Optimum nuclear rocket start-up to develop full power at exact time with consideration of noise.
Random noise effects and optimal control of nuclear rocket start-up for rapid and precise attainment of full power
RANDOM NOISE↗
SEARCH · Engineering Papers
Results for “optimal start”
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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Optimal Mobile Energy Storage Pre-Placement for Black-Start Restoration.
Abstract not provided.
Yip, Joshua↗
DarkNESS Project Testing
This internship involved the design and assembly of a mechanical system for testing skipper-Charge Couple Devices (CCDs) for the DarkNESS project. Skipper-CCDs are highly-sensitive sensors with ultra-low readout noise that are deployed for direct detection of dark matter. DarkNESS is a CubeSat that aims to use skipper CCDs to search for dark matter decaying into X-rays. Currently, in the testing phase, the design process involved creating 3D models of mechanical adapters to operate a prototype space Multi Chip Module (sMCM) package in existing testing chambers in the CCD lab at Fermilab's IERC. The drawings were sent out to be machined, and while waiting to receive the finished adapters, we assembled a vacuum chamber for skipper-CCD testing and performed initial testing of a single CCD controlled by the space Low Threshold Acquisition (sLTA) board that will be employed on the CubeSat. The results showed that the testing conditions are optimal for data to start being taken and analyzed.
Akouedjinoude, Kodjo↗
Optimal Mobile Energy Storage Pre-Placement for Black-Start Restoration.
Abstract not provided.
Yip, Joshua↗
Navigation path planning for autonomous aircraft - Voronoi diagram approach
The present technique for generating a search graph depicting topologically unique paths around mountain boundaries at constant altitudes involves a description of mountain boundaries as polygons; the search graph is then generated on the basis of a geometric construct. All nodes and arcs of the search graph are guaranteed to lie in free space, thereby ensuring an autonomous aircraft's avoidance of mountain obstacles. The solution path is generated by searching the graph for the optimal path from a start location to a finish location.
Krozel, Jimmy↗
Experiments in Neural-Network Control of a Free-Flying Space Robot
Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.
NEURAL NETS↗
Probing electronic and dielectric properties of ultrathin Ga 2 O 3 /Al 2 O 3 atomic layer stacks made with in vacuo atomic layer deposition
Ultrathin (1–4 nm) films of wide-bandgap semiconductors are important to many applications in microelectronics, and the film properties can be sensitively affected by defects especially at the substrate/film interface. Motivated by this, an in vacuo atomic layer deposition (ALD) was developed for the synthesis of ultrathin films of Ga 2 O 3 /Al 2 O 3 atomic layer stacks (ALSs) on Al electrodes. It is found that the Ga 2 O 3 /Al 2 O 3 ALS can form an interface with the Al electrode with negligible interfacial defects under the optimal ALD condition whether the starting atomic layer is Ga 2 O 3 or Al 2 O 3 . Such an interface is the key to achieving an optimal and tunable electronic structure and dielectric properties in Ga 2 O 3 /Al 2 O 3 ALS ultrathin films. In situ scanning tunneling spectroscopy confirms that the electronic structure of Ga 2 O 3 /Al 2 O 3 ALS can have tunable bandgaps (E g ) between ~2.0 eV for 100% Ga 2 O 3 and ~3.4 eV for 100% Al 2 O 3 . With variable ratios of Ga:Al, the measured E g exhibits significant non-linearity, agreeing with the density functional theory simulation, and tunable carrier concentration. Furthermore, the dielectric constant of ultrathin Ga 2 O 3 /Al 2 O 3 ALS capacitors is tunable through the variation in the ratio of the constituent Ga 2 O 3 and Al 2 O 3 atomic layer numbers from 9.83 for 100% Ga 2 O 3 to 8.28 for 100% Al 2 O 3 . The high ε leads to excellent effective oxide thickness ~1.7–2.1 nm for the ultrathin Ga 2 O 3 /Al 2 O 3 ALS, which is comparable to that of high-K dielectric materials.
36 MATERIALS SCIENCE↗
Fleet Assignment Using Collective Intelligence
Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).
Antoine, Nicolas E.↗
START Analysis for ESAS Capability Needs Prioritization
START is a tool to optimize research and development primarily for NASA missions. It was developed within the Strategic Systems Technology Program Office, a division of the Office of the Chief Technologist at NASA's Jet Propulsion Laboratory. START is capable of quantifying and comparing the risks, costs, and potential returns of technologies that are candidates for funding. START can be enormously helpful both in selecting technologies for development -- within the constraints of budget, schedule, and other resources -- and in monitoring their progress. START's methods are applicable to everything from individual tasks to multiple projects comprising entire programs of investigation. They can address virtually any technology assessment and capability prioritization issue. In this report, START is used to analyze the capability needs using data from NASA's Exploration Systems Architecture Study (ESAS).
optimization↗
When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing
Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.
Aerosol jet printing↗
A trajectory planning scheme for spacecraft in the space station environment
Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is special because the space station will define a multivehicle environment in space. The optimization surface is a complex nonlinear function of the initial conditions of the chase and target crafts. Small permutations in the input conditions can result in abrupt changes to the optimization surface. Since no prior knowledge about the number or location of local minima on the surface is available, the optimization must be capable of functioning on a multimodal surface. It was reported in the literature that the simulated annealing algorithm is more effective on such surfaces than descent techniques using random starting points. The simulated annealing optimization was found to be capable of identifying a minimum fuel, two-burn trajectory subject to four constraints which are integrated into the optimization using a barrier method. The computations required to solve the optimization are fast enough that missions could be planned on board the space station. Potential applications for on board planning of missions are numerous. Future research topics may include optimal planning of multi-waypoint maneuvers using a knowledge base to guide the optimization, and a study aimed at developing robust annealing schedules for potential on board missions.
Soller, Jeffrey Alan↗
GW with hybrid functionals for large molecular systems
A low-cost approach for stochastically sampling static exchange during time-dependent Hartree–Fock-type propagation is presented. This enables the use of an excellent hybrid density functional theory (DFT) starting point for stochastic GW quasiparticle energy calculations. Generalized Kohn–Sham molecular orbitals and energies, rather than those of a local-DFT calculation, are used for building the Green function and effective Coulomb interaction. The use of an optimally tuned hybrid diminishes the starting point dependency in one-shot stochastic GW, effectively avoiding the need for self-consistent GW iterations.
Chemistry↗
PHOENIX Electrostatic Design
PHOENIX (Portable, High-efficiency, Optimal ENergy Imaging X-rays) is a quasi-DC, electrostatic, vacuum-diode designed as a portable x-ray source with national defense and commercial applications. The patent-pending PHOENIX concept combines a megavoltage, Cockroft-Walton voltage multiplier with a Van de-Graaff electrostatic charge-storage dome to create a vacuum-diode suitable for x-ray production. Naturally this structure must minimize internal electric fields to reduce electrical breakdown while simultaneously reducing size and weight to enhance portability. In this paper we describe the optimization process and model results obtained using the COMSOL multi-physics code. We describe three models: a prototype model built to demonstrate the PHOENIX concept as part of Laboratory Directed Research and Development (LDRD) Mission Foundation Research (MFR) Phase-I , a “back-of-the-envelope” design used as a starting point for further COMSOL optimization, and finally, the optimized geometry implemented in the MFR Phase-II. In all cases compromises resulting from cost, schedule, and manufacturing constraints were taken into account as the design progressed.
42 ENGINEERING↗
Optimization studies for the growth of Pb(1-x)Sn(x)Te in space
A study of the effects of convection on the quality of crystals grown by the Bridgman technique has been initiated. This study is to provide a basis for the utilization of the low-gravity environment furnished by the Space Shuttle in the development of crystals with a better quality than obtainable under terrestrial conditions. A series of ground-based studies has been started with the objective to optimize the results of Shuttle experiments. A description is presented of preliminary results of some of these studies. Attention is given to a thermo-solutal convection analysis, the development of a technique for interface demarcation, the growth of Pb(1-x)Sn(x)Te crystals, thermophysical property measurements, and solutal diffusion coefficients.
Crouch, R. K.↗
Cryogenic Tank Structure Sizing With Structural Optimization Method
Structural optimization methods in MSC /NASTRAN are used to size substructures and to reduce the weight of a composite sandwich cryogenic tank for future launch vehicles. Because the feasible design space of this problem is non-convex, many local minima are found. This non-convex problem is investigated in detail by conducting a series of analyses along a design line connecting two feasible designs. Strain constraint violations occur for some design points along the design line. Since MSC/NASTRAN uses gradient-based optimization procedures. it does not guarantee that the lowest weight design can be found. In this study, a simple procedure is introduced to create a new starting point based on design variable values from previous optimization analyses. Optimization analysis using this new starting point can produce a lower weight design. Detailed inputs for setting up the MSC/NASTRAN optimization analysis and final tank design results are presented in this paper. Approaches for obtaining further weight reductions are also discussed.
Wang, J. T.↗
Cryogenic Tank Structure Sizing With Structural Optimization Method
Structural optimization methods in MSC/NASTRAN are used to size substructures and to reduce the weight of a composite sandwich cryogenic tank for future launch vehicles. Because the feasible design space of this problem is non-convex, many local minima are found. This non-convex problem is investigated in detail by conducting a series of' analyses along a design line connecting two feasible designs. Strain constraint violations occur for some design points along the design line. Since MSC/NASTRAN uses gradient based optimization procedures, it does not guarantee that the lowest weight design can be found. In this study, a simple procedure is introduced to create a new starting point based on design variable values from previous optimization analyses. Optimization analysis using this new starting point can produce a lower weight design. Detailed inputs for setting up the MSC/NASTRAN optimization analysis and final tank design results are presented in this paper. Approaches for obtaining further weight reductions are also discussed.
Wang, J. T.↗
On the optimality of the MAP estimation loop for carrier phase tracking BPSK and QPSK signals
Starting with MAP estimation theory as a basis for optimally estimating carrier phase of BPSK and QPSK modulations, it is shown in this paper that the closed loop phase trackers, which are motivated by this approach, are indeed closed loop optimum in the minimum mean-square phase tracking jitter sense. The corresponding squaring loss performance of these so-called MAP estimation loops is compared with that of more practical implementations wherein the hyperbolic tangent nonlinearity is approximated by simpler functions.
Simon, M. K.↗
Mathematical Model of a Regenerative Fuel Cell for System Optimization
This thesis developed a system-level optimization model of a regenerative fuel cell (RFC) system for long-duration, off-world energy storage applications. Prior RFC design studies have typically been limited to reduced parameter sets and simplified constraints due to computational limitations relative to the number of relevant degrees of freedom. As a result, important nonlinear interactions between subsystems have not been fully captured. This work began to address that gap by developing a higher-fidelity, nonlinear optimization framework that incorporates a broader set of design variables and coupled constraints, enabling a multidimensional model that captures the coupled behavior of RFC subsystems and demonstrates the feasibility of applying optimization to such systems. An expanded system-level optimization approach was established that captures interactions between electrochemical performance, structural requirements, and storage design. This enabled a more comprehensive evaluation of trade-offs than conventional formulations. The model integrates four coupled subsystems: a fuel cell, an electrolyzer, reactant gas, and high-pressure storage tanks, and was formulated to accommodate a wide range of mission parameters, including operational time and required output power. It incorporates constraints on available solar array power, reactant mass balance between production and consumption, and pressure-dependent storage requirements. To enable reliable convergence, the optimization problem was reformulated to reduce dimensionality and improve numerical stability, with subsystem models organized for efficient evaluation. Problem dimensionality was reduced by consolidating lower-level design variables into higher-level representative quantities, and subsystem behavior was evaluated within the optimization loop. A multi-start initialization strategy was employed to mitigate sensitivity to local minima and improve solution quality, while nonlinear relationships were solved using robust numerical methods. The results showed that convergence was achieved across a range of required output power values. Specific energy reached a maximum at a critical mission power level, where the electrolyzer power matched the available solar input and operated near its voltage and current density limits. Beyond this point, further increases in required power resulted in less mass-efficient operation, increasing total system mass and reducing overall performance. The developed model represents an advancement in RFC system-level optimization by enabling analysis of a broader and more tightly coupled design space than previous considerations. While convergence behavior and computational cost remain challenges, the methods introduced improve solvability and allow inclusion of additional design variables with minimal loss of physical fidelity. However, the numerical results should not be interpreted as definitive design recommendations, as the model includes simplifying assumptions and omits several higher-order effects. Future work should extend this framework by incorporating additional subsystems and loss mechanisms, such as thermal management, parasitic power consumption, and reactant losses, to improve fidelity and ensure more representative design conclusions.
Electrochemistry↗