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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.

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NASA NTRS2009

A Critical Study of Agglomerated Multigrid Methods for Diffusion

Agglomerated multigrid techniques used in unstructured-grid methods are studied critically for a model problem representative of laminar diffusion in the incompressible limit. The studied target-grid discretizations and discretizations used on agglomerated grids are typical of current node-centered formulations. Agglomerated multigrid convergence rates are presented using a range of two- and three-dimensional randomly perturbed unstructured grids for simple geometries with isotropic and highly stretched grids. Two agglomeration techniques are used within an overall topology-preserving agglomeration framework. The results show that multigrid with an inconsistent coarse-grid scheme using only the edge terms (also referred to in the literature as a thin-layer formulation) provides considerable speedup over single-grid methods but its convergence deteriorates on finer grids. Multigrid with a Galerkin coarse-grid discretization using piecewise-constant prolongation and a heuristic correction factor is slower and also grid-dependent. In contrast, grid-independent convergence rates are demonstrated for multigrid with consistent coarse-grid discretizations. Actual cycle results are verified using quantitative analysis methods in which parts of the cycle are replaced by their idealized counterparts.

Thomas, James L.↗
DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2019

Initial Development of an Improved Creep-Fatigue Design Method that Avoids the Separate Evaluation of Creep and Fatigue Damage and Eliminates the Requirement for Stress Classification

This report describes a method for capturing the effect of strain range, temperature, hold time, and elastic follow up on the cyclic life of a material undergoing uniaxial load. Developing design correlations of this type requires methods for extrapolating experimental data as a function of hold time and follow up factor. Various extrapolation methods are developed and discussed here and recommendations are developed for the final approaches to be adopted for design use. The report also describes comparisons between the new creep-fatigue design method, described here, and current creep-fatigue design methods in the ASME Boiler and Pressure Vessel Code and associated nuclear Code Cases. These comparisons demonstrate several advantages of the new method: it is easier to execute and is less over conservative than many current approaches. This report, along with a companion on extending these results to multiaxial load and on combining load cycles, represents the accumulation of several years of work on the development of this new approach to creep-fatigue design, combining an elastic perfectly plastic analysis method with simplified model tests measuring the effect of elastic follow up on creep-fatigue cyclic life. The conclusions to this report describe a path forward to developing the required design data for high temperature advanced reactor structural materials and guiding the adoption of the new method in the ASME Boiler and Pressure Vessel Code and related standards.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Small-crack effects in high-strength aluminum alloys

The National Aeronautics and Space Administration and the Chinese Aeronautical Establishment participated in a Fatigue and Fracture Mechanics Cooperative Program. The program objectives were to identify and characterize crack initiation and growth of small cracks (10 microns to 2 mm long) in commonly used US and PRC aluminum alloys, to improve fracture mechanics analyses of surface- and corner-crack configurations, and to develop improved life-prediction methods. Fatigue and small-crack tests were performed on single-edgenotch tension (SENT) specimens and large-crack tests were conducted on center-crack tension specimens for constant-amplitude (stress ratios of -1, 0, and 0.5) and Mini-TWIST spectrum loading. The plastic replica method was used to monitor the initiation and growth of small fatigue cracks at the semicircular notch. Crack growth results from each laboratory on 7075-T6 bare and LC9cs clad aluminum alloys agreed well and showed that fatigue life was mostly crack propagation from a material defect (inclusion particles or void) or from the cladding layer. Finite-element and weight-function methods were used to determine stress intensity factors for surface and corner cracks in the SENT specimens. Equations were then developed and used in a crack growth and crack-closure model to correlate small- and large-crack data and to make life predictions for various load histories. The cooperative program produced useful experimental data and efficient analysis methods for improving life predictions. The results should ultimately improve aircraft structural reliability and safety.

Newman, J. C., Jr.↗
NASA NTRS2011

Summary of CPAS Gen II Parachute Analysis

The Orion spacecraft is currently under development by NASA and Lockheed Martin. Like Apollo, Orion will use a series of parachutes to slow its descent and splashdown safely. The Orion parachute system, known as the CEV Parachute Assembly System (CPAS), is being designed by NASA, the Engineering and Science Contract Group (ESCG), and Airborne Systems. The first generation (Gen I) of CPAS testing consisted of thirteen tests and was executed in the 2007-2008 timeframe. The Gen I tests provided an initial understanding of the CPAS parachutes. Knowledge gained from Gen I testing was used to plan the second generation of testing (Gen II). Gen II consisted of six tests: three singleparachute tests, designated as Main Development Tests, and three Cluster Development Tests. Gen II required a more thorough investigation into parachute performance than Gen I. Higher fidelity instrumentation, enhanced analysis methods and tools, and advanced test techniques were developed. The results of the Gen II test series are being incorporated into the CPAS design. Further testing and refinement of the design and model of parachute performance will occur during the upcoming third generation of testing (Gen III). This paper will provide an overview of the developments in CPAS analysis following the end of Gen I, including descriptions of new tools and techniques as well as overviews of the Gen II tests.

Morris, Aaron L.↗
NASA NTRS1994

Experience report: Using formal methods for requirements analysis of critical spacecraft software

Formal specification and analysis of requirements continues to gain support as a method for producing more reliable software. However, the introduction of formal methods to a large software project is difficult, due in part to the unfamiliarity of the specification languages and the lack of graphics. This paper reports results of an investigation into the effectiveness of formal methods as an aid to the requirements analysis of critical, system-level fault-protection software on a spacecraft currently under development. Our experience indicates that formal specification and analysis can enhance the accuracy of the requirements and add assurance prior to design development in this domain. The work described here is part of a larger, NASA-funded research project whose purpose is to use formal-methods techniques to improve the quality of software in space applications. The demonstration project described here is part of the effort to evaluate experimentally the effectiveness of supplementing traditional engineering approaches to requirements specification with the more rigorous specification and analysis available with formal methods.

Lutz, Robyn R.↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Characterization of renal stone composition by using fast kilovoltage switching dual-energy computed tomography compared to laboratory stone analysis: a pilot study

Purpose: To prospectively examine the diagnostic performance of fast kilovoltage switching dual-energy computed tomography (DECT) in characterization of in vivo renal stone composition compared with postoperative stone analysis. Methods: Consecutive consenting patients scheduled for endoscopic kidney stone surgery in a tertiary referral hospital from June 2015 to January 2016 were enrolled. Patients were preoperatively scanned with single-source, fast kilovoltage switching DECT. Stone compositions were determined regarding the effective atomic number measurements. Results of the stone compositions from DECT were compared to postoperative infrared spectroscopy stone analysis as the standard reference. Results: For the 39 patients enrolled in the study, DECT was able to detect uric acid stone with sensitivity of 88.9% and specificity of 100%. There was 100% positive predictive value, 96.8% negative predictive value, and 97.4% accuracy. For non-contrast CT scan, sensitivity was 88.9%, specificity was 96.7%, positive predictive value was 88.9%, negative predictive value was 96.7%, and accuracy was 94.8%. Of the 39 samples examined, 21 (54%) were single composition, whereas 18 (46%) were combined. Single composition stones were correctly characterized by DECT in 100% (8/8) for uric acid. Whereas the result of uric acid stone containing stone discrimination in mixed composition was not so good with Z{sub eff} alone, iodine imaging can compensated this fault. Conclusions: DECT provides excellent accuracy in characterizing uric acid stone compositions. With the addition of iodine image, all of uric acid-containing stones can be determined by the DECT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Sensitive parameter identification and uncertainty quantification for the stability of pipeline conveying fluid

In this study, several uncertainty quantification and sensitivity analysis methods are used to determine the most sensitive geometric and material input parameters of a cantilevered pipeline conveying fluid when uncertainty is introduced to the system at the onset of instability. The full nonlinear equations of motion are modeled using the extended Hamilton’s principle and then discretized using Galerkin’s method. A parametric study is first performed, and the Morris elementary effects are calculated to obtain a preliminary understanding of how the onset speed changes when each parameter is introduced to a ± 5% uncertainty. Then, four different input uncertainty distributions, mainly, uniform and Gaussian distribution, are chosen to investigate how input distributions affect uncertainty in the output. A convergence analysis is used to determine the number of samples needed to maintain simulation accuracy while saving the most computational time. Then, Monte Carlo simulations are run, and the output distributions for each input distribution at ± 1%, ± 3% and ± 5% input uncertainty range are found and discussed. Additionally, the Pearson correlation coefficients are evaluated for different uncertainty ranges. A final Monte Carlo study is performed in which single parameters are held constant while all others still have uncertainty. Overall, the flow speed at the onset of instability is the most sensitive to changes in the outer diameter of the pipe.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Finally, our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid prediction of fuel research octane number and octane sensitivity using the AFIDA constant-volume combustion chamber

Current research octane number (RON) and motor octane number (MON) gasoline performance characterization techniques use dated, complex engine testing methodology and limit researchers’ ability to easily characterize small volumes of experimental fuels. A novel methodology is presented that correlates measured ignition delay (ID) time to RON in an Advanced Fuel Ignition Delay Analyzer (AFIDA) constant-volume combustion chamber device at a single pressure/temperature condition, with an r2 of 0.99 and standard error (SE) of 1.0. The correlation of the slope of the ID time between two additional temperature points to octane sensitivity (S) produces an r2 of 0.97 and SE of 0.69; however, fuels with S>12 are indistinguishable. These results are based on methodology calibration using 31 primary and toluene reference fuels containing 0%-40% ethanol with RON values ranging from 85 to 113. Validation of these methods using a 102-sample fuel matrix spanning an array of base fuels and additive chemistry designed to test the robust applicability of the method, along with pump gasoline and high-octane surrogate blend samples, demonstrates an r2 of 0.94 and SE of 1.3 for the RON correlation over all samples, whereas the equivalent S correlation produces an r2 of 0.78 and SE of 1.2 by excluding two additives, 3-pentanone and diisobutylene, which displayed poor S correlation results. This novel AFIDA analysis method can be performed in 1 h and with 40 mL of fuel, offering significant improvements in time and volume requirements over traditional techniques.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1999

Aeroelastic Analysis of the NASA/ARMY/MIT Active Twist Rotor

Aeroelastic modeling procedures used in the design of a piezoelectric controllable twist helicopter rotor wind tunnel model are described. Two aeroelastic analysis methods developed for active twist rotor studies, and used in the design of the model blade, are described in this paper. The first procedure uses a simple flap-torsion dynamic representation of the active twist blade, and is intended for rapid and efficient control law and design optimization studies. The second technique employs a commercially available comprehensive rotor analysis package, and is used for more detailed analytical studies. Analytical predictions of hovering flight twist actuation frequency responses are presented for both techniques. Forward flight fixed system nP vibration suppression capabilities of the model active twist rotor system are also presented. Frequency responses predicted using both analytical procedures agree qualitatively for all design cases considered, with best correlation for cases where uniform blade properties are assumed.

Wilkie, W. Keats↗
NASA NTRS2011

A Critical Study of Agglomerated Multigrid Methods for Diffusion

Agglomerated multigrid techniques used in unstructured-grid methods are studied critically for a model problem representative of laminar diffusion in the incompressible limit. The studied target-grid discretizations and discretizations used on agglomerated grids are typical of current node-centered formulations. Agglomerated multigrid convergence rates are presented using a range of two- and three-dimensional randomly perturbed unstructured grids for simple geometries with isotropic and stretched grids. Two agglomeration techniques are used within an overall topology-preserving agglomeration framework. The results show that multigrid with an inconsistent coarse-grid scheme using only the edge terms (also referred to in the literature as a thin-layer formulation) provides considerable speedup over single-grid methods but its convergence deteriorates on finer grids. Multigrid with a Galerkin coarse-grid discretization using piecewise-constant prolongation and a heuristic correction factor is slower and also grid-dependent. In contrast, grid-independent convergence rates are demonstrated for multigrid with consistent coarse-grid discretizations. Convergence rates of multigrid cycles are verified with quantitative analysis methods in which parts of the two-grid cycle are replaced by their idealized counterparts.

Nishikawa, Hiroaki↗
NASA NTRS2014

Functional Analysis for an Integrated Capability of Arrival/Departure/Surface Management with Tactical Runway Management

The runway is a critical resource of any air transport system. It is used for arrivals, departures, and for taxiing aircraft and is universally acknowledged as a constraining factor to capacity for both surface and airspace operations. It follows that investigation of the effective use of runways, both in terms of selection and assignment as well as the timing and sequencing of the traffic is paramount to the efficient traffic flows. Both the German Aerospace Center (DLR) and NASA have developed concepts and tools to improve atomic aspects of coordinated arrival/departure/surface management operations and runway configuration management. In December 2012, NASA entered into a Collaborative Agreement with DLR. Four collaborative work areas were identified, one of which is called "Runway Management." As part of collaborative research in the "Runway Management" area, which is conducted with the DLR Institute of Flight Guidance, located in Braunschweig, the goal is to develop an integrated system comprised of the three DLR tools - arrival, departure, and surface management (collectively referred to as A/D/S-MAN) - and NASA's tactical runway configuration management (TRCM) tool. To achieve this goal, it is critical to prepare a concept of operations (ConOps) detailing how the NASA runway management and DLR arrival, departure, and surface management tools will function together to the benefit of each. To assist with the preparation of the ConOps, the integrated NASA and DLR tools are assessed through a functional analysis method described in this report. The report first provides the highlevel operational environments for air traffic management (ATM) in Germany and in the U.S., and the descriptions of the DLR's A/D/S-MAN and NASA's TRCM tools at the level of details necessary to compliment the purpose of the study. Functional analyses of each tool and a completed functional analysis of an integrated system design are presented next in the report. Future efforts to fully develop the ConOps will include: developing scenarios to fully test environmental, procedural, and data availability assumptions; executing the analysis by a walk-through of the integrated system using these scenarios; defining the appropriate role of operators in terms of their monitoring requirements and decision authority; executing the analysis by a walk-through of the integrated system with operator involvement; characterizing the environmental, system data requirements, and operator role assumptions for the ConOps.

Phojanamongkolkij, Nipa↗
DOE OSTI2016

Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwent macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

The γ-ray spectrum of the core of Centaurus A as observed with H.E.S.S. and Fermi-LAT

Centaurus A (Cen A) is the nearest radio galaxy discovered as a very-high-energy (VHE; 100 GeV–100 TeV) γ-ray source by the High Energy Stereoscopic System (H.E.S.S.). It is a faint VHE γ-ray emitter, though its VHE flux exceeds both the extrapolation from early Fermi-LAT observations as well as expectations from a (misaligned) single-zone synchrotron-self Compton (SSC) description. The latter satisfactorily reproduces the emission from Cen A at lower energies up to a few GeV. New observations with H.E.S.S., comparable in exposure time to those previously reported, were performed and eight years of Fermi-LAT data were accumulated to clarify the spectral characteristics of the γ-ray emission from the core of Cen A. The results allow us for the first time to achieve the goal of constructing a representative, contemporaneous γ-ray core spectrum of Cen A over almost five orders of magnitude in energy. Advanced analysis methods, including the template fitting method, allow detection in the VHE range of the core with a statistical significance of 12σ on the basis of 213 hours of total exposure time. The spectrum in the energy range of 250 GeV–6 TeV is compatible with a power-law function with a photon index Γ = 2.52 ± 0.13 stat ± 0.20 sys . An updated Fermi-LAT analysis provides evidence for spectral hardening by ΔΓ ≃ 0.4 ± 0.1 at γ-ray energies above 2.8+1.0-0.6 GeV at a level of 4.0σ. The fact that the spectrum hardens at GeV energies and extends into the VHE regime disfavour a single-zone SSC interpretation for the overall spectral energy distribution (SED) of the core and is suggestive of a new γ-ray emitting component connecting the high-energy emission above the break energy to the one observed at VHE energies. Finally, the absence of significant variability at both GeV and TeV energies does not yet allow disentanglement of the physical nature of this component, though a jet-related origin is possible and a simple two-zone SED model fit is provided to this end.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

The natural flow wing-design concept

A wing-design study was conducted on a 65 degree swept leading-edge delta wing in which the wing geometry was modified to take advantage of the naturally occurring flow that forms over a slender wing in a supersonic flow field. Three-dimensional nonlinear analysis methods were used in the study which was divided into three parts: preliminary design, initial design, and final design. In the preliminary design, the wing planform, the design conditions, and the near-conical wing-design concept were derived, and a baseline standard wing (conventional airfoil distribution) and a baseline near-conical wing were chosen. During the initial analysis, a full-potential flow solver was employed to determine the aerodynamic characteristics of the baseline standard delta wing and to investigate modifications to the airfoil thickness, leading-edge radius, airfoil maximum-thickness position, and wing upper to lower surface asymmetry on the baseline near-conical wing. The final design employed an Euler solver to analyze the best wing configurations found in the initial design and to extend the study of wing asymmetry to develop a more refined wing. Benefits resulting from each modification are discussed, and a final 'natural flow' wing geometry was designed that provides an improvement in aerodynamic performance compared with that of a baseline conventional uncambered wing, linear-theory cambered wing, and near-conical wing.

Wood, Richard M.↗