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The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.
Heat transfer data measured in gaps typical of those under consideration for joints in space shuttle reusable surface insulation protection systems have been assimilated, analyzed and correlated. The data were obtained in four NASA facilities. Several types of gaps were investigated with emphasis on simple butt joints. Gap widths ranged from 0.07 to 0.7 cm and depths ranged from 1 to 6 cm. Laminar, transitional and turbulent boundary layer flows over the gap opening were investigated. Three-dimensional heating variations were observed within gaps in the absence of external flow pressure gradients. Heat transfer correlation equations were obtained for several of the tests. Thermal protection system performance with and without gaps was compared for a representative shuttle entry trajectory.
This paper presents an overview of the correlation of helicopter rotor performance and loads data from various tests and analyses. Information is included from U.S. Army-sponsored tests conducted by Bell Helicopter Company for free-flight full-scale tests in the NASA-Ames 40 x 80 wind tunnel, one-fifth scale tests in the NASA-Langley Transonic Dynamics Tunnel, and small-scale tests of a rotor in air. These test data are compared with each other, where appropriate, and with calculated results. Typical examples illustrate the state of the art for correlation and indicate anomalies encountered. It is concluded that a procedure using theoretical analyses to aid in interpretation and evaluation of test results is essential to developing a science of correlation.
For abstract, see N75-28103.
For abstract, see N75-28103.
The NASA Shell Buckling Knockdown Factor Project (SBKF) was established in 2007 by the NASA Engineering and Safety Center (NESC) with the primary goal to develop new analysis-based buckling design factors (a.k.a. knockdown factors) and high-fidelity buckling simulations for selected launch-vehicle-like cylindrical shell structures. A series of tests are being conducted on large-scale metallic and composite cylindrical shells in order to provide validation data for these new factors and simulations. However, the validation of these new factors and simulations is quite demanding and requires test data that is commensurate with their fidelity. Traditional instrumentation, such as linear variable displacement transducers (LVDTs) and electrical-resistance strain gages serve a critical role in providing accurate displacement and strain measurements in these tests, but only allow for data to be recorded at a select number of point locations and are not sufficient to provide all the necessary validation data. Advanced measurement technologies can be used effectively to complement traditional instrumentation and gather additional data required to validate these structural simulations. In particular, three-dimensional digital image correlation (DIC) was implemented during SBKF cylinder testing to characterize the full-field displacement and strain behavior. Commercially available VIC-3DTM software and user-written data processing scripts were used to generate valuable data and insight into the complex buckling response of the cylinders that otherwise would be impossible to gather using traditional instrumentation. In addition, the measured data from DIC was used to verify measured test data obtained from other instrumentation, enhance test and analysis correlation, and help identify the root cause of anomalous test results that may have gone unexplained if only traditional instrumentation was used. Selected test results that demonstrate the use of DIC on the SBKF cylinders are presented and a portion of the data processing methods are described.
The real-time array feed compensation system is currently being evaluated at DSS 13. This system recovers signal-to-noise ratio (SNR) loss due to mechanical antenna deformations by using an array of seven Ka-band (33.7-GHz) horns to collect the defocused signal fields. The received signals are downconverted and digitized, in-phase and quadrature samples are generated, and combining weights are applied before the samples are recombined. It is shown that when optimum combining weights are employed, the SNR of the combined signal approaches the sum of the channel SNR's. The optimum combining weights are estimated directly from the signals in each channel by the Real-Time Block 2 (RTB2) correlator; since it was designed for very-long-baseline interferometer (VLBI) applications, it can process broadband signals as well as tones to extract the required weight estimates. The estimation algorithms for the optimum combining weights are described for tones and broadband sources. Data recorded in correlator output files can also be used off-line to estimate combiner performance by estimating the SNR in each channel, which was done for data taken during a Jupiter track at DSS 13.
The effects of rotation on impingement cooling have been experimentally studied under simulated gas turbine operating conditions. A large scale model of a blade is spun in vacuum. External heating is simulated by resistve dissipation in the thin wall of the model, which is impingement cooled. The local internal Nu is measured by an IR radiometric technique with high spatial resolution. At low rotational speeds the data agree well with published measurements taken under stationary conditions. At higher speeds, a decrease in the average Nu by up to 20-30 percent is observed. Severe gradients are generated near the hub region. Empirical correlations, which fit the data well and render it more useful for design purposes, are presented. Measurements and correlations suggest that rotational effects are very important and can cause premature blade failure.
ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.
Heat transfer data measured in gaps representative of those being employed for joints in the space shuttle reusable surface insulation (RSI) thermal protection systems (TPS) were assimilated, analyzed, and correlated. Several types of gap were investigated with emphasis on simple butt joints. Gap widths ranged from 0.0 to 0.76 cm and depths ranged from 1 to 6 cm. Laminar, transitional, and turbulent boundary layer flows over the gap opening were investigated. The angle between gap axis and external flow was varied between 0 and pi/2 radians. The contoured cross section gap performed significantly better than all other wide gaps and slightly better than all other narrow gap geometries. Three dimensional heating variations were observed within gaps in the absence of external flow pressure gradients. Interactions between heating within gaps and heating of adjacent top tile surfaces were observed. Gaps aligned with the flow were observed to promote boundary layer transition. Heat transfer correlation equations were obtained for many of the tests. The TPS thickness requirements with and without gaps were computed for a current shuttle entry trajectory. Experimental data employed in the study are summarized. A description of each test facility, run schedule and test conditions, model descriptive information, and heat flux data are included.
Impact dynamic tests are used in the automobile and aircraft industries to assess survivability of occupants during crash, to assert adequacy of the design, and to gain federal certification. Although there is no substitute for experimental tests, analytical models are often developed and used to study alternate test conditions, to conduct trade-off studies, and to improve designs. To validate results from analytical predictions, test and analysis results must be compared to determine the model adequacy. The mathematical approach evaluated in this paper decomposes observed time responses into dominant deformation shapes and their corresponding contribution to the measured response. To correlate results, orthogonality of test and analysis shapes is used as a criterion. Data from an impact test of a composite fuselage is used and compared to finite element predictions. In this example, the impact response was decomposed into multiple shapes but only two dominant shapes explained over 85% of the measured response
The primary purpose of the study presented in this volume is to present the results and data analysis of in-duct transmission loss measurements. Transmission loss testing was performed on full-scale, 1/2-scale, and 115-scale treatment panel samples. The objective of the study was to compare predicted and measured transmission loss for full-scale and subscale panels in an attempt to evaluate the variations in suppression between full- and subscale panels which were ostensibly of equivalent design. Generally, the results indicated an unsatisfactory agreement between measurement and prediction, even for full-scale. This was attributable to difficulties encountered in obtaining sufficiently accurate test results, even with extraordinary care in calibrating the instrumentation and performing the test. Test difficulties precluded the ability to make measurements at frequencies high enough to be representative of subscale liners. It is concluded that transmission loss measurements without ducts and data acquisition facilities specifically designed to operate with the precision and complexity required for high subscale frequency ranges are inadequate for evaluation of subscale treatment effects.
Throughout the literature authors have consistently discussed the suspicion that regression results were less than satisfactory when the independent variables were correlated. Camm, Gulledge, and Womer, and Womer and Marcotte provide excellent applied examples of these concerns. Many authors have obtained partial solutions for this problem as discussed by Womer and Marcotte and Wonnacott and Wonnacott, which result in generalized least squares algorithms to solve restrictive cases. This paper presents a simple but relatively general multivariate method for obtaining linear least squares coefficients which are free of the statistical distortion created by correlated independent variables.
Results are presented of an analysis which compares the performance predictions of a thermal model of a multi-panel modular radiator system with thermal vacuum test data. Comparisons between measured and predicted individual panel outlet temperatures and pressure drops and system outlet temperatures have been made over the full range of heat loads, environments and plumbing arrangements expected for the shuttle radiators. Both two sided and one sided radiation have been included. The model predictions show excellent agreement with the test data for the maximum design conditions of high load and hot environment. Predictions under minimum design conditions of low load-cold environments indicate good agreement with the measured data, but evaluation of low load predictions should consider the possibility of parallel flow instabilities due to main system freezing. Performance predictions under intermediate conditions in which the majority of the flow is not in either the main or prime system are adequate although model improvements in this area may be desired. The primary modeling objective of providing an analytical technique for performance predictions of a multi-panel radiator system under the design conditions has been met.
Correlations are presented which allow determination of penetration and mixing of multiple cold air jets injected normal to a ducted subsonic heated primary air stream. Correlations were obtained over jet-to-primary stream momentum flux ratios of 6 to 60 for locations from 1 to 30 jet diameters downstream of the injection plane. The range of geometric and operating variables makes the correlations relevant to gas turbine combustors. Correlations were obtained for the mixing efficiency between jets and primary stream using an energy exchange parameter. Also jet centerplane velocity and temperature trajectories were correlated and centerplane dimensionless temperature distributions defined. An assumption of a Gaussian vertical temperature distribution at all stations is shown to result in a reasonable temperature field model. Data are presented which allow comparison of predicted and measured values over the range of conditions specified above.
In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. This study suggests that Cascade Error Projection (CEP) is an implementable learning technique for hardware consideration.
Since the Long Duration Exposure Facility was gravity gradient stabilized and did not rotate, the directional dependence of the flux can be easily distinguished. During the deintegration of LDEF, all impact features larger than 0.5 mm into aluminum were documented for diameters and locations. In addition, all diameters and locations of all impact features larger than 0.3 mm into Scheldahl G411500 thermal control blankets were also documented. This data, along with additional information collected from LDEF materials archived at NASA Johnson Space Center (JSC) on smaller features, will be compared with current meteoroid and debris models. This comparison will provide a validation of the models and will identify discrepancies between the models and the data.