Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “measurements 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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GaN-based W-band receiver chip development for fusion plasma diagnostics

Millimeter-wave diagnostics have proven effective on various magnetic fusion devices worldwide, yet the formidable challenges posed by the harsh environments of future burning plasma devices, characterized by extreme temperatures, pressures, and radiation levels, remain a significant hurdle. To address these challenges, the utilization of wide bandgap Gallium Nitride (GaN)-based millimeter-wave diagnostics is a most promising solution for fusion reactor safety monitoring and control. A noteworthy W-band GaN-based system-on-chip receiver has been the demonstrated by employing HRL T3 40 nm GaN technology. This receiver chip, compactly designed with dimensions of 3 × 5 mm 2 , incorporates essential components such as the 75–110 GHz RF Low-Noise Amplifier (LNA), mixer, Intermediate Frequency (IF) amplifier, and Local Oscillator (LO) chain. This receiver chip will be packaged as a millimeter-wave receiver module and applied on the DIII-D National Fusion Facility, for fusion plasma edge shape monitoring for operational safety and dangerous disruption prediction. The laboratory measurement results have demonstrated suitable performance. Furthermore, this advancement is pivotal for accurate analysis of plasma behavior in the extreme conditions of burning plasma devices, driving progress in fusion research and technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Integrated modeling of alpha particle losses in JET DT plasmas

Alpha particle confinement is crucial for sustaining burning plasmas and designing future reactor concepts. Along with classical/prompt losses, various magnetohydrodynamic instabilities can lead to wave-particle interactions which can transport alpha particles outward from the plasma. This can result in a reduction in plasma heating/performance, and, at worst, damage in-vessel components. Joint European Torus’s recent deuterium–tritium campaigns in 2021–2023 have produced numerous alpha particle loss measurements with its scintillator probe and Faraday cup array fast ion loss detectors as discussed in Bonofiglo et al (2024 Nucl. Fusion 64 096038). This paper will report on integrated energetic particle transport modeling in support of those measurements. The modeling is accomplished with the TRANSP and ORBIT-kick codes with the use of recently developed reduced models which calculate mode structure, amplitude, and the evolving dynamics. When possible, constraints and comparisons to experiment are conducted. Case studies are performed on a variety of magnetohydrodynamic activity, including: fishbones, tearing modes (TMs), and sawtooth crashes. Additionally, a special case of an alpha-driven toroidal Alfvén eigenmode is briefly discussed, where modeling showed marginally weak alpha losses and was unable to support experimental observations. Coupled effects between a TM and toroidal field ripple are presented and were unable to replicate the observations in lost particle pitch but did duplicate the localized flattening of the measured neutron profile. Additional modeling results compare the magnitude of losses and energy/velocity-space sensitivities against experimental observations/measurements for each scenario. This work corroborates numeric alpha transport modeling while also identifying model deficiencies. While this report details alpha transport, it also presents open issues for discussion in assessing the validity of our numerical models towards burning plasmas.

Bonofiglo, Phillip J. [Princeton Plasma Physics La↗
DOE OSTI2025

Direct Experimental Proof of the Principal Role of Reduced High-Mode Hydrodynamic Mix in Recent Ignition Success on NIF

This Letter details the role of reduced hydrodynamic mix in achieving the first fusion plasma to satisfy the Lawson criterion for ignition. Using novel 3D measurements of the temperature and density of the burning deuterium-tritium plasma across a series of experiments, we demonstrate that hydrodynamic mixing from small-scale capsule defects was a key degradation mechanism inhibiting ignition. A series of five layered deuterium-tritium experiments using 1.9 MJ of laser energy demonstrates a 2 × reduction of the thermonuclear yield when a lower quality capsule, typical of preignition experiments, is used. Radiation hydrodynamic calculations consistent with the observed mix profile show that the mixed fuel’s reduced compression and increased radiative loss explain the yield reduction. Furthermore, a hydroscaling argument suggests that a ∼ 3 MJ laser would be needed to reach ignition with such a capsule, well beyond the current capabilities of high energy laser systems. As such, this Letter gives critical insight into the physics of mix in self-heated fusion plasmas and establishes a foundation to evaluate capsule quality needs of future inertial fusion designs that aim at ignition and high gain.

High-energy-density plasmas↗
DOE OSTI2021

Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps

We report that local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Proton radiography inversions with source extraction and comparison to mesh methods

In this article, proton radiography is a central diagnostic technique for measuring electromagnetic (EM) fields in high-energy-density, laser-produced plasmas. In this technique, protons traverse the plasma where they accumulate small EM deflections which lead to variations in the proton fluence pattern on a detector. Path-integrated EM fields can then be extracted from the fluence image through an inversion process. In this work, experiments of laser-driven foils were conducted on the OMEGA laser and magnetic field reconstructions were performed using both “fluence-based” techniques and high-fidelity “mesh-based” methods. We implement nonzero boundary conditions into the inversion and show their importance by comparing against mesh measurements. Good agreement between the methods is found only when nonzero boundary conditions are used. We also introduce an approach to determine the unperturbed proton source profile, which is a required input in fluence reconstruction algorithms. In this approach, a fluence inversion is embedded inside of a mesh region, which provides overconstrained magnetic boundary conditions. A source profile is then iteratively optimized to satisfy the boundary information. This method substantially enhances the accuracy in recovering EM fields. Lastly, we propose a scheme to quantify uncertainty in the final inversion that is introduced through errors in the source retrieval.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Measurement of the 75 As ⁢(𝑛, 2⁢𝑛) cross section at 14.1 MeV at the National Ignition Facility

Here, a new measurement of the 75 As (𝑛, 2⁢𝑛) measurement was performed using the 14.1 MeV neutron pulse produced by fusion experiments at the National Ignition Facility (NIF). The target material consisted of GaAs foils encapsulated in aluminum irradiation containers, along with Au monitor foils, and positioned in the NIF chamber during high neutron yield shots. After irradiation, the GaAs and Au foils were counted with high purity germanium detectors to assess the production of (𝑛, 2⁢𝑛) activation products to determine the flux and the cross section of interest. The measured cross section was 966 ± 71 mb at 14.1 ± 0.37 MeV. This work provides a proof of concept for a platform for performing “ride-along” cross section measurements at NIF to contribute to existing nuclear data as well as measure new cross sections in the future.

and nuclear chemistry↗
NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗
DOE OSTI2024

Eliminating Signal Bias Caused by Vacuum System Backstreaming in the Diagnostic Residual Gas Analyzer of ITER

In fusion neutral gas analysis, such as with the Diagnostic Residual Gas Analyzer (DRGA) for ITER, the primary measurement range of interest comprises the low-amu species (1 to 6), especially deuterium and helium. The challenge in successfully obtaining accurate measurements is two-fold. First, the sensitivity of the method must be sufficient to resolve trace amounts accurately; typically, one percent or less. Second, the gas signal from the fusion processes must be free of bias caused by the latent presence (from system outgassing and/or vacuum backstreaming) of these gases to enable accurate interpretation of the measured signal. This latter criterion can be problematic for the lightest gases since there is a propensity for some fraction of the pumped gas load to undergo a phenomenon known as backstreaming. This behavior is manifested in pumping systems for gas properties related to relative atomic weight (lightest) and size (smallest). Backstreaming results in a significant amount of the pumped gas undertaking a reverse flow and re-entering the measurement region; thus, contaminating the forward, real-time measurement. To fully eliminate this adverse effect, a conductance-limiting device – or orifice – has been installed in the high-vacuum pumping system of the present ITER DRGA prototype. The system was already equipped with a secondary turbomolecular pump (TMP), but with limited effectiveness against backstreaming in the inter-pump volume (IPV). This orifice is placed within the suction inlet coupling of the secondary TMP, which is downstream of the IPV. Its objective is to eliminate the backstreaming phenomenon by increasing the back pressure in the IPV. However, the orifice sizing must take into consideration other factors, such as the diagnostic measurement objectives. For example, in the ITER DRGA, one of the measurement requirements is a dynamic response time of ~1s. Fortunately, an added benefit of the pumping restriction created by the orifice is that the upstream pressure increase is beneficial for the DRGA’s optical gas analysis (OGA) sensors. These sensors are attached to the IPV in the present design. The glow discharges, when used as an OGA light source, will typically have a brighter light emission with increasing plasma cell pressure. In addition to the fusion machine research sector, there are other potential applications of this pumping technique where the monitoring of lighter gas concentrations is essential, such as the photolithography process for the semiconductor fabrication of integrated circuits. This presentation will describe the vacuum system used to demonstrate a process to eliminate backstreaming as well as show test results to verify the accomplishment of this critical objective.

Marcus, Chris↗
DOE OSTI2025

A database and meta-analysis on the performance of exploding pusher implosions conducted at OMEGA

A database of 222 exploding pusher implosions conducted at the OMEGA Laser Facility is presented. The dataset consists of glass-shell capsules filled with varying pressures of D 2 , T 2 , and 3 He, which were imploded using square laser pulses with intensities ranging from 1 to 1 × 10 15 W/cm 2 . The database includes measurements of bang times, ion temperatures, and yields from the DD, D 3 He, and DT fusion reactions. A semi-analytic exploding pusher model is introduced, which effectively captures the observed trends in the data. This model predicts that the measurements scale according to a power-law relation based on the initial capsule and laser conditions. A generalized power-law scaling relation is directly fit to each dataset, providing a useful interpolation of the entire database. Overall, the database provides a valuable resource to estimating bang times, temperatures, and yields for the design of future experiments. Additionally, it provides a diverse set of data for validating more advanced implosion physics models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Assessment of Segmentation-Induced Deviations of Porosity Metrics in Powder Bed Fusion Additively Manufactured Components

Post processing X-ray computational tomography (CT) inspection data for additively manufactured (AM) components can induce deviations in defect quantification, affecting subsequent fatigue and failure predictions. To assess the influence and potential impact of segmentation-induced measurement deviations, this paper applies several segmentation techniques to X-ray CT data for powder bed fusion Ti-6Al-4V specimens exhibiting porosity conditions. X-ray CT reconstructions were segmented with varying techniques including Otsu’s thresholding, random forest, k-nearest neighbors, and the multilayer perceptron. Metrics such as pore size and global porosity were compared for internal validity. Then, top-down X-ray CT measurements of surface-breaking porosity were compared to optical profilometry for cross-validation.

Data fusion↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

42 ENGINEERING↗
NASA NTRS2001

Facility Monitoring: A Qualitative Theory for Sensor Fusion

Data fusion and sensor management approaches have largely been implemented with centralized and hierarchical architectures. Numerical and statistical methods are the most common data fusion methods found in these systems. Given the proliferation and low cost of processing power, there is now an emphasis on designing distributed and decentralized systems. These systems use analytical/quantitative techniques or qualitative reasoning methods for date fusion.Based on other work by the author, a sensor may be treated as a highly autonomous (decentralized) unit. Each highly autonomous sensor (HAS) is capable of extracting qualitative behaviours from its data. For example, it detects spikes, disturbances, noise levels, off-limit excursions, step changes, drift, and other typical measured trends. In this context, this paper describes a distributed sensor fusion paradigm and theory where each sensor in the system is a HAS. Hence, given the reach qualitative information from each HAS, a paradigm and formal definitions are given so that sensors and processes can reason and make decisions at the qualitative level. This approach to sensor fusion makes it possible the implementation of intuitive (effective) methods to monitor, diagnose, and compensate processes/systems and their sensors. This paradigm facilitates a balanced distribution of intelligence (code and/or hardware) to the sensor level, the process/system level, and a higher controller level. The primary application of interest is in intelligent health management of rocket engine test stands.

Figueroa, Fernando↗
DOE OSTI2022

Synthesis and Thermophysical Property Determination of NaCl-PuCl3 Salts

Currently, a knowledge gap exists in the available data and understanding of thermophysical properties relating to fresh fuel salts, especially those containing plutonium. Determination of this data is necessary for the design and construction of test reactors, as well as the licensing of future commercial molten salt reactors. Thermophysical properties such as melting temperature, salt stability, density, and heat capacity were determined on synthesized eutectic NaCl-PuCl3 and a more sodium rich composition containing 25 mol% PuCl3. These measurements document the baseline properties of the salt as a function of temperature for future experiments on irradiated fuel salt which will provide a holistic perspective on the change of thermophysical properties during reactor operations. It was determined that the NaCl-PuCl3 ingot synthesized for this study contained 63.4 mol% NaCl, 36.3 mol% PuCl3, and was 99.7% pure. Upon heating it was shown that the NaCl- PuCl3 eutectic was stable at temperatures of 800°C. The onset of melting occurred at 541°C and the enthalpy of fusion was determined to be 140.7 ± 8.4 J/g. Specific heat capacity measurements showed a slightly decreasing trend with respect to temperature in the liquid phase ranging from 0.67 to 0.57 J/g·K, with an average value of 0.637 ± 0.03 J/g·K (104 ± 5 J/mol·K) between 500 to 720°C. Three independent trials of the molten NaCl-PuCl3 eutectic salt found the density to be ?(T) = 3.8589 – 9.5342·10-4 T(°C), validated between 500 to 800°C. In addition to salt synthesis and experimentally determining thermodynamic properties, Ab Initio molecular dynamic (AIMD) simulations were used to calculate density and heat capacity values.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2012

Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error

The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..

LiDAR↗
DOE OSTI2024

Applicability of alkali beam emission spectroscopy on NSTX-U

Understanding fast pedestal dynamics and turbulent transport in the edge and scrape-off layer (SOL) plasma of spherical tokamaks is crucial for the design and operation of future fusion reactors. The alkali beam emission spectroscopy diagnostic technique offers a means to measure the absolute electron density radial profile and fluctuation amplitude in these regions. Here, in this study, we demonstrate that injecting a sodium neutral beam radially into the plasma and analyzing the light emission from its 3p–3s atomic transition using near-orthogonal viewing angles allows for accurate measurement of the electron density profile and fluctuations in the National Spherical Torus Experiment (NSTX) Upgrade spherical tokamak. Our findings indicate a peak signal-to-noise ratio of 118 in the pedestal and 12 in the SOL under typical NSTX plasma conditions. The spatial resolution for the electron density profile is estimated to be between 2 and 8 mm, while for fluctuation measurements, it ranges from 12 to 15 mm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A cryogenic white light absorption spectroscopy setup with in situ gamma irradiation and thermo-optical annealing for optical fiber radiation-induced attenuation characterization

Silica optical fiber sensors offer a fast, distributed measurement solution in various cryogenic and radiation environments, such as magnets for fusion power. Under these conditions, light-absorbing point defects limit lifetime via radiation-induced attenuation (RIA). To support RIA kinetics prediction, we present an in situ, broadband absorption spectroscopy setup combining gamma irradiation with liquid nitrogen cooling. The apparatus enables continuous monitoring of narrowband RIA levels and the use of secondary optical annealing light sources, alongside broadband spectrum measurements to characterize RIA defects through spectrum decomposition. Initial results confirm the inevitable photobleaching effect of the probe light source, which must be accounted for. In addition, we report RIA kinetics during cycles of gamma irradiation at 77 K and isochronal thermal annealing steps from liquid nitrogen to room temperature, showcasing the setup’s ability to replicate real fiber operation scenarios. These instruments form an ideal platform to further study the kinetics of RIA coupled with thermal and optical annealing.

Absorption spectroscopy↗