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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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CELCAL computer program helps in optimizing grid patterns for different photovoltaic cell geometries and metalization processes. Five different powerloss phenomena associated with front-surface metal grid pattern on photovoltaic cells.
Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to its top-down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug-in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom-up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100-feeder test system demonstrate the efficiency of MILP-based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.
At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.
On December 11, 2013, the International Space Station (ISS) experienced a failure of the External Thermal Control System (ETCS) Loop A Pump Module (PM). To minimize the number of extravehicular activities (EVA) required to replace the PM, jettisoning the faulty pump was evaluated. The objective of this study was to independently evaluate the jettison options considered by the ISS Trajectory Operations Officer (TOPO) and to provide recommendations for safe jettison of the ETCS Loop A PM. The simulation selected to evaluate the TOPO options was the NASA Engineering and Safety Center's (NESC) version of Program to Optimize Simulated Trajectories II (POST2) developed to support another NESC assessment. The objective of the jettison analysis was twofold: (1) to independently verify TOPO posigrade and retrograde jettison results, and (2) to determine jettison guidelines based on additional sensitivity, trade study, and Monte Carlo (MC) analysis that would prevent PM recontact. Recontact in this study designates a propagated PM trajectory that comes within 500 m of the ISS propagated trajectory. An additional simulation using Systems Tool Kit (STK) was run for independent verification of the POST2 simulation results. Ultimately, the ISS Program removed the PM jettison option from consideration. However, prior to the Program decision, the retrograde jettison option remained part of the EVA contingency plan. The jettison analysis presented showed that, in addition to separation velocity/direction and the atmosphere conditions, the key variables in determining the time to recontact the ISS is highly dependent on the ballistic number (BN) difference between the object being jettisoned and the ISS.
The application of the parameter optimization computer program to the design of a rudder coordination system for the F-8 airplane is summarized. The flight condition was Mach 0.56 at 20,000 feet altitude. The system configuration selected consisted of signal paths that fed yaw rate and aileron signals to the rudder. The two signal paths were summed and then modified by a high pass filter to eliminate any steady state bias signal. The input axis of the yaw gyro was perpendicular to the aircraft zero lift line and the trim angle of attack was 7.75 degrees. The parameter optimization was used to design the complete system first, and then a root locus analysis of the individual component effects was made. The results are presented.
The design and development of equipment for flight use in earth-orbital programs, when optimally approached cost effectively, proceed through the following logical progression: (1) bench testing of breadboard designs, (2) the fabrication and evaluation of prototype equipment, (3) redesign to meet flight-imposed requirements, and (4) qualification and testing of a flight-ready system. Each of these steps is intended to produce the basic design information necessary to progress to the next step. The cost of each step is normally substantially less than that of the following step. An evaluation of the cost elements involved in each of the steps and their impact on total program cost are presented. Cost analyses of four leading oxygen recovery subsystems which include two carbon dioxide reduction subsystem, Sabatier and Bosch, and two water electrolysis subsystems, the solid polymer electrolyte and the circulating KOH electrolyte are described.
The overall objective of this three-year grant is to provide NASA Langley's System Analysis Branch with improved affordability tools and methods based on probabilistic cost assessment techniques. In order to accomplish this objective, the Aerospace Systems Design Laboratory (ASDL) needs to pursue more detailed affordability, technology impact, and risk prediction methods and to demonstrate them on variety of advanced commercial transports. The affordability assessment, which is a cornerstone of ASDL methods, relies on the Aircraft Life Cycle Cost Analysis (ALCCA) program originally developed by NASA Ames Research Center and enhanced by ASDL. This grant proposed to improve ALCCA in support of the project objective by updating the research, design, test, and evaluation cost module, as well as the engine development cost module. Investigations into enhancements to ALCCA include improved engine development cost, process based costing, supportability cost, and system reliability with airline loss of revenue for system downtime. A probabilistic, stand-alone version of ALCCA/FLOPS will also be developed under this grant in order to capture the uncertainty involved in technology assessments. FLOPS (FLight Optimization System program) is an aircraft synthesis and sizing code developed by NASA Langley Research Center. This probabilistic version of the coupled program will be used within a Technology Impact Forecasting (TIF) method to determine what types of technologies would have to be infused in a system in order to meet customer requirements. A probabilistic analysis of the CER's (cost estimating relationships) within ALCCA will also be carried out under this contract in order to gain some insight as to the most influential costs and the impact that code fidelity could have on future RDS (Robust Design Simulation) studies.
This paper considers a bi-level real-time algorithmic framework for networked systems, consisting of several local controllers and a central controller. The central controller issues setpoints to the local controllers to optimize their operational objectives while satisfying system-wide constraints. In this context, the paper develops an online algorithm for tracking the optimal solution of the underlying dynamic optimization problem. The design of the algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements (i.e., feedback). Optimality claims are established in terms of the dynamic regret of the algorithm; the latter is a natural performance criterion in nonstationary environments associated with real-time control problems. Finally, the application of the algorithm to real-time control of power setpoints in an electrical grid is illustrated.
Attempt to validate experimentally several intrinsic models for the concept of program locality. The models considered are a two-parameter simple locality model, a simple least recently used stack model, and an independent reference model. The first two models appear to produce good approximations to real world behavior, while the independent reference model, because of its static concept of locality, does very poorly. The working set is shown to be a good estimator of the simple two-parameters model's locality, provided that the locality does not change too rapidly. The working set exactly measures the locality in the case of the least recently used stack model and is thus nearly optimal for programs whose behavior can be closely approximated by this model.
The Earth-to-Orbit Team (ETO) of the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) is considered the pre-eminent go-to group for pre-phase A and phase A concept definition. Over the past several years the ETO team has evaluated thousands of launch vehicle concept variations for a significant number of studies including agency-wide efforts such as the Exploration Systems Architecture Study (ESAS), Constellation, Heavy Lift Launch Vehicle (HLLV), Augustine Report, Heavy Lift Propulsion Technology (HLPT), Human Exploration Framework Team (HEFT), and Space Launch System (SLS). The ACO ETO Team is called upon to address many needs in NASA s design community; some of these are defining extremely large trade-spaces, evaluating advanced technology concepts which have not been addressed by a large majority of the aerospace community, and the rapid turn-around of highly time critical actions. It is the time critical actions, those often limited by schedule or little advanced warning, that have forced the five member ETO team to develop a design process robust enough to handle their current output level in order to meet their customer s needs. Based on the number of vehicle concepts evaluated over the past year this output level averages to four completed vehicle concepts per day. Each of these completed vehicle concepts includes a full mass breakdown of the vehicle to a tertiary level of subsystem components and a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. A structural analysis of the vehicle to determine flight loads based on the trajectory output, material properties, and geometry of the concept is also performed. Due to working in this fast-paced and sometimes rapidly changing environment, the ETO Team has developed a finely tuned process to maximize their delivery capabilities. The objective of this paper is to describe the interfaces between the three disciplines used in the design process: weights and sizing, trajectory, and structural analysis. The tools used to perform such analysis are INtegrated Rocket Sizing (INTROS), Program to Optimize Simulated Trajectories (POST), and Launch Vehicle Analysis (LVA) respectively. The methods each discipline uses to streamline their particular part of the design process will also be discussed.
The Earth-to-Orbit Team (ETO) of the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) is considered the pre-eminent "go-to" group for pre-phase A and phase A concept definition. Over the past several years the ETO team has evaluated thousands of launch vehicle concept variations for a significant number of studies including agency-wide efforts such as the Exploration Systems Architecture Study (ESAS), Constellation, Heavy Lift Launch Vehicle (HLLV), Augustine Report, Heavy Lift Propulsion Technology (HLPT), Human Exploration Framework Team (HEFT), and Space Launch System (SLS). The ACO ETO Team is called upon to address many needs in NASA's design community; some of these are defining extremely large trade-spaces, evaluating advanced technology concepts which have not been addressed by a large majority of the aerospace community, and the rapid turn-around of highly time critical actions. It is the time critical actions, those often limited by schedule or little advanced warning, that have forced the five member ETO team to develop a design process robust enough to handle their current output level in order to meet their customer's needs. Based on the number of vehicle concepts evaluated over the past year this output level averages to four completed vehicle concepts per day. Each of these completed vehicle concepts includes a full mass breakdown of the vehicle to a tertiary level of subsystem components and a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. A structural analysis of the vehicle to determine flight loads based on the trajectory output, material properties, and geometry of the concept is also performed. Due to working in this fast-paced and sometimes rapidly changing environment, the ETO Team has developed a finely tuned process to maximize their delivery capabilities. The objective of this paper is to describe the interfaces between the three disciplines used in the design process: weights and sizing, trajectory, and structural analysis. The tools used to perform such analysis are INtegrated Rocket Sizing (INTROS), Program to Optimize Simulated Trajectories (POST), and Launch Vehicle Analysis (LVA) respectively. The methods each discipline uses to streamline their particular part of the design process will also be discussed.
Quality assurance (QA) of volumetric modulated arc therapy (VMAT) increases the workload significantly. We compared the results from 4 verification methods to establish an efficient VMAT QA. Planning for VMAT treatments was carried out for 40 consecutive patients. Pretreatment verifications were carried out with ion chamber array Physikalish-Technische Werkstätten (PTW729), electronic portal dosimetry (EPID), ion chamber measurements, and independent dose calculation with Diamond program. 2D analyses were made using the gamma analysis (3 mm distance to agreement and 3% dose difference relative to maximum, 10% dose threshold). Average point dose difference calculated by Eclipse relative to ion chamber measurements and Diamond were 0.1%±0.9% and 0.6%±2.2%, respectively. Average pass rate for PTW729 was 99.2%±1.9% and 98.3%±1.3% for EPID. The total required time (linac occupancy time given in parentheses) for each QA method was: PTW729 43.5 minutes (26.5 minutes), EPID 14.5 minutes (2.5 minutes), ion chamber 34.5 minutes (26.5 minutes), and Diamond 12.0 minutes (0 minute). The results were consistent and allowed us to establish an optimized protocol, considering safety and accuracy as well as workload, consisting of 2 verification methods: EPID 2D analysis and independent dose calculation.
A FORTRAN computer code is described that could be used to design and optimize a free-displacer, free-piston Stirling engine similar to the RE-1000 engine made by Sunpower. The code contains options for specifying displacer and power piston motion or for allowing these motions to be calculated by a force balance. The engine load may be a dashpot, inertial compressor, hydraulic pump or linear alternator. Cycle analysis may be done by isothermal analysis or adiabatic analysis. Adiabatic analysis may be done using the Martini moving gas node analysis or the Rios second-order Runge-Kutta analysis. Flow loss and heat loss equations are included. Graphical display of engine motions and pressures and temperatures are included. Programming for optimizing up to 15 independent dimensions is included. Sample performance results are shown for both specified and unconstrained piston motions; these results are shown as generated by each of the two Martini analyses. Two sample optimization searches are shown using specified piston motion isothermal analysis. One is for three adjustable input and one is for four. Also, two optimization searches for calculated piston motion are presented for three and for four adjustable inputs. The effect of leakage is evaluated. Suggestions for further work are given.
In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2 R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2 R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability to dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2 R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.
A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.
This talk will present an overview of Traffic Flow Management (TFM) research at NASA Ames Research Center. Dr. Rios will focus on his work developing a large-scale, parallel approach to solving traffic flow management problems in the national airspace. In support of this talk, Dr. Rios will provide some background on operational aspects of TFM as well a discussion of some of the tools needed to perform such work including a high-fidelity airspace simulator. Current, on-going research related to TFM data services in the national airspace system and general aviation will also be presented.
On-demand transportation services have seen a dramatic rise in the past decade, thanks to technology. Connected and automated vehicle (CAV) technology holds potential for a major transformation in the on-demand mobility services landscape.The timeline for fully automated vehicles (AVs) to reach the critical market share is still uncertain. In the short term, many cities in the United States and abroad are testing low-speed automated electric shuttles (AES) as a shared on-demand mobility service in geo-fenced regions. An automated mobility district (AMD) is a campus-sized implementation of connected/automated vehicle technology to realize the full benefits of a fully electric automated mobility service within a confined region or district. Building on this concept, an AMD toolkit is under development at the National Renewable Energy Laboratory (NREL) to inform a campus-or district-sized implementation in which automated electric shuttles (AES). This research extends the functionality of the AMD toolkit by developing a mathematical program to optimize the operations of an AES fleet in an on-demand service configuration.
We report results of the experimental campaign, which studied the initiation, implosion dynamics, and radiation yield of tungsten wire arrays as a function of the wire number. The wire array dimensions and mass were those of interest for the Z-pinch driven Inertial Confinement Fusion (ICF) program. An optimization study of the x-ray emitted peak power, rise time, and full width at half maximum was effectuated by varying the wire number while keeping the total array mass constant and equal to {approx}5.8 mg. The driver utilized was the {approx}20-MA Z accelerator before refurbishment in its usual short pulse mode of 100 ns. We studied single arrays of 20-mm diameter and 1-cm height. The smaller wire number studied was 30 and the largest 600. It appears that 600 is the highest achievable wire number with present day's technology. Radial and axial diagnostics were utilized including crystal monochromatic x-ray backlighter. An optimum wire number of {approx}375 was observed which was very close to the routinely utilized 300 for the ICF program in Sandia.