Process Optimization of Aerosol Based Printing of Polyimide for Capacitor Application.
Abstract not provided.
Keicher, David M↗
SEARCH · Search NASA
Results for “optimization applications”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Optimizing Dynamic Data-Effect Analysis for Iterative Overdecomposed Applications.
Abstract not provided.
Lifflander, Jonathan Josiah↗
Surrogate Model Application to Actuator-disc Current-energy Converter Optimization.
Abstract not provided.
Olson, Sterling S↗
Development and Application of a Risk Analysis Toolkit for Plant Resources Optimization
This report summarizes the R&D activities of the Risk Informed Asset Management (RIAM) project during fiscal year 2020 (FY20). This project focuses on the development of methods designed to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture) provided system/component health/cost data. This project development lives in cooperation with the Plant Health Management (PHM) project which focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate most relevant sources of risk. This year’s activities for the RIAM project focused on the continuation of schedule optimization algorithms developed in FY19. While in FY19 we focused on both deterministic and stochastic capital budgeting methods, in FY20 we moved forward by implementing two versions of schedule optimization methods. The first one reformulates the capital budgeting problem in a distributionally robust form which allows the user to rely on data directly rather than proposing a distribution from the data itself. The second version reformulates the capital budgeting explicitly using risk measures as variables to maximize/minimize. Lastly, we focused on the development of methods designed to identify the optimal maintenance posture based on the Pareto Frontier analysis. Rather than performing a tradeoff analysis (i.e., identify the absolute best posture), the Pareto Frontier analysis performs a trade space exploration approach (i.e., identify value and costs of several postures and have the analyst perform the task of imposing desired value and cost constraints). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space.
97 MATHEMATICS AND COMPUTING↗
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Optimal Design Formulations for Families of Similar Processes with Applications in Energy Systems
This presentation was delivered at the 2022 INFORMS Annual Meeting.
Stinchfield, Georgia↗
Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) – Capabilities and Applications
Presented at the 2022 Carbon Management Project Review Meeting in Pittsburgh, PA
Deshpande, Anuja↗
Relationship between pore size and reversible and irreversible immobilization of ionic liquid electrolytes in porous carbon under applied electric potential
Transport of electrolytes in nanoporous carbon-based electrodes largely defines the function and performance of energy storage devices. Here, using molecular dynamics simulation and quasielastic neutron scattering, we investigate the microscopic dynamics of a prototypical ionic liquid electrolyte, [emim][Tf 2 N], under applied electric potential in carbon materials with 6.7 nm and 1.5 nm pores. The simulations demonstrate the formation of dense layers of counter-ions near the charged surfaces, which is reversible when the polarity is reversed. In the experiment, the ions immobilized near the surface manifest themselves in the elastic scattering signal. The experimentally observed ion immobilization near the wall is fully reversible as a function of the applied electric potential in the 6.7 nm, but not in the 1.5 nm nanopores. In the latter case, remarkably, the first application of the electric potential leads to apparently irreversible immobilization of cations or anions, depending on the polarity, near the carbon pore walls. This unexpectedly demonstrates that in carbon electrode materials with the small pores, which are optimal for energy storage applications, the polarity of the electrical potential applied for the first time after the introduction of an ionic liquid electrolyte may define the decoration of the small pore walls with ions for prolonged periods of time and possibly for the lifetime of the electrode.
25 ENERGY STORAGE↗
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.↗
Classical Optimizers for Noisy Intermediate-Scale Quantum Devices
We present a collection of optimizers tuned for usage on Noisy Inter-mediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They have further uses in calibration, hyperparameter tuning, machine learning, etc. We employ the VQE algorithm as a case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, our study indicates that in the presence of quantum noise the classical minimizer step is a weak link and a careful choice combined with tuning is required for correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, as well as projecting forward on fault tolerant circuits.
Lavrijsen, Wim↗
Alloy and structural optimization of a directionally solidified lamellar eutectic alloy
Mechanical property characterization tests of a directionally solidified Ni-20 percent Cb-2.5 percent Al-6 percent Cr cellular eutectic turbine blade alloy demonstrated excellent long time creep stability and indicated intermediate temperature transverse tensile ductility and shear strength to be somewhat low for turbine blade applications. Alloy and structural optimization significantly improves these off-axis properties with no loss of longitudinal creep strength or stability. The optimized alloy-structure combination is a carbon modified Ni-20.1 percent Cb-2.5 percent Al-6.0 percent Cr-0.06 percent C composition processed under conditions producing plane front solidification and a fully-lamellar microstructure. With current processing technology, this alloy exhibits a creep-rupture advantage of 39 C over the best available nickel base superalloy, directionally solidified MAR M200+ Hf. While improved by about 20 percent, shear strength of the optimized alloy remains well below typical superalloy values.
Sheffler, K. D.↗
Demodulation System for Fiber Optic Bragg Grating Dynamic Pressure Sensing
Fiber optic Bragg gratings have been used for years to measure quasi-static phenomena. In aircraft engine applications there is a need to measure dynamic signals such as variable pressures. In order to monitor these pressures a detection system with broad dynamic range is needed. This paper describes an interferometric demodulator that was developed and optimized for this particular application. The signal to noise ratio was maximized through temporal coherence analysis. The demodulator was incorporated in a laboratory system that simulates conditions to be measured. Several pressure sensor configurations incorporating a fiber optic Bragg grating were also explored. The results of the experiments are reported in this paper.
Lekki, John D.↗
Asymmetrical Magnet Shape Optimization Based on S-C Mapping for Torque Profile Mitigation in Unidirectional Application of SPMS Machine
Surface-mounted Permanent Magnet Synchronous (SPMS) machines are widely used in applications that require low torque ripple, reduced vibration and acoustic noise. For those applications of SPMSM in which only unidirectional operation is needed, asymmetric design optimization show to have significant advantages over symmetric designs. In this paper, an analytical method based on Schwarz -Christoffel mapping is presented for asymmetrical magnet shape optimization in SPMS machine. In this method, permanent magnet is divided into finite cells so that each cell can be assigned with either air (OFF) or magnet (ON). The Optimization problem is solved using Genetic Algorithms (GAs). In order to remove non-manufacturable shapes, the cluster of materials and cleaning method is used and applied into magnet cells. The analytical method can significantly reduce the computation time used by optimization process as compared with methods based on Finite Element Analysis (FEA). This method is applied to a 12-pole SPMS machine. Validation of the S-C mapping method and final results are carried out using 2D and 3D finite element analysis. Here, the results obtained demonstrate that asymmetrical magnet shape improves cogging torque and torque ripple performance of SPMS machine by a significant margin.
42 ENGINEERING↗
A Time-Regularized Multiple Gravity-Assist Low-Thrust Bounded-Impulse Model for Trajectory Optimization
The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Kepler's equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight-suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Kepler's equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.
Optimization↗
A Time-Regularized, Multiple Gravity-Assist Low-Thrust, Bounded-Impulse Model for Trajectory Optimization
The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Keplers equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Keplers equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.
Optimization↗
Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019
While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.
97 MATHEMATICS AND COMPUTING↗
Performance and energy impact of OpenMP runtime configurations on power constrained systems
Energy consumption of an application has attained the same vitality as execution time in current HPC systems, primarily due to the massive energy consumption of these systems and consequently substantial energy bills. This fact forced software developers as well as hardware vendors to put an extensive focus on energy consumption. This focus becomes even more crucial in power constrained systems, where the amount of power dedicated to applications is limited. In such systems, finding the optimal balance between execution time and energy consumption has a significant impact on utilizing these systems efficiently. A programming model is a necessary layer where optimizations can be applied to find the optimal balance. OpenMP is a standard programming model for intra-node parallelism. In this paper, we give a detailed analysis of how to optimize the execution time and energy consumption of an OpenMP application using the OpenMP runtime environment at different power levels. We studied twenty-eight OpenMP parallel regions from five NAS Parallel Benchmark (NPB) applications. Based on the study, we show that for a given power level, a suitable selection of OpenMP runtime parameters can improve the execution time and energy consumption of a parallel region up to 67% and 72%, respectively. We also show that these fine-grained improvements resulted in up to 26% of execution time and 38% of energy consumption improvement for a given OpenMP application. We finally observed that optimizing the energy consumption behavior alone across all power levels regardless of execution time behavior can lead to an additional energy consumption improvement of up to 55%. However, this improvement comes at the expense of execution time.
97 MATHEMATICS AND COMPUTING↗