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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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At least 433 records · Page 24

Analytical and experimental performance of optimal controller designs for a supersonic inlet

The techniques of modern optimal control theory were applied to the design of a control system for a supersonic inlet. The inlet control problem was approached as a linear stochastic optimal control problem using as the performance index the expected frequency of unstarts. The details of the formulation of the stochastic inlet control problem are presented. The computational procedures required to obtain optimal controller designs are discussed, and the analytically predicted performance of controllers designed for several different inlet conditions is tabulated. The experimental implementation of the optimal control laws is described, and the experimental results obtained in a supersonic wind tunnel are presented. The control laws were implemented with analog and digital computers. Comparisons are made between the experimental and analytically predicted performance results. Comparisons are also made between the results obtained with continuous analog computer controllers and discrete digital computer versions.

Zeller, J. R.↗

Predictive Modeling and Operational Monitoring of Starlink Leo Satellite Network Performance in Maritime Environments

This thesis investigates short-term performance prediction for maritime LEO operational planning and application performance cannot anticipate available bandwidth or communication delay, which complicates variation alter signal conditions over short time scales. As a result, maritime users often fluctuations in link quality. In addition, Frequent satellite handoffs and Doppler-induced rapid satellite movement and changing atmospheric conditions cause substantial unavailable. Despite their availability, reliable operation at sea remains difficult because broadband connectivity in maritime regions where terrestrial infrastructure in

97 MATHEMATICS AND COMPUTING↗

Skylab extravehicular mobility unit thermal simulator

The analytical methods, thermal model, and user's instructions for the Skylab Extravehicular Mobility Unit (SEMU) routine are presented. This digital computer program was developed for detailed thermal performance predictions of the SEMU on the NASA-JSC Univac 1108 computer system. It accounts for conductive, convective, and radiant heat transfer as well as fluid flow and special component characterization. The program provides thermal performance predictions for a 967 node thermal model in one thirty-sixth (1/36) of mission time when operated at a calculating interval of three minutes (mission time). The program has the operational flexibility to: (1) accept card or magnetic tape data input for the thermal model describing the SEMU structure, fluid systems, crewman and component performance, (2) accept card and/or magnetic tape input of internally generated heat and heat influx from the space environment, and (3) output tabular or plotted histories of temperature, flow rates, and other parameters describing system operating modes.

Hixon, C. W.↗

High capacity demonstration of honeycomb panel heat pipes

The feasibility of performance enhancing the sandwich panel heat pipe was investigated for moderate temperature range heat rejection radiators on future-high-power spacecraft. The hardware development program consisted of performance prediction modeling, fabrication, ground test, and data correlation. Using available sandwich panel materials, a series of subscale test panels were augumented with high-capacity sideflow and temperature control variable conductance features, and test evaluated for correlation with performance prediction codes. Using the correlated prediction model, a 50-kW full size radiator was defined using methanol working fluid and closely spaced sideflows. A new concept called the hybrid radiator individually optimizes heat pipe components. A 2.44-m long hybrid test vehicle demonstrated proof-of-principle performance.

Tanzer, H. J.↗

Dual nozzle aerodynamic and cooling analysis study

Geometric, aerodynamic flow field, performance prediction, and heat transfer analyses are considered for two advanced chamber nozzle concepts applicable to Earth-to-orbit engine systems. Topics covered include improvements to the dual throat aerodynamic and performance prediction program; geometric and flow field analyses of the dual expander concept; heat transfer analysis of both concepts, and engineering analysis of data from the NASA/MSFC hot-fire testing of a dual throat thruster model thrust chamber assembly. Preliminary results obtained are presented in graphs.

Meagher, G. M.↗

A new method for the prediction of airplane performance

A new method for the prediction of airplane performance in level and climbing flight, together with complete information regarding propeller speeds, is described in this report. Developed from Bairstow's system and making use of American absolute coefficients, this method has advantages of simplicity and brevity.

AIRPLANES - PERFORMANCE PREDICTION↗

Analytical simulation of nonlinear satellite links

It is noted that performance predictions for nonlinear digital satellite channels are usually obtained by either purely analytical methods or Monte Carlo simulations. When used with other than very simple channel models, however, they become extremely costly. It is shown that, through analytical modeling of the effect of the nonlinearity on the uplink waveform, it is possible to obtain approximate performance predictions with a minimum of computer time. It is also demonstrated that the results agree quite well with Monte Carlo simulations and that significant trade-offs can be performed with this tool. An analytical simulation based on Blachman's (1968) definition of signal x signal, signal x noise, and noise x noise terms is shown to provide the required flexibility for modeling the effect of signal distortions on the link performance and to produce good results over a wide range of nonlinearities and channel conditions.

Braun, W. R.↗

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗

Formal optimization of hovering performance using free wake lifting surface theory

Free wake techniques for performance prediction and optimization of hovering rotor are discussed. The influence functions due to vortex ring, vortex cylinder, and source or vortex sheets are presented. The vortex core sizes of rotor wake vortices are calculated and their importance is discussed. Lifting body theory for finite thickness body is developed for pressure calculation, and hence performance prediction of hovering rotors. Numerical optimization technique based on free wake lifting line theory is presented and discussed. It is demonstrated that formal optimization can be used with the implicit and nonlinear objective or cost function such as the performance of hovering rotors as used in this report.

Chung, S. Y.↗

Simulation-based Performance Evaluation of Model Predictive Control for Building Energy Systems

The performance of model predictive control (MPC) can be significantly affected by different choices of controller parameters such as the time intervals for model discretization and control sampling. Due to the lack of a systematic understanding on how these parameters affect control performance, they are usually selected arbitrarily in practice.In this paper, the combined impacts of selected time intervals for model discretization and control sampling on the performance of MPC are comprehensively investigated for the first time through detailed simulations. Specifically, a typical MPC strategy is first designed to improve building operations based on a reduced-order model of building dynamics. Then, the performance of the designed MPC is evaluated against different choices of time intervals for model discretization and control sampling on a simulated office building. The detailed simulation results reveal that the time interval for model discretization has a much greater influence on the performance of MPC than the time interval for control sampling. Although the time interval for control sampling usually receives more attentions in practice, it turns out that the time interval for model discretization affects the prediction performance, cost saving, and computation time simultaneously and more significantly. Therefore, the simulation-based performance evaluation presented here sheds light on the impacts of different time intervals and facilitates their selection for practical applications of MPC to building operations

Huang, Sen↗

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗

Results of a sub-scale model rotor icing test

A heavily instrumented sub-scale model of a helicopter main rotor was tested in the NASA Lewis Research Center Icing Research Tunnel (IRT) in September and November 1989. The four-bladed main rotor had a diameter of 1.83 m (6.00 ft) and the 0.124 m (4.9 in) chord rotor blades were specially fabricated for this experiment. The instrumented rotor was mounted on a Sikorsky Aircraft Powered Force Model, which enclosed a rotor balance and other measurement systems. The model rotor was exposed to a range of icing conditions that included variations in temperature, liquid water content, and median droplet diameter, and was operated over ranges of advance ratio, shaft angle, tip Mach number (rotor speed) and weight coefficient to determine the effect of these parameters on ice accretion. In addition to strain gage and balance data, the test was documented with still, video, and high speed photography, ice profile tracings, and ice molds. The sensitivity of the model rotor to the test parameters, is given, and the result to theoretical predictions are compared. Test data quality was excellent, and ice accretion prediction methods and rotor performance prediction methods (using published icing lift and drag relationships) reproduced the performance trends observed in the test. Adjustments to the correlation coefficients to improve the level of correlation are suggested.

Flemming, Robert J.↗

Results of a sub-scale model rotor icing test

A heavily instrumented sub-scale model of a helicopter main rotor was tested in the NASA Lewis Research Center Icing Research Tunnel (IRT) in September and November 1989. The four-bladed main rotor had a diameter of 1.83 m (6.00 ft) and the 0.124 m (4.9 in) chord rotor blades were specially fabricated for this experiment. The instrumented rotor was mounted on a Sikorsky Aircraft Powered Force Model, which enclosed a rotor balance and other measurement systems. The model rotor was exposed to a range of icing conditions that included variations in temperature, liquid water content, and median droplet diameter, and was operated over ranges of advance ratio, shaft angle, tip Mach number (rotor speed) and weight coefficient to determine the effect of these parameters on ice accretion. In addition to strain gage and balance data, the test was documented with still, video, and high speed photography, ice profile tracings, and ice molds. The sensitivity of the model rotor to the test parameters is given, and the result to theoretical predictions are compared. Test data quality was excellent, and ice accretion prediction methods and rotor performance prediction methods (using published icing lift and drag relationships) reproduced the performance trends observed in the test. Adjustments to the correlation coefficients to improve the level of correlation are suggested.

Flemming, Robert J.↗

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.

Eisenbach, Markus↗

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗

Digital accumulators in phase and frequency tracking loops

Results on the effects of digital accumulators in phase and frequency tracking loops are presented. Digital accumulators or summers are used extensively in digital signal processing to perform averaging or to reduce processing rates to acceptable levels. For tracking the Doppler of high-dynamic targets at low carrier-to-noise ratios, it is shown through simulation and experiment that digital accumulators can contribute an additional loss in operating threshold. This loss was not considered in any previous study and needs to be accounted for in performance prediction analysis. Simulation and measurement results are used to characterize the loss due to the digital summers for three different tracking loops: a digital phase-locked loop, a cross-product automatic frequency tracking loop, and an extended Kalman filter. The tracking algorithms are compared with respect to their frequency error performance and their ability to maintain lock during severe maneuvers at various carrier-to-noise ratios. It is shown that failure to account for the effect of accumulators can result in an inaccurate performance prediction, the extent of which depends highly on the algorithm used.

Hinedi, Sami↗

Design study of a dual-cycle turbofan-ramjet engine for a hypersonic aircraft

Computer modelling was used with two different designs of an advanced turbofan-ramjet in order to derive performance predictions. The engine would enable an aircraft to take-off, accelerate to Mach 5.0, and climb to 90,000 ft. The two concepts included a turbofan with a ramjet annularly wrapped around it and a side-by-side configuration with the ramjet having a rectangular shape and mounted alongside the turbofan. The studies were performed to model weight, length, fuel efficiency, and the requirements of the thrust/drag ratio to exceed unity over the entire flight path. LH2 would be used for fuel and to regeneratively cool the combustion chamber. Turbofan operation with and without afterburner and with and without the ramjet inlet open were examined, as were variable areas for the burners. A side-by-side configuration displayed the best performance predictions, with a ramjet mass flow being 75 percent that of the turbofan and maximum temperatures being equal.

Zumwalt, G. W.↗