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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Method and apparatus for real time, in situ sensing and characterization of roughness, geometrical shapes, geometrical structures, composition, defects, and temperature in three-dimensional manufacturing systems

Methods and apparatuses for manufacturing are disclosed, including (a) providing an apparatus having: a laser; scanner; powder injection system; powder spreading system; dichroic filter; imager-and-processor; and computer; (b) programming the computer with specifications of a sample; (c) using the computer to set initial parameters based on the sample specifications; (d) adjusting a stage to position the sample; (e) focusing and scanning electromagnetic radiation onto the sample while powder is concurrently injected onto the sample in order to deposit a layer; (f) capturing two-dimensional images of the sample and probing the sample to determine whether the deposited layer was manufactured per the specifications; (g) use the computer to adjust the three-dimensional manufacturing parameters based on the determination made in step (f) prior to additively manufacturing a subsequent layer or making repairs; and (h) repeating steps (d), (e), (f), and (g) until the manufacture is complete. Other embodiments are described and claimed.

Liu, Jian↗
NASA NTRSDate not supplied

Assessing the Effects of Various Surface Textures and Features on Turbulent Heat Transfer in Hypersonic Flight

Experiments were conducted in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility (ballistic range) to quantify the effects on turbulent convective heat transfer of distributed surface roughness, and of isolated features, representative of thermal protection systems on atmospheric entry vehicles. The surface textures and features were applied on the conic frusta of 45o sphere-cone models having a nose-to-base radius ratio of 0.5, similar to the forebody geometry of the Galileo and Pioneer-Venus entry probes. Test conditions were selected to provide turbulent roughness Reynolds numbers, k+, in the ranges expected for outer planet entry missions. Turbulent flow on the conic frustum was achieved by tripping the flow on the sphere-segment nose cap with distributed surface roughness, created by sand-blasting the nose cap. Surface textures included distributed, acreage, roughness, as well as cavity and groove discrete features. The majority of the data to be presented are results for distributed roughness, which includes both random, sand-grain-like roughness, and regular pattern roughness. The pattern roughness was designed to represent the roughness on woven thermal protection system materials, such as NASA’s 3-D Medium Density Carbon Phenolic (3MDCP), also known as HEEET, developed by the Heatshield for Extreme Entry Environments Technology project. The pattern roughness tested is a 3-D wavy surface, and includes three different roughness element height-to-spacing ratios representative of two configurations of 3MDCP, and spanning ratios measured both before, and after, ablation in an arc jet test facility. The patterns tested in the ballistic range were laser-etched on metal models, and represented an idealized version of the real-world materials in which each roughness element was nearly identical. Additional tests were performed wherein the laser-etched patterns were degraded by sand-blasting with various sized grit media, to produce regular patterns with superimposed irregular roughness, more representative of flight materials. Results of each will be compared. The discrete features tested included cylindrical cavities and rectangular grooves of various width-to-depth ratios. Cavities represent either heatshield damage, such as from micro-meteoroid and orbital debris (MMOD) damage, or from designed penetrations, such as on the Genesis sample return capsule. The grooves were scaled representations of seams between segments of HEEET material in a notional tiled thermal protection system. The tests examined the effects on turbulent heating downstream of the isolated features. The tests were conducted at speeds between 3 km/s and 4 km/s in air between 0.15 atm and 0.25 atm (Mach numbers between 9 and 12). Roughness Reynolds numbers, k+, ranged from 12 to 70 for the sand roughness, and as high as 200 for the pattern roughness. Boundary-layer parameters required for calculating k+ were evaluated using computational fluid dynamics simulations using the DPLR (Data Parallel Line Relaxation) code. Each model included both rough- and smooth-wall segments, and heat transfer augmentation factors were determined as the ratio of the rough-wall to smooth-wall heat flux measured on each test.

hypersonic↗
NASA NTRS2024

Aerothermal Analysis of the Rocket Lab Venus Probe Heatshield

This document provides an overview of the aerothermodynamic analyses performed by the Aerothermodynamics Branch at NASA Langley Research Center for the Rocket Lab Venus Probe (RLVP). In addition to defining the baseline heating environment to the heatshield, this document pursues the experimental validation of key physical models at RLVP-relevant conditions. This experimental validation analysis, which captures the model form uncertainty, is used as one of two primary components of the margin assessment, where the other component is the parametric uncertainty. These model form (experimental) and parametric uncertainty components are used to construct a spatial and time varying margin for the heating to the RLVP heatshield. The margin is evaluated as the sum of the parametric and model form uncertainty components. The model form uncertainty is defined as the difference between the RLVP-relevant measurements and their simulations, using the upper limit uncertainty bounds for both the measurements and simulations in the comparisons. The differences in the dominant physics in the stagnation region and flank lead to the separate RLVP-relevant measurements for assessing the model form uncertainty in these two regions. These regions are addressed as follows: Stagnation Region Heating Environment: For the high-temperature stagnation-region, both the radiative heating and impact of blowing on convective heating are significant, while the impacts of turbulence and roughness are negligible. Coupled radiation and ablation LAURA/HARA solutions with ray-tracing provide the radiative heating over the entire vehicle, including the contributions from the Venus atmosphere and ablation species. Non-ablating LAURA simulations provide the convective heating. During the material-response computation typically used for TPS sizing, this non-ablating convective heating is corrected for the impact of ablation using the blowing correction. Coupled ablation LAURA simulations that capture finite-rate sur-face processes show that this blowing correction may be non-conservative over most of the heatshield. This non-conservatism is due to hydrogen recombination in the finite-rate surface model, which tends to increase the coupled ablation convective heating to near the non-ablating values, therefore making any reduction in the non-ablating value through the blowing correction non-conservative. This non-conservatism due to H catalysis is captured in the parametric component of the margin. The best available ground-test measurements that capture the impact of blowing on stagnation region convective heating, at RLVP-relevant conditions, indicate that the current blowing reduction model is non-conservative by up to 20% at RLVP-relevant blowing rates (the coupled blowing simulations were also non-conservative). Because of the relatively low velocity of the ground tests and the non-Venus atmospheric chemistry, these measurements do not capture the chemistry and therefore do not inform the uncertainty due to H catalysis. However, they do capture the fluid mechanics of blowing. The non-conservatism of the blowing correction implied by these measurements is covered by the model form component of the margin, which leads to total margin values over 50%. For the radiative heating, the shock-tube informed bias approach suggests a model form uncertainty of roughly 20%, while the parametric uncertainty analysis suggests values over 100%. The combined stagnation-point radiation margin of over 100% leads to peak margined radiative heating values of over300 W/cm2, which remains small relative to the peak margined convective heating of nearly 2000 W/cm2. Based on this analysis, at the stagnation point, the peak margined heat rate is 2203 W/cm2 and the margined total heat load is 31.5 kJ/cm2 for the current nominal trajectory. Flank Heating Environment: The forebody flank (and near-shoulder) heating environment is dominated by the impact of turbulence, roughness augmentation, and ablation on the convective heating. An extensive collection of ground test measurements with RLVP-relevant turbulence and roughness is studied to show that the maximum difference between the simulated and measured convective heating is 5%. However, with the exception of the Holden measurements from the 1980s, these measurements do not include roughness elements extending into the supersonic region of the boundary layer, which is likely to occur for RLVP (due to the 45 degree sphere-cone geometry). The interaction between the supersonic flow and roughness could cause convective heating augmentation fundamentally different than for locally subsonic flow. Although these Holden measurements are consistent with the other measurements considered, another path was pursued to assure that the rough-ness height extending into supersonic flow does not fundamentally change the roughness augmentation. This additional path was a computational effort to resolve the roughness elements in the CFD grid, so that the interaction be-tween the roughness elements and locally supersonic flow may be simulated in detail. This roughness-resolved CFD simulation is feasible because of the RLVP forebody TPS’s patterned roughness, which may be approximated analytically, and because of the axisymmetric nominal flow field, which allows a narrow surface region to be simulated and therefore make the computational expense feasible. These grid-resolved roughness simulations, which are performed at actual RLVP flight conditions, result in heating augmentation values that are below the design approach for roughness augmentation. This provides evidence that the design approach for RLVP roughness augmentation is sufficient. Based on this analysis, at this flank or near-shoulder location, the peak margined total heat rate is 2088 W/cm2and the margined total heat load is 26.0 kJ/cm2for the current nominal trajectory. Heat flux, shear, pressure and heat transfer coefficient at the RLVP stagnation point and near shoulder location are evaluated for the entire trajectory, and curved fit to a functional form of F=AρB∞UC∞. These simplified relationships for the nominal and margined aerothermal environments are referred to as aerothermal indicators, and presented at the end of this document.

Christopher O Johnston↗
NASA NTRS1960

Investigation by Schlieren Technique of Methods of Fixing Fully Turbulent Flow on Models at Supersonic Speeds

An investigation has been made of the effectiveness of two-dimensional wire and three-dimensional grandular-type roughnesses for fixing transition on a 27 deg cone at a free-stream Mach number of 2.20. One height of each type of roughness was investigated over a free-stream Reynolds number per foot range of about 1.0 x 10 (exp 6) to 7.0 x 10 (exp 6). The tests were made at an angle of attack of 0 deg and with essentially zero heat transfer. The results indicate that, within the Reynolds number per foot range investigated, both two-dimensional wire and three-dimensional granular types of roughness caused large forward movements of transition at constant Reynolds number per foot; however, the forward movement with increase in Reynolds number per foot was very slow for the two-dimensional wire roughness and transition never reached the roughness location as it did with the three-dimensional granular roughness. An extrapolated value of the roughness Reynolds number (based on roughness height and flow conditions at the outer edge of the roughness) for the first appearance of forward movement from the smooth-cone case for the three-dimensional granular roughness was in good agreement with the value of about 600 for the first appearance of turbulent bursts obtained on a similar cone by the hot-wire technique. The range of values of roughness Reynolds number for which a fully turbulent boundary layer existed in the vicinity of the roughness element was about 4,500 to 6,500.

Jackson, Mary W.↗
DOE OSTI2023

Selective laser melting of oxide dispersion strengthened MA956 alloy and its surface finishing by magnetic field assisted finishing

Selective laser melting (SLM) is a prominent metal additive manufacturing (AM) capable of processing a myriad of engineering materials with high precision and design freedom. However, similar to other AM processes, poor surface finishing has been an omnipresent problem in SLM technology. In this study, magnetic field assisted finishing (MAF) was used to finish SLM fabricated oxide dispersion strengthened (ODS) MA956, an iron-chromium-aluminum alloy. The effect of laser processing parameters on part density and surface roughness was first studied. Using a multi-objective optimization technique, the optimal parameters to obtain the highest density and lowest surface roughness were determined. Finally, MAF was applied to the parts built with the obtained optimal SLM parameters, but without significant improvement on the surfaces of as-printed samples. Hence, the surfaces of as-printed samples were post-processed (ground) to yield better initial surface conditions prior to MAF. The effect of initial roughness, iron particles size, and abrasive size on MAF performance was studied. Initial roughness had the most dominant effect followed by abrasive size. The underlying mechanism behind the dependency on initial roughness on final surface quality was analyzed by studying the change on the surface profiles with different starting initial roughness. The initial roughness required for MAF to be effective was determined. In conclusion, using the optimal processing conditions, MAF was applied to the post-processed samples to attain the final average surface roughness (R a ) as little as 0.36 μm starting from initial average roughness (R a ) of 1.53 μm.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Surface scattering effects at different spectral regions

The development of theoretical models of rough surface scattering in different frequency regions is reviewed. The applications of the models to four different types of rough surface scattering are presented. The four categories of surface roughness effects are: (1) where the angular distribution of first-order average incoherent scattered power is proportional to the roughness spectrum of the surface; (2) where the horizontal roughness scale is less than the incident wavelength; (3) where both the horizontal and vertical roughness scales are large compared to the incident wavelength; and (4) scattering in the specular direction is dominated by large-scale roughness, and scattering in the opposite direction is dominated by small-scale roughness. Acoustic, optical and microwave scattering measurements from rough surfaces are presented which illustrate the four types of scattering behavior.

Fung, A. K.↗
NASA NTRS2005

Representation of Vegetation and Other Nonerodible Elements in Aeolian Shear Stress Partitioning Models for Predicting Transport Threshold

The presence of nonerodible elements is well understood to be a reducing factor for soil erosion by wind, but the limits of its protection of the surface and erosion threshold prediction are complicated by the varying geometry, spatial organization, and density of the elements. The predictive capabilities of the most recent models for estimating wind driven particle fluxes are reduced because of the poor representation of the effectiveness of vegetation to reduce wind erosion. Two approaches have been taken to account for roughness effects on sediment transport thresholds. Marticorena and Bergametti (1995) in their dust emission model parameterize the effect of roughness on threshold with the assumption that there is a relationship between roughness density and the aerodynamic roughness length of a surface. Raupach et al. (1993) offer a different approach based on physical modeling of wake development behind individual roughness elements and the partition of the surface stress and the total stress over a roughened surface. A comparison between the models shows the partitioning approach to be a good framework to explain the effect of roughness on entrainment of sediment by wind. Both models provided very good agreement for wind tunnel experiments using solid objects on a nonerodible surface. However, the Marticorena and Bergametti (1995) approach displays a scaling dependency when the difference between the roughness length of the surface and the overall roughness length is too great, while the Raupach et al. (1993) model's predictions perform better owing to the incorporation of the roughness geometry and the alterations to the flow they can cause.

King, James↗
DOE OSTI2023

Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

42 ENGINEERING↗
DOE OSTI2023

Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Local Interpretable and Model-agnostic Explanation (LIME), an explainable artificial intelligence (XAI) approach is adapted to identify the globally important time-frequency bands for predicting average surface roughness (Ra) in a smart grinding process. The smart grinding setup consisted of a Supertech CNC precision surface grinding machine, instrumented with a Dytran piezoelectric accelerometer attached to the tailstock quill along the tangential direction (Y-axis). For every grinding pass, vibration signatures were captured, and the ground truth surface roughness values were recorded using a Mahr Marsurf M300C portable surface roughness profilometer. The roughness values ranged from 0.06 to 0.14 microns over the complete set of experiments. Time-frequency domain spectrogram frames were extracted for each of the vibration signals collected during the grinding process. Convolutional Neural Networks (CNNs) were modeled to predict the surface roughness based on these spectrogram frames and their image augmentations. The best CNN model was able to predict the roughness values with an overall R2-score of 0.95, training R2-score of 0.99, and testing R2-score of 0.81 with only 80 sets of vibration signals corresponding to 4 experiments with 20 trials each. Although the data size is not large enough to guarantee such performance metrics in real-world scenarios, one can extract statistically consistent explanations underlying the relationships these complex deep learning models capture. Further, the LIME methodology was implemented on the developed surface roughness CNN model to identify the important time-frequency bands (i.e., the superpixels of a spectrogram) influencing the predictions. Based on the identified important regions on the spectrogram frames, the corresponding frequency characteristics were determined that influence the surface roughness predictions. The important frequency range based on LIME results was approximately 11.7 to 19.1 kHz. The power of XAI was demonstrated by cutting down the sampling rate from 160 kHz to 30, 20, 10, and 5 kHz based on the important frequency range and considering Nyquist criteria. Separate CNN models were developed for these ranges by only extracting time-frequency contents below their corresponding Nyquist cut-offs. A proper data acquisition strategy is proposed by comparing the model performances to argue the selection of a sufficient sampling rate to capture the grinding process successfully and robustly.

47 OTHER INSTRUMENTATION↗