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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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Trajectory, guidance and environmental parameters and uncertainties effect on design optimization of out-of-orbit Viking soft lander
Twelve aerothermodynamic space technology needs were identified to reduce the design uncertainties in aerodynamic heating and forces experienced by heavy lift launch vehicles, orbit transfer vehicles, and advanced single stage to orbit vehicles for the space transportation system, and for probes, planetary surface landers, and sample return vehicles for solar system exploration vehicles. Research and technology needs identified include: (1) increasing the fluid dynamics capability by at least two orders of magnitude by developing an advanced computer processor for the solution of fluid dynamic problems with improved software; (2) predicting multi-engine base flow fields for launch vehicles; and (3) developing methods to conserve energy in aerothermodynamic ground test facilities.
This paper compares the expected performance of two Nuclear Thermal Propulsion fuel types. High fidelity, fluid/thermal/structural + neutronic simulations help predict the performance of graphite-composite and cermet fuel types from point of departure engine designs from the Nuclear Thermal Propulsion project. Materials and nuclear reactivity issues are reviewed for each fuel type. Thermal/structural simulations predict thermal stresses in the fuel and thermal expansion mis-match stresses in the coatings. Fluid/thermal/structural/neutronic simulations provide predictions for full fuel elements. Although NTP engines will utilize many existing chemical engine components and technologies, nuclear fuel elements are a less developed engine component and introduce design uncertainty. Consequently, these fuel element simulations provide important insights into NTP engine performance.
Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.
A review of the current facility capabilities for testing Thermal Protection Systems and quantifying their entry environments at NASA Ames Research Center is presented based on the expected targets of interest to the Planetary Science and Astrobiology communities. While the operational capabilities of these facilities are generally considered sufficient for supporting future missions to targets of interest, expanded ground test capabilities such as larger sample sizes, flight-relevant gas mixtures, dusty environments, and flight-relevant shear/pressure combinations would reduce future entry vehicle design uncertainties and applied margins. These reduced uncertainties may translate into reduced entry vehicle masses, increased robustness, and decreased operational risks during entry phases for science missions. Expanded ground test capabilities would also offer the ability to study material failure modes in environments even more representative of flight than are currently achievable. A list of desired future test capabilities is presented along with suggestions of possible methods of achieving each. The main recommendation of this paper is the undertaking of a detailed study of the benefits and associated costs of each of these expanded capabilities to determine the best future path.
The process used in the application of aerodynamic uncertainties for the design and verification of the Space Shuttle Orbiter Entry Flight Control System is presented. The uncertainties were used to help set center of gravity, angle of attack and dynamic pressure lateral control divergence parameter as well as C sub n sub beta dynamic were instrumental in setting these placards.
Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.
Effects of mars atmospheric uncertainties on entry vehicle design
Planetary landing vehicle design optimization, considering effects of trajectory, guidance and environmental parameters under uncertainty
Design method including effects of parameter uncertainties in design of linear control systems
A design methodology capable of dealing with nonlinear systems, such as a controlled ecological life support system (CELSS), containing parameter uncertainty is discussed. The methodology was applied to the design of discrete time nonlinear controllers. The nonlinear controllers can be used to control either linear or nonlinear systems. Several controller strategies are presented to illustrate the design procedure.
The standard AP8 and AE8 models for predicting trapped proton and electron environments have been compared with several sets of flight data to evaluate model uncertainties. Model comparisons are made with flux, dose, and activation measurements made on various U.S. low-Earth orbit satellites (APEX, CRRES, DMSP. LDEF, NOAA) and Space Shuttle flights, on Russian satellites (Photon-8, Cosmos-1887, Cosmos-2044), and on the Russian Mir space station. This report gives a summary of the model-data given in a companion report. Results from the model comparisons with flight data show, for example, that the AP8 model underpredicts the trapped proton flux at low altitudes by a factor of about two (independent of proton energy and solar cycle conditions), and that the AE8 model overpredict the flux in the outer electron belt be an order of magnitude or more.
The standard AP8 and AE8 models for predicting trapped proton and electron environments have been compared with several sets of flight data to evaluate model uncertainties. Model comparisons are made with flux, dose, and activation measurements made on various U.S. low-Earth orbit satellites (APEX, CRRES, DMSP, LDEF, NOAA) and Space Shuttle flights, on Russian satellites (Photon-8, Cosmos-1887, Cosmos-2044), and on the Russian Mir Space Station. This report gives a summary of the model-data comparisons-detailed results are given in a companion report. Results from the model comparisons with flic,ht data show, for example, the AP8 model underpredicts the trapped proton flux at low altitudes by a factor of about two (independent of proton energy and solar cycle conditions), and that the AE8 model overpredicts the flux in the outer electron belt by an order of magnitude or more.
In connection with the decision to conduct with the Shuttle an orbital, manned mission on its first launch, certain problems arose related to mission safety, which had to be provided without the benefit of either a graduated flight test program or an initial unmanned flight concept. In an attempt to overcome these problems, the philosophy was adopted to provide a reasonable estimate of maximum possible errors in the preflight predicted aerodynamics. The flight control system (FCS) was to be certified on the basis of the estimated errors prior to STS-1. A set of 'worst case' aerodynamic uncertainties, defined as variations, was developed. As part of the first flight certification, variations, combined with other system uncertainties, were used to 'stress' the FCS through a multitude of simulations. Attention is given to an Orbiter description, the entry mission, the correlation of aerodynamic uncertainties, the application of aerodynamic variation in FCS and trajectory design, and the flight test results.
Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.
A novel approach to uncertainty management which is particularly effective in intelligent design aiding systems for large-scale systems is presented. The use of this approach in the materials handling system design domain is discussed. It is noted that, during any point in the design process, a point value can be obtained for the evaluation of feasible designs; however, the techniques described provide unique solutions for these point values using only the current information about the design environment.
The Gravity Recovery and Climate Experiment (GRACE) primary mission will be performed by making measurements of the inter-satellite range change between two co-planar, low altitude, near-polar orbiting satellites. Understanding the uncertainties in the disturbance environment, particularly the aerodynamic drag and torques, is critical in several mission areas. These include an accurate estimate of the spacecraft orbital lifetime, evaluation of spacecraft attitude control requirements, and estimation of the orbital maintenance maneuver frequency necessitated by differences in the drag forces acting on both satellites. The FREEMOL simulation software has been developed and utilized to analyze and suggest design modifications to the GRACE spacecraft. Aerodynamic accommodation bounding analyses were performed and worst-case envelopes were obtained for the aerodynamic torques and the differential ballistic coefficients between the leading and trailing GRACE spacecraft. These analyses demonstrate how spacecraft aerodynamic design and analysis can benefit from a better understanding of spacecraft surface accommodation properties, and the implications for mission design constraints such as formation spacing control.
This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.