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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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Water radiolysis measurement in nuclear reactor tests, discussing experiment design as doubly telescoping sequences of blocks
Liquid metal elevated temperature time dependent corrosion effects on immersed structural materials, discussing blocked two level factorial experiment design for multiply telescoping sequences
Axion dark matter experiment ultra-low noise haloscope technology has enabled the successful completion of two science runs (1A and 1B) that looked for dark matter axions in the 2.66–3.1 μeV mass range with Dine–Fischler–Srednicki–Zhitnisky sensitivity. Therefore, it is the most sensitive axion search experiment to date in this mass range. Here, we discuss the technological advances made in the last several years to achieve this sensitivity, which includes the implementation of components, such as the state-of-the-art quantum-noise-limited amplifiers and a dilution refrigerator. Furthermore, we demonstrate the use of a frequency tunable microstrip superconducting quantum interference device amplifier in run 1A, and a Josephson parametric amplifier in run 1B, along with novel analysis tools that characterize the system noise temperature.
This paper is a study of optimal experiment design applied to the measure of thermal properties in functionally graded materials. As a first step, a material with linearly-varying thermal properties is analyzed, and several different tran- sient experimental designs are discussed. An optimality criterion, based on sen- sitivity coefficients, is used to identify the best experimental design. Simulated experimental results are analyzed to verify that the identified best experiment design has the smallest errors in the estimated parameters. This procedure is general and can be applied to design of experiments for a variety of materials.
This report presents the final design (CED-2) for three additional mixed-spectra configurations for plutonium Thermal/Epithermal eXperiments (TEX) to target the intermediate energy region (IER-553). The baseline cases of IER-184 (PU-MET-MIXED-002 [2]) spanned the entire fission energy spectrum. Case 3, which had a median fission energy (MFE) of approximately 6E-5 MeV and had a fission fraction of about 42% in the intermediate energy range, resulted in a $k_{eff}$ overestimation of 1.1%. Compared to 749 previous ICSBEP plutonium benchmarks, the baseline cases accurately predicted the experiments in the thermal and fast regions where the majority of benchmarks inhabit. The benchmarks in the intermediate energy region to date are sparse and overestimate $k_{eff}$ with an average C/E between 1.02 and 1.03. The additional proposed configurations span the whole of the intermediate energy region. The experimental design utilizes the plutonium/aluminum metal alloy Zero Power Physics Reactor (ZPPR) Plutonium-Aluminum No-Nickel (PANN) plates with varying polyethylene moderator thicknesses to span the intermediate fission energy region. Each of the cases have varying fractions of thermal, intermediate, and fast fissions. The designs were chosen to maximize the intermediate energy fraction. The experiment will take place on the universal critical assembly machine, Planet. The layers will be split as equally as possible between the lower platen and the upper stationary platform of Planet. The upper half of the experimental configuration will also have an upper reflector of polyethylene of specified thicknesses to achieve criticality when the lower platen is raised fully. The previous IER-184 configurations, specifically Case 3, were used to determine the configurations for the additional experiments and neutronics calculations were used to fine-tune the configurations to ensure criticality. The quadrature sum uncertainty in Δ$k_{eff}$ for Case 3 in PU-MET-MIXED-002 was found to be 0.00219. Section 3.8 gives a detailed description of the uncertainties calculated. The additional configurations, which are based directly on Case 3, are expected to have similar uncertainties. However, it is possible to reduce the overall uncertainty of Δ$k_{eff}$ for the additional configurations using the knowledge obtained from the calculations in the benchmark.
Designing a hypersonic vehicle is a complicated process due to the multi-disciplinary synergy that is required. The greatest challenge involves propulsion-airframe integration. In the past, a two-dimensional flowpath was generated based on the engine performance required for a proposed mission. A three-dimensional CAD geometry was produced from the two-dimensional flowpath for aerodynamic analysis, structural design, and packaging. The aerodynamics, engine performance, and mass properties arc inputs to the vehicle performance tool to determine if the mission goals were met. If the mission goals were not met, then a flowpath and vehicle redesign would begin. This design process might have to be performed several times to produce a "closed" vehicle. This paper will describe an attempt to design a hypersonic cruise vehicle propulsion flowpath using a Design of' Experiments method to reduce the resources necessary to produce a conceptual design with fewer iterations of the design cycle. These methods also allow for more flexible mission analysis and incorporation of additional design constraints at any point. A design system was developed using an object-based software package that would quickly generate each flowpath in the study given the values of the geometric independent variables. These flowpath geometries were put into a hypersonic propulsion code and the engine performance was generated. The propulsion results were loaded into statistical software to produce regression equations that were combined with an aerodynamic database to optimize the flowpath at the vehicle performance level. For this example, the design process was executed twice. The first pass was a cursory look at the independent variables selected to determine which variables are the most important and to test all of the inputs to the optimization process. The second cycle is a more in-depth study with more cases and higher order equations representing the design space.
Design of Experiments (DOE) was applied to the LAS geometric parameter study to efficiently identify and rank primary contributors to integrated drag over the vehicles ascent trajectory in an order of magnitude fewer CFD configurations thereby reducing computational resources and solution time. SME s were able to gain a better understanding on the underlying flowphysics of different geometric parameter configurations through the identification of interaction effects. An interaction effect, which describes how the effect of one factor changes with respect to the levels of other factors, is often the key to product optimization. A DOE approach emphasizes a sequential approach to learning through successive experimentation to continuously build on previous knowledge. These studies represent a starting point for expanded experimental activities that will eventually cover the entire design space of the vehicle and flight trajectory.
Design of Experiments (DOE) techniques were applied to the Launch Abort System (LAS) of the NASA Crew Exploration Vehicle (CEV) parametric geometry Computational Fluid Dynamics (CFD) study to efficiently identify and rank the primary contributors to the integrated drag over the vehicles ascent trajectory. Typical approaches to these types of activities involve developing all possible combinations of geometries changing one variable at a time, analyzing them with CFD, and predicting the main effects on an aerodynamic parameter, which in this application is integrated drag. The original plan for the LAS study team was to generate and analyze more than1000 geometry configurations to study 7 geometric parameters. By utilizing DOE techniques the number of geometries was strategically reduced to 84. In addition, critical information on interaction effects among the geometric factors were identified that would not have been possible with the traditional technique. Therefore, the study was performed in less time and provided more information on the geometric main effects and interactions impacting drag generated by the LAS. This paper discusses the methods utilized to develop the experimental design, execution, and data analysis.
Statistical design of experiments (DOE) has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits DOE techniques by accelerating learning, maximizing knowledge, ensuring strategic resource investment, and informing data-driven decisions. In practice, DOE relies on multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to meet challenging research objectives. This presentation shares experiences in the practice of design of experiments at NASA in aeronautics, space exploration, and atmospheric science.
Reaction modeling is a key ingredient in designing experiments and interpreting their results, and is an essential component in the process of evaluating nuclear data and assembling nuclear data libraries used in nuclear technology applications. Typically, experimental data are available only for a handful of reaction channels and theory models are used to fill in the gaps. In addition, theory is often called upon as the arbitrator between discrepant data. Most importantly, theory and modeling are required for an accurate determination of uncertainties in the evaluated data and the correlations between the multiple channels. A fast, accurate, and flexible modeling capability has been developed at LLNL with the code system YAHFC (Yet Another Hauser-Feshbach Code). YAHFC is a Monte Carlo, Hauser-Feshbach code framework, making full use of dynamic memory allocation, derived types, and parallel computing. YAHFC can generate events to simulate experiments and is guiding experiments designed to measure inelastic neutron scattering from actinide targets. YAHFC is also being used to analyze decays from surrogate experiments, thereby enabling the inference of reaction cross sections inaccessible by direct measurement. Finally, by modeling nuclear reactions with constraints from experimental data, YAHFC can deliver complete nuclear data libraries, with evaluated uncertainties, using the modernized Generalized Nuclear Data Structure (GNDS).
A manned space flight experiment (designated experiment T-013) to assess the characteristics of astronaut crew-motion disturbances was conducted on the second manned Skylab mission. A brief description of the experiment hardware utilized is given, and a comprehensive discussion of the experiment data reduction and analysis is presented. Data obtained from a force-measuring system, an astronaut limb-motion measuring system, motion-picture film, and the Skylab attitude and pointing control system is described. Results show that astronaut crew members can produce significant disturbance inputs to a spacecraft's attitude control system. Total forces of up to 400 N were exerted during vigorous soaring activities, whereas ""restrained'' motions by the experiment subject generated total forces of up to 300 N. A discussion of potential applications of the experiment results is given and appendixes provide additional detail with respect to experiment operations and results.
Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.
Rapid modeling and efficient testing methods are important in a number of aerospace applications. In this study efficient testing strategies were evaluated in a wind tunnel test environment and combined to suggest a promising approach for both ground-based and flight-based experiments. Benefits of using Design of Experiment techniques, well established in scientific, military, and manufacturing applications are evaluated in combination with newly developing methods for global nonlinear modeling. The nonlinear modeling methods, referred to as Learn-to-Fly methods, utilize fuzzy logic and multivariate orthogonal function techniques that have been successfully demonstrated in flight test. The blended approach presented has a focus on experiment design and identifies a sequential testing process with clearly defined completion metrics that produce increased testing efficiency.
The Modern Design of Experiments (MDOE) has been applied to wind tunnel testing at NASA Langley Research Center for several years. At Langley, MDOE has proven to be a useful and robust approach to aerodynamic testing that yields significant reductions in the cost and duration of experiments while still providing for the highest quality research results. This paper extends its application to include empty tunnel wall pressure calibrations. These calibrations are performed in support of wall interference corrections. This paper will present the experimental objectives, and the theoretical design process. To validate the tunnel-empty-calibration experiment design, preliminary response surface models calculated from previously acquired data are also presented. Finally, lessons learned and future wall interference applications of MDOE are discussed.
A design of experiments approach has been implemented using computational hypervelocity impact simulations to determine the most effective place to add mass to an existing metallic Thermal Protection System (TPS) to improve hypervelocity impact protection. Simulations were performed using axisymmetric models in CTH, a shock-physics code developed by Sandia National Laboratories, and validated by comparison with existing test data. The axisymmetric models were then used in a statistical sensitivity analysis to determine the influence of five design parameters on degree of hypervelocity particle dispersion. Several damage metrics were identified and evaluated. Damage metrics related to the extent of substructure damage were seen to produce misleading results, however damage metrics related to the degree of dispersion of the hypervelocity particle produced results that corresponded to physical intuition. Based on analysis of variance results it was concluded that the most effective way to increase hypervelocity impact resistance is to increase the thickness of the outer foil layer. Increasing the spacing between the outer surface and the substructure is also very effective at increasing dispersion.
An experiment designed to test the feasibility of using rotating unbalanced-mass (RUM) devices for line and raster scanning gimbaled payloads, while expending very little power is described. The experiment is configured for ground-based testing, but the scan concept is applicable to ground-based, balloon-borne, and space-based payloads, as well as free-flying spacecraft. The servos used in scanning are defined; the electronic hardware is specified; and a computer simulation model of the system is described. Simulation results are presented that predict system performance and verify the servo designs.
An experiment, designed to determine the difference between fields-magnetic and electric-surrounding a uniformly moving charge as contrasted with the fields surrounding an accelerated charge, is presented. A thought experiment is presented to illustrate the process.
In this paper numerical calculations are presented for the steady-periodic temperature in layered materials and functionally-graded materials to simulate photothermal methods for the measurement of thermal properties. No laboratory experiments were performed. The temperature is found from a new Green s function formulation which is particularly well-suited to machine calculation. The simulation method is verified by comparison with literature data for a layered material. The method is applied to a class of two-component functionally-graded materials and results for temperature and sensitivity coefficients are presented. An optimality criterion, based on the sensitivity coefficients, is used for choosing what experimental conditions will be needed for photothermal measurements to determine the spatial distribution of thermal properties. This method for optimal experiment design is completely general and may be applied to any photothermal technique and to any functionally-graded material.