for SBRT, daily CBCTs were used to calculate delivered target and OAR doses compared to those expected from planning. Methods: CBCT images obtained prior to each fraction of SBRT for a lung and thyroid metastatic lesion were evaluated. The target CTV/ITV and OARs on each of 8 CBCT data sets were contoured. Using MIM fusion software and Pinnacle{sup 3} RTP system, delivered dose distribution was reconstructed on each CBCT, utilizing translational shifts performed prior to treatment. Actual delivered vs. expected doses received by target CTV/ITV and adjacent critical structures were compared to characterize accuracy of pre-treatment translational shifts and PTV margins. Results: The planned CTV/ITV D95% and V100% were 4595cGy and 91.47% for the lung lesion, and 3010cGy and 96.34% for the thyroid lesion. Based on CBCT analysis, actual mean D95% and V100% for lung ITV were 4542±344.4cGy and 91.54±3.45%; actual mean D95% and V100% for thyroid metastasis CTV were 3005±25.98cGy and 95.20±2.522%. For the lung lesion, ipsilateral lung V20, heart V32 (cc) and spinal cord (.03 cc) max were 110.15cc, 3.33cc, and 1680cGy vs. 110.27±14.79cc, 6.74±3.76cc, and 1711±46.56cGy for planned vs. delivered doses, respectively. For the thyroid metastatic lesion, esophagus V18, trachea (.03 cc) max, and spinal cord (.03 cc) max were 0.35cc, 2555cGy, and 850cGy vs. 0.16±0.13cc, 2147±367cGy, and 838±45cGy for planned vs. delivered treatments, respectively. Conclusion: Minimal variability in SBRT target lesion dose delivered based on pre-treatment CBCT-based translational shifts suggests tighter PTV margins may be considered to further decrease dose to surrounding critical structures. Guidelines for optimal target alignment during CBCT-guidance for lung and soft tissue metastatic lesions treated with SBRT are being established.

60 APPLIED LIFE SCIENCES↗