Development of a Cloud-Based Tool for Predicting Thermal Battery Performance: TABS v6
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A method is developed to predict the rotor hover performance. This method solves the compressible mass conservation equation much like current full potential codes and can therefore predict the transonic flows on a rotor. However, the newly developed approach also allows for the free convection of shed vorticity and permits the computation of the entire hover wake system. The method uses a vortex embedding scheme in potential flow and has been implemented in a computer code, HELIX -I. To predict power we implement a simple boundary layer and two different induced-drag integration schemes. The induced-drag is obtained from surface pressure integration and an energy flux integral. Comparisons between computations and experiment show good agreement for the prediction of power polars, surface pressure distribution, and tip vortex geometry.
Predicted bearing heat generation and bearing temperature were verified by experimental data for ball bearings over a range of sizes, shaft speeds, and lubricant flow rates. The computer program Shaberth requires, as input, a factor which describes the air-oil mixture in the bearing cavity for calculation of the ball drag contribution to bearing heat generation. An equation for this lubricant percent volume in the bearing cavity was derived and appears to be valid over the range of test conditions including bearing bore sizes from 35 to 167 mm and shaft speeds from 1.0 to 3.0 million DN.
Caterpillar's approach to applying Thick Thermal Barrier Coatings (TTBC's) to diesel engine combustion chambers has been to use advanced modeling techniques to predict engine conditions and combine this information with fundamental property evaluation of TTBC systems to predict engine performance and TTBC stress states. Engine testing has been used to verify the predicted performance of the TTBC systems and provide information on failure mechanisms. The objective of Caterpillar's subcontract with ORNL is to advance the fundamental understanding of thick thermal barrier coating systems. Previous reviews of thermal barrier coating technology concluded that the current level of understanding of coating system behavior is inadequate and the lack of fundamental understanding may impede the application of TTBC's to diesel engines. Areas of TTBC technology being examined in this program include powder characteristics and chemistry; bond coat composition; coating design, microstructure, and thickness as they affect properties, durability, and reliability; and TTBC 'aging' effects (microstructural and property changes) under diesel engine operating conditions. Methods to evaluate the reliability and durability of TTBC's have been developed that attempt to understand the fundamental strength of TTBC's for particular stress states.
Caterpillar's approach to applying thick thermal barrier coatings (TTBC's) to diesel engine combustion chambers has been to use advanced modeling techniques to predict engine conditions and combine this information with fundamental property evaluation of TTBC systems to predict engine performance and TTBC stress states. Engine testing has been used to verify the predicted performance of the TTBC systems and provide information on failure mechanisms. The objective Caterpillar's program to date has been to advance the fundamental understanding of thick thermal barrier coating systems. Previous reviews of thermal barrier coating technology concluded that the current level of understanding of coating system behavior is inadequate and the lack of fundamental understanding may impeded the application of TTBC's to diesel engines. Areas of TTBC technology being examined in this program include powder characteristics and chemistry; bond coat composition; coating design, microstructure, and thickness as they affect properties, durability, and reliability; and TTBC 'aging' effects (microstructural and property changes) under diesel engine operating conditions. Methods to evaluate the reliability and durability of TTBC's have been developed that attempt to understand the fundamental strength of TTBC's for particular stress states.
The NASA Glenn Research Center’s Power Architecture and Analysis Branch performs spacecraft electrical power system (EPS) performance analysis using the System Power Analysis for Capability Evaluation (SPACE) model. Accurately predicting EPS performance requires a wide range of inputs, including the spacecraft’s position, attitude, solar array temperature, load demand, and data on the EPS itself. By operating as a combined analysis tool with integrated orbit mechanics, attitude, shadowing, thermal, solar array, battery, and electrical component models, SPACE provides flexibility for the analyst to examine various scenarios without a time-consuming iterative process involving separate analysis tools and groups. SPACE also achieves short run times by tailoring the complexity of the included models, providing enough fidelity to ensure accurate EPS performance predictions without being bogged down by unnecessary detail. These advantages that SPACE has over similar power system models has contributed to its successful use on a variety of human spaceflight programs for over three decades, and makes it applicable across the development cycle from early conceptual design up through on-orbit operations.
Work is currently underway at the NASA Lewis Research Center to develop an analytical method for predicting the performance degradation of a helicopter operating in icing conditions. A brief survey is performed of possibilities available to perform such a calculation along with the reasons for choosing the present approach. A complete description of the proposed jobstream is given as well as a discussion of the present state of the development.
Work is currently underway at the NASA Lewis Research Center to develop an analytical method for predicting the performance degradation of a helicopter operating in icing conditions. A brief survey is performed of possibilities available to perform such a calculation along with the reasons for choosing the present approach. A complete description of the proposed jobstream is given as well as a discussion of the present state of the development.
The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.
Robots involved in high value manipulation must be effectively coupled to a human operator either at the work-site or remotely connected via communication links. In order to make use of experimental performance evaluation data, models must be developed. Powerful models of remote manipulation by humans can be used to predict manipulation performance in future systems based on today's laboratory systems. In this paradigm, the models are developed from experimental data, and then used to predict performance in slightly different situations. Second, accurate telemanipulation will allow design of manipulation systems which extend manipulation capability beyond its current bounds.
Analog and digital computer programs for predicting transient performance of inducer driven by hydraulic turbine
Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.
NASA’s Soil Moisture Active Passive (SMAP) Mission is currently flying in a 685 km orbit. Featuring a Synthetic Aperture Radar (SAR) and a radiometer sharing the same antenna, SMAP was developed in collaboration between Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). While the radar requirements on the instrument antenna were more benign from an RF point of view, the radiometer requirement were more difficult to meet because of the stability required by the radiometer to operate to its full potential. The instrument antenna performance was predicted by a very detailed RF model and verified by measuring a 1/10th scale model with great accuracy before launch. Once in orbit, we had the opportunity to measure the antenna performance for both the radiometer and the radar and compare it with the predicted performance given by our RF model. This paper discusses the work done both at JPL and GSFC in order to verify and validate the on orbit performance of the SMAP instrument antenna.
NASA’s Soil Moisture Active Passive (SMAP) Mission is currently flying in a 685 km orbit. Featuring a Synthetic Aperture Radar (SAR) and a radiometer sharing the same antenna, SMAP was developed in collaboration between Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). While the radar requirements on the instrument antenna were more benign from an RF point of view, the radiometer requirement were more difficult to meet because of the stability required by the radiometer to operate to its full potential. The instrument antenna performance was predicted by a very detailed RF model and verified by measuring a 1/10th scale model with great accuracy before launch. Once in orbit, we had the opportunity to measure the antenna performance for both the radiometer and the radar and compare it with the predicted performance given by our RF model. This paper discusses the work done both at JPL and GSFC in order to verify and validate the on orbit performance of the SMAP instrument antenna.
Here, this article presents a regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative uncertainties. Under a finite support assumption, the problem can be cast as a finite-dimensional quadratic program, but the problem becomes quickly intractable as the problem size grows exponentially in the horizon length. SMPC aims to compute approximate solutions by solving a sequence of problems with truncated prediction horizons and committing the solution in a receding-horizon fashion. Although this approach is widely used in practice, its performance relative to the optimal solution is not well understood. This article reports for the first time a rigorous near-optimal performance guarantee of SMPC: under stabilizability and detectability conditions, the regret of SMPC is exponentially small in the prediction horizon length, allowing SMPC to achieve near-optimal performance at a substantially reduced computational expense.
There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.
As part of a contract with the NASA Lewis Research Center, a simple, accurate method of predicting the performance characteristics of a nozzle design has been developed for use in conceptual design studies. The Nozzle Performance Analysis Code (NPAC) can predict the on- and off-design performance of axisymmetric or two-dimensional convergent and convergent-divergent nozzle geometries. NPAC accounts for the effects of overexpansion or underexpansion, flow divergence, wall friction, heat transfer, and small mass addition or loss across surfaces when the nozzle gross thrust and gross thrust coefficient are being computed. NPAC can be used to predict the performance of a given nozzle design or to develop a preliminary nozzle system design for subsequent analysis. The input required by NPAC consists of a simple geometry definition of the nozzle surfaces, the location of key nozzle stations (entrance, throat, exit), and the nozzle entrance flow properties. NPAC performs three analysis "passes" on the nozzle geometry. First, an isentropic control volume analysis is performed to determine the gross thrust and gross thrust coefficient of the nozzle. During the second analysis pass, the skin friction and heat transfer losses are computed. The third analysis pass couples the effects of wall shear and heat transfer with the initial internal nozzle flow solutions to produce a system of equations that is solved at steps along the nozzle geometry. Small mass additions or losses, such as those resulting from leakage or bleed flow, can be included in the model at specified geometric sections. A final correction is made to account for divergence losses that are incurred if the nozzle exit flow is not purely axial.
NASA’s X-57 “Maxwell” distributed electric propulsion flight demonstrator has a high-lift system that includes 12 fixed-pitch high-lift propellers located upstream of the wing leading edge for lift augmentation at low speeds. These high-lift propellers are not required at higher speeds and are folded conformally along the nacelles to reduce drag when not in operation. Aircraft performance models and flight simulations incorporate propeller performance to predict thrust, moments, and power consumption and propeller model accuracy is important in identifying safe operating regimes for this aircraft. Current high-lift propeller performance models have been verified and calibrated against numerous computational fluid dynamics analyses under a variety of flight conditions. We performed a series of full-scale wind tunnel tests at the NASA Langley Research Center Low Speed Aeroacoustic Wind Tunnel to further validate these models, to identify any adverse operating conditions for these propellers, and to assess the accuracy of the facility’s new Propeller Test Stand. The results indicate that the models accurately predicted performance and that the right-handed propeller showed lower torque and thrust for a given propeller speed compared to the left-handed version. Acoustic data support these measurements, showing higher tonal noise for the left versus right propeller. We suspect this is due to slight differences in the material properties between the early production (right) and more recent (left) blade sets leading to greater detwisting in the right-handed blades under load. Both propellers demonstrated very stable operation throughout the test including during deployment, stowing, and windmilling. Furthermore, high-speed imagery of the spinning propeller indicated no blade oscillations or instabilities for any test case. We also leveraged these images to analyze the effects of propeller loading on blade detwisting and tip position. Unfortunately, the thrust measurements varied significantly during the test due to cross-loading of the Propeller Test Stand load cell, though trends in the thrust data were still observable. The torque measurement, however, remained very precise for all test points and was used for performance comparisons. As a result of this test, we validated and improved the X-57 high-lift propeller performance models, obtained detailed propeller acoustic data and high-speed imagery, observed the propeller operating safely during a variety of dynamic events, and identified two potentially adverse, low-propeller-speed conditions which will be avoided during normal aircraft operation.