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Results for “Realizability Analysis”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Assessment of current state of the art in modeling techniques and analysis methods for large space structures

Advances in continuum modeling, progress in reduction methods, and analysis and modeling needs for large space structures are covered with specific attention given to repetitive lattice trusses. As far as continuum modeling is concerned, an effective and verified analysis capability exists for linear thermoelastic stress, birfurcation buckling, and free vibration problems of repetitive lattices. However, application of continuum modeling to nonlinear analysis needs more development. Reduction methods are very effective for bifurcation buckling and static (steady-state) nonlinear analysis. However, more work is needed to realize their full potential for nonlinear dynamic and time-dependent problems. As far as analysis and modeling needs are concerned, three areas are identified: loads determination, modeling and nonclassical behavior characteristics, and computational algorithms. The impact of new advances in computer hardware, software, integrated analysis, CAD/CAM stems, and materials technology is also discussed.

Noor, A. K.↗
DOE OSTI2015

Group‐sparsity regularization for ill‐posed subsurface flow inverse problems

Abstract Sparse representations provide a flexible and parsimonious description of high‐dimensional model parameters for reconstructing subsurface flow property distributions from limited data. To further constrain ill‐posed inverse problems, group‐sparsity regularization can take advantage of possible relations among the entries of unknown sparse parameters when: (i) groups of sparse elements are either collectively active or inactive and (ii) only a small subset of the groups is needed to approximate the parameters of interest. Since subsurface properties exhibit strong spatial connectivity patterns they may lead to sparse descriptions that satisfy the above conditions. When these conditions are established, a group‐sparsity regularization can be invoked to facilitate the solution of the resulting inverse problem by promoting sparsity across the groups. The proposed regularization penalizes the number of groups that are active without promoting sparsity within each group. Two implementations are presented in this paper: one based on the multiresolution tree structure of Wavelet decomposition, without a need for explicit prior models, and another learned from explicit prior model realizations using sparse principal component analysis (SPCA). In each case, the approach first classifies the parameters of the inverse problem into groups with specific connectivity features, and then takes advantage of the grouped structure to recover the relevant patterns in the solution from the flow data. Several numerical experiments are presented to demonstrate the advantages of additional constraining power of group‐sparsity in solving ill‐posed subsurface model calibration problems.

Golmohammadi, Azarang↗
DOE OSTI2021

Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind

Turbine and plant upsizing are major trends in offshore wind deployment, although the quantitative impact on project costs has not been well-characterized. The uncertain value of continued wind turbine and project growth limits the ability of the supply chain to prepare for future technology trends, leading to challenges in the realization of larger projects. This analysis explores the levelized cost of energy impacts of turbine ratings between 6 and 20 MW and plant capacities between 250 and 2,500 MW for fixed-bottom offshore wind using techno-economic cost models for foundation, electrical, installation, and operation and maintenance costs, along with annual energy production. We consider a nominal set of technology assumptions for all scenarios to isolate economies of size and scale without additional benefits from decreasing turbine capital costs, quantity discounts for larger projects, or optimized technology solutions. These results indicate that using a 20-MW wind turbine in a 2,500-MW power plant array can reduce the levelized cost of energy by over 23% relative to the global average turbine and plant size installed in 2019; primarily because of reductions in the balance-of-system and operation and maintenance costs. We also identify improved installation vessels, optimized export systems, and novel operation and maintenance strategies as additional cost reduction opportunities. These results suggest that upsizing represents a significant cost reduction opportunity for offshore wind energy and will continue to be a main factor in shaping the future of the sector.

17 WIND ENERGY↗
DOE OSTI2023

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2011

Long-Working-Distance Kirkpatrick-Baez Mirrors for Hard X-ray Beamlines at SPring-8

We designed and installed two types of long-working-distance Kirkpatrick-Baez (KB) mirrors and mirror manipulators, which were customized into each experiment for hard x-ray undulator beamlines at SPring-8. For the BL32XU RIKEN Targeted Proteins beamline, 400-mm-long KB focusing mirrors for a beam size of 1 {mu}m with a 730-mm-long working distance were designed for carrying out the structural analysis of protein microcrystals. We realized a focusing beam size of 0.9x0.9 {mu}m{sup 2}(FWHM) and a focusing intensity of 6x10{sup 10} (photons/s) at an x-ray energy of 12.4 keV. For the BL19LXU RIKEN SR Physics beamline, we developed KB mirrors for 100-nm focusing with a 100-mm-working distance for the purpose of nano-focus x-ray diffraction. A focusing beam size of 100x100 nm{sup 2}(FWHM) and a high focusing intensity of 3.7x10{sup 10} (photons/s) at an x-ray energy of 12.4 keV were realized.

43 PARTICLE ACCELERATORS↗
NASA NTRS1977

Monte Carlo simulation of wave sensing with a short pulse radar

A Monte Carlo simulation is used to study the ocean wave sensing potential of a radar which scatters short pulses at small off-nadir angles. In the simulation, realizations of a random surface are created commensurate with an assigned probability density and power spectrum. Then the signal scattered back to the radar is computed for each realization using a physical optics analysis which takes wavefront curvature and finite radar-to-surface distance into account. In the case of a Pierson-Moskowitz spectrum and a normally distributed surface, reasonable assumptions for a fully developed sea, it has been found that the cumulative distribution of time intervals between peaks in the scattered power provides a measure of surface roughness. This observation is supported by experiments.

Levine, D. M.↗
NASA NTRS1978

Design of a microprocessor based packet switch

The design of a packet switch incorporating a minicomputer to control and process data packets in a communications system has been reported. This paper proposes a design which uses a 6500 series microprocessor to realize the functions of a message switch in order to evaluate the viability of microprocessors in this application. The proposed design realizes the functions of header analysis, packet security, error control and line prioritization. The system hardware and software are presented together with a brief description of operation.

Carville, D. E.↗
NASA NTRS1985

An Overview of Latest Model Reduction and Control Methods of Large Flexible Space Structures

The latest trends and theoretical developments involved with the modeling and control of Large Flexible Space Structures (LFSS) are described. The paper addresses first the basic problems, characteristics, and difficulties inherent in modeling and control of LFSS. Major sources of difficulties and errors are the stiffness and damping operators of the dynamic model. Extensions of Linear Quadratic Gaussian (LQG) theory as applied to LFSS are presented, including frequency-shaped cost functionals and perturbation methods. The minimum data/maximum entropy approach which uses a stochastic design model to overcome difficulties found in the LQG-based methods is described. Latest trends in system theory including balanced realization and singular-value analysis are used to determine reduced order controllers and models. Ad hoc methods such as component cost analysis and modal cost analysis are discussed in context with the closed-loop reduction problem of controller order versus performance. The minimum data/maximum entropy approach also addresses controller order versus performance. Those areas of control science and large scale systems that appear to have an important role in understanding and solving LFSS modeling and control are also identified.

Santiago, J. M.↗
NASA NTRS2017

Aerodynamically-Actuated Radical Shape-Change Concept

Aerodynamically-actuated radical shape change (AARSC) is a novel concept that enables flight vehicles to conduct a mission profile containing radically different flight regimes while possibly mitigating the typical penalties incurred by radical geometric change. Weight penalties are mitigated by utilizing a primary flight control to generate aerodynamic loads that then drive a shape-change actuation. The flight mission profile used to analyze the AARSC concept is that of a transport aircraft that cruises at a lower altitude than typical transports. Based upon a preliminary analysis, substantial fuel savings are realized for mission ranges below 2000 NM by comparison to a state-of-the-art baseline, with an increasing impact as mission range is reduced. The predicted savings are so significant at short-haul ranges that the shape-change concept rivals the fuel-burn performance of turboprop aircraft while completing missions in less time than typical jet aircraft. Lower-altitude cruise has also been sought after in recent years for environmental benefits, however, the performance penalty to conventional aircraft was prohibitive. AARSC may enable the opportunity to realize the environmental benefits of lower-altitude emissions coupled with mission fuel savings. The findings of this study also reveal that the AARSC concept appears to be controllable, turbulence susceptibility is likely not an issue, and the shape change concept appears to be mechanically and aerodynamically feasible.

Ivanco, Thomas G.↗
NASA NTRS2011

Early Oscillation Detection for Hybrid DC/DC Converter Fault Diagnosis

This paper describes a novel fault detection technique for hybrid DC/DC converter oscillation diagnosis. The technique is based on principles of feedback control loop oscillation and RF signal modulations, and Is realized by using signal spectral analysis. Real-circuit simulation and analytical study reveal critical factors of the oscillation and indicate significant correlations between the spectral analysis method and the gain/phase margin method. A stability diagnosis index (SDI) is developed as a quantitative measure to accurately assign a degree of stability to the DC/DC converter. This technique Is capable of detecting oscillation at an early stage without interfering with DC/DC converter's normal operation and without limitations of probing to the converter.

Wang, Bright L.↗
DOE OSTI2018

Hardware Design of a 1.7 kV SiC MOSFET Based MMC for Medium Voltage Motor Drives

Abstract—This paper presents the hardware design of a 7 kV dc bus,1 MVA, 0-1,000 Hz, 1.7 kV SiC MOSFET based medium voltage variable speed drive (MVVSD)1. The system is based on a 7-level three-phase modular multilevel converter (MMC). The full in-depth design, development, and testing of this prototype are provided, including MMC submodule development, arm inductor design, control hardware realization, system integration and thermal analysis. Finally, experimental results demonstrate the excellent performance of the 1.7 kV SiC MOSFET based submodule and the 1 MW, 7 kV MVVSD as a unity in various operation modes

SiC MOSFET, Modular Multilevel Converter, Medium V↗
DOE OSTI2013

Spent nuclear fuel reprocessing modeling

The long-term wide development of nuclear power requires new approaches towards the realization of nuclear fuel cycle, namely, closed nuclear fuel cycle (CNFC) with respect to fission materials. Plant nuclear fuel cycle (PNFC), which is in fact the reprocessing of spent nuclear fuel unloaded from the reactor and the production of new nuclear fuel (NF) at the same place together with reactor plant, can be one variant of CNFC. Developing and projecting of PNFC is a complicated high-technology innovative process that requires modern information support. One of the components of this information support is developed by the authors. This component is the programme conducting calculations for various variants of process flow sheets for reprocessing SNF and production of NF. Central in this programme is the blocks library, where the blocks contain mathematical description of separate processes and operations. The calculating programme itself has such a structure that one can configure the complex of blocks and correlations between blocks, appropriate for any given flow sheet. For the ready sequence of operations balance calculations are made of all flows, i.e. expenses, element and substance makeup, heat emission and radiation rate are determined. The programme is open and the block library can be updated. This means that more complicated and detailed models of technological processes will be added to the library basing on the results of testing processes using real equipment, in test operating mode. The development of the model for the realization of technical-economic analysis of various variants of technologic PNFC schemes and the organization of 'operator's advisor' is expected. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

On the Speed-up of Wave-like Dark Matter Searches with Entangled Qubits

Qubit-based sensing platforms offer promising new directions for wave-like dark matter searches. Recent proposals demonstrate that entangled qubits can achieve quadratic scaling of the signal in the number of qubits. In this work we expand on these proposals to analyze the bandwidth and scan rate performance of entangled qubit protocols across different error regimes. We find that the phase-based readout of entangled protocols preserves the search bandwidth independent of qubit number, in contrast to power-based detection schemes, thereby achieving a genuine scan-rate advantage. We derive coherence time and error rate requirements for qubit systems to realize this advantage. Applying our analysis to dark photon searches, we find that entangled states of approximately 100 qubits can become competitive with benchmark photon-counting cavity experiments for masses $\gtrsim 30{-}40~μ{\rm eV}$, provided sufficiently low error rates are achieved. The advantage increases at higher masses where cavity volume scaling becomes less favorable.

Bodas, Arushi [Fermilab; Chicago U.] (ORCID:000000↗
NASA NTRS2023

Implementation, Realization and an Effective Solver of Two-Equation Turbulence Models

Currently, when the Reynolds-Averaged Navier-Stokes (RANS) equations are solved using turbulence modeling, most often the one-equation model of Spalart and Allmaras is used. Then, it is only necessary to solve the RANS equations in conjunction with a single transport equation for modeling turbulence. For this model, considerable assessment and analysis has been performed, allowing the possibility of a reliable solution method for an eddy viscosity required to compute the Reynolds stresses in the RANS equations. Such evaluation along with analysis has not been achieved to realize similar performance with two-equation models of the k-w type. The primary objective of this paper is to present and discuss the components of an effective numerical algorithm for solving the RANS equations and the two transport equations of k-w type turbulence models. All the important details of the turbulence model as actually implemented are given, which is sometimes not done in various papers considering such modeling. The viability and effectiveness of this solution algorithm are demonstrated by solving both two-dimensional and three-dimensional aerodynamic flows. In all applications, a linear rate of convergence without oscillations or other evidence of unstable behavior is observed. This behavior is also particularly true when the proposed algorithm is applied to systematically renewed mesh sequences, which is generally not observed with algorithms solving more than one transport equation. Thus, numerical integration errors are systematically reduced, allowing for a significantly more reliable assessment of the effectiveness of the turbulence model. Additionally, in this paper, analysis of the solution algorithm, including linear stability, is also performed for a particular flow problem.

Turbulence↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗