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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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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A multitree approach for global solution of ACOPF problems using piecewise outer approximations

Electricity markets rely on the rapid solution of the optimal power flow (OPF) problem to determine generator power levels and set nodal prices. Traditionally, the OPF problem has been formulated using linearized, approximate models, ignoring nonlinear alternating current (AC) physics. These approaches do not guarantee global optimality or even feasibility in the real ACOPF problem. We introduce an outer-approximation approach to solve the ACOPF problem to global optimality based on alternating solution of upper- and lower-bounding problems. The lower-bounding problem is a piecewise relaxation based on strong second-order cone relaxations of the ACOPF, and these piecewise relaxations are selectively refined at each major iteration through increased variable domain partitioning. Our approach is able to efficiently solve all but one of the test cases considered to an optimality gap below 0.1%. This approach opens the door for global solution of MINLP problems with AC power flow equations.

42 ENGINEERING↗
DOE OSTI2025

Optimal Control Strategy With Efficiency and Reliability Improvement for Offshore DC Microgrids

Offshore microgrids, due to their remote location and lack of external energy support, face significant challenges in wide-range load operation and maintenance. Consequently, efficiency and reliability are critical concerns for converters in offshore dc microgrids. This article presents an optimal control strategy aimed at enhancing both efficiency and reliability. A normalized nonlinear relationship between power loss and thermal stress of a paralleled converter is first established. Based on this, a dual-objective optimization function with an active weight function as well as a system overall performance index is established. The active weight function dynamically adjusts the control priority based on converter efficiency and switching device thermal stress. Then, the optimal power-sharing strategy is derived by the Lagrange multiplier method with the proposed optimal function. Additionally, to accommodate a wide load range, an optimal selection strategy for operating converter combinations is proposed, requiring only low-bandwidth communication. Experiment verification is given to validate the effectiveness of the proposed control strategy. The experiment results demonstrate that the proposed control strategy can improve the overall performance of offshore microgrids by optimizing efficiency and reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

High resolution simulations of ignition capsule designs for the National Ignition Facility

Ignition capsule designs for the National Ignition Facility (NIF) [G. H. Miller, E. I. Moses, and C. R. Wuest, Opt. Eng. 443, 2841 (2004)] have continued to evolve in light of improved physical data inputs, improving simulation techniques, and - most recently - experimental data from a growing number of NIF sub-ignition experiments. This paper summarizes a number of recent changes to the cryogenic capsule design and some of our latest techniques in simulating its performance. Specifically, recent experimental results indicated harder x-ray drive spectra in NIF hohlraums than were predicted and used in previous capsule optimization studies. To accommodate this harder drive spectrum, a series of high-resolution 2-D simulations, resolving Legendre mode numbers as high as two thousand, were run and the germanium dopant concentration and ablator shell thicknesses re-optimized accordingly. Simultaneously, the possibility of cooperative or nonlinear interaction between neighboring ablator surface defects has motivated a series of fully 3-D simulations run with the massively parallel HYDRA code. These last simulations include perturbations seeded on all capsule interfaces and can use actual measured shell surfaces as initial conditions. 3-D simulations resolving Legendre modes up to two hundred on large capsule sectors have run through ignition and burn, and higher resolution simulations resolving as high as mode twelve hundred have been run to benchmark high-resolution 2-D runs. Finally, highly resolved 3-D simulations have also been run of the jet-type perturbation caused by the fill tube fitted to the capsule. These 3-D simulations compare well with the more typical 2-D simulations used in assessing the fill tube's impact on ignition. Coupled with the latest experimental inputs from NIF, our improving simulation capability yields a fuller and more accurate picture of NIF ignition capsule performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

DFT calculations on spectroscopic and structural properties of a NLO chromophore

The molecular geometry optimization, vibrational frequencies and gauge including atomic orbital (GIAO) {sup 1}H and {sup 13}C NMR chemical shift values of 2-(1′-(4’’’-Methoxyphenyl)-5′-(thien-2″-yl)pyrrol-2′-yl)-1,3-benzothiazole as potential nonlinear optical (NLO) material were calculated using density functional theory (DFT) HSEh1PBE method with 6–311G(d,p) basis set. The best of our knowledge, this study have not been reported to date. Additionally, a detailed vibrational study was performed on the basis of potential energy distribution (PED) using VEDA program. It is noteworthy that NMR chemical shifts are quite useful for understanding the relationship between the molecular structure and electronic properties of molecules. The computed IR and NMR spectra were used to determine the types of the experimental bands observed. Predicted values of structural and spectroscopic parameters of the chromophore were compared with each other so as to display the effects of the different substituents on the spectroscopic and structural properties. Obtained data showed that there is an agreement between the predicted and experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Real-time Nonlinear Model Predictive Control (NMPC) Strategies using Physics-Based Models for Advanced Lithium-ion Battery Management System (BMS)

Optimal operation of lithium-ion batteries requires robust battery models for advanced battery management systems (ABMS). A nonlinear model predictive control strategy is proposed that directly employs the pseudo-two-dimensional (P2D) model for making predictions. Using robust and efficient model simulation algorithms developed previously, the computational time of the nonlinear model predictive control algorithm is quantified, and the ability to use such models for nonlinear model predictive control for ABMS is established.

25 ENERGY STORAGE↗
DOE OSTI2023

In situ high-harmonic microscopy of a nanostructured solid

Nanostructured optical surfaces allow exquisite control over linear and nonlinear light interactions, where the surface actively creates new frequencies up to high-order harmonics of an intense infrared driving laser field. The function and performance of these surfaces depend sensitively on the distribution of the high-harmonic fields in and between the nanostructured elements, as the high-harmonic wavelength becomes comparable to the nanoscale features of the surface. Imaging the nonlinear response at the active surface with nanometer resolution would greatly benefit the optimization of the metasurface's function. Here we demonstrate an approach to lensless imaging of extreme ultraviolet high harmonics that resolves the amplitude and phase of nonlinear polarization at the active nanostructured surface of an MgO crystal. Imaging the near-field distribution of high harmonics is the gateway to optimized functional high-harmonic metasurfaces and the integration of high harmonics on a chip.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Monotonically convergent optimization in quantum control using Krotov's method

The non-linear optimization method developed by A. Konnov and V. Krotov [Autom. Remote Cont. (Engl. Transl.) 60, 1427 (1999)] has been used previously to extend the capabilities of optimal control theory from the linear to the non-linear Schroedinger equation [S. E. Sklarz and D. J. Tannor, Phys. Rev. A 66, 053619 (2002)]. Here we show that based on the Konnov-Krotov method, monotonically convergent algorithms are obtained for a large class of quantum control problems. It includes, in addition to nonlinear equations of motion, control problems that are characterized by non-unitary time evolution, nonlinear dependencies of the Hamiltonian on the control, time-dependent targets, and optimization functionals that depend to higher than second order on the time-evolving states. We furthermore show that the nonlinear (second order) contribution can be estimated either analytically or numerically, yielding readily applicable optimization algorithms. We demonstrate monotonic convergence for an optimization functional that is an eighth-degree polynomial in the states. For the ''standard'' quantum control problem of a convex final-time functional, linear equations of motion and linear dependency of the Hamiltonian on the field, the second-order contribution is not required for monotonic convergence but can be used to speed up convergence. We demonstrate this by comparing the performance of first- and second-order algorithms for two examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

High-performance high-resolution semi-Lagrangian tracer transport on a sphere

Current climate models have a limited ability to increase spatial resolution because numerical stability requires the time step to decrease. We describe a semi-Lagrangian method for tracer transport that is stable for arbitrary Courant numbers, and we test a parallel implementation discretized on the cubed sphere. The method includes a fixer that conserves mass and constrains tracers to a physical range of values. The method shows third-order convergence and maintains nonlinear tracer correlations to second order. It shows optimal accuracy at Courant numbers of 10-20, more than an order of magnitude higher than explicit methods. We present parallel performance in terms of strong scaling, weak scaling, and spatial scaling (where the time step stays constant while the resolution increases). For a 0.2° test with 100 tracers, the implementation scales efficiently to 10,000 MPI tasks.

Computer Science↗
DOE OSTI2022

Kernel learning backward SDE filter for data assimilation

In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. Further, to characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A quantitative assessment of the model form error of friction models across different interface representations for jointed structures

Hysteretic models are widely used to model frictional interactions in joints to recreate experimental behavior. However, it is unclear which models are best suited for fitting or predicting the responses of structures. Here, the present study evaluates 26 friction model/interface representation combinations to quantify the model form error. A Quasi-Static Modal Analysis approach (termed Rayleigh Quotient Nonlinear Modal Analysis) is adopted to calculate the nonlinear system response, and a Multi-Objective Optimization is solved to fit experimental data of the first mode of the Brake-Reuß Beam. Optimized parameters from the first mode are applied to the second and third bending modes to quantify the predictive ability of the models. Formulations for both tracing full hysteresis loops and recreating hysteresis loops from a single loading curve (Masing assumptions) are considered. Smoothly varying models applied to a five patch representation showed the highest flexibility (for fitting mode 1) and good predictive potential (for modes 2 and 3). For a second formulation, which uses 152 frictional elements to represent the interface, the physically motivated spring in series with a Coulomb slip model (elastic dry friction) has high error for fitting mode 1 and performs near the middle for predicting higher modes. For both interface representation, the best fit models are not the most physical, but rather the ones with the most parameters (as expected); however, the more physical models perform somewhat better for predicting the higher modes.

42 ENGINEERING↗
DOE OSTI2011

Minimizers of Hartree Type Functionals and Related Interpolation Inequalities

In this work we are concerned with the minimization problem associated to the best constant C{sub n,{alpha}} in the following interpolation inequality {integral}{integral}(|u(x)|{sup {alpha}}|u(y)|{sup {alpha}}/|x-y|{sup n-2})dxdy{<=}C{sub n,{alpha}}||{nabla}{psi}||{sub L{sup 2}}{sup n{alpha}-(n+2)}||{psi}|| {sub L{sup 2}}{sup (n+2)-(n-2){alpha}}, u is an element of H{sup 1}(R{sup n}) for 2{<=}{alpha}<1+4/(n-2) and n{>=}3. The corresponding variational problem is equivalent to the problem of existence of a ground state solution to the nonlinear non-local elliptic equation -{Delta}u+{omega}u-(|x|{sup -(n-2)}*|u|{sup {alpha}})|u|{sup {alpha}-2}u = 0. We show that the ground state solution is unique and its mass parallel u parallel {sub L{sup 2}}{sup 2} determines the best constant C{sub n,{alpha}}. As a consequence of the above results, we obtain sharp sufficient condition for global existence for the L{sup 2}-critical Hartree equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Short-wavelength and three-dimensional instability evolution in National Ignition Facility ignition capsule designs

Ignition capsule designs for the National Ignition Facility (NIF) [G. H. Miller, E. I. Moses, and C. R. Wuest, Opt. Eng. 443, 2841 (2004)] have continued to evolve in light of improved physical data inputs, improving simulation techniques, and, most recently, experimental data from a growing number of NIF sub-ignition experiments. This paper summarizes a number of recent changes to the cryogenic capsule design and some of our latest techniques in simulating its performance. Specifically, recent experimental results indicated harder x-ray drive spectra in NIF hohlraums than were predicted and used in previous capsule optimization studies. To accommodate this harder drive spectrum, a series of high-resolution 2-D simulations, resolving Legendre mode numbers as high as 2000, were run and the germanium dopant concentration and ablator shell thicknesses re-optimized accordingly. Simultaneously, the possibility of cooperative or nonlinear interaction between neighboring ablator surface defects has motivated a series of fully 3-D simulations run with the massively parallel HYDRA code. These last simulations include perturbations seeded on all capsule interfaces and can use actual measured shell surfaces as initial conditions. 3-D simulations resolving Legendre modes up to 200 on large capsule sectors have run through ignition and burn, and higher resolution simulations resolving as high as mode 1200 have been run to benchmark high-resolution 2-D runs. Finally, highly resolved 3-D simulations have also been run of the jet-type perturbation caused by the fill tube fitted to the capsule. These 3-D simulations compare well with the more typical 2-D simulations used in assessing the fill tube's impact on ignition. Coupled with the latest experimental inputs from NIF, our improving simulation capability yields a fuller and more accurate picture of NIF ignition capsule performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Computation of Direct Sensitivities of Spatial Multibody Systems With Joint Friction

Abstract Friction exists in most mechanical systems and may have a major influence on the dynamic performance of the system. The incorporation of friction in dynamic systems has been a subject of active research for several years owing to its high nonlinearity and its dependence on several parameters. Consequently, optimization of dynamic systems with friction becomes a challenging task. Gradient-based optimization of dynamical systems is a prominent technique for optimal design and requires the computation of model sensitivities with respect to the design parameters. The novel contribution of this paper is the derivation of the analytical methodology for the computation of direct sensitivities for smooth multibody systems with joint friction using the Lagrangian index-1 formulation. System dynamics have been computed using two different friction models; the Brown and McPhee, and the Gonthier et al. model. The methodology proposed to obtain model sensitivities has also been validated using the complex finite difference method. A case study has been conducted on a spatial multibody system to observe the effect of friction on the dynamics and model sensitivities, compare sensitivities with respect to different parameters and demonstrate the numerical and validation aspects. Since design parameters can have very different magnitudes and units, the sensitivities have been scaled with the parameters for comparison. Finally, a discussion has been presented on the interpretation of the case study results. Due to the incorporation of joint friction, ‘jumps’ or discontinuities are observed in the model sensitivities akin to those observed for hybrid dynamical systems.

Engineering↗
DOE OSTI2014

Numerical Studies of Optimization and Aberration Correction Methods for the Preliminary Demonstration of the Parametric Ionization Cooling (PIC) Principle in the Twin Helix Muon Cooling Channel

Muon colliders have been proposed for the next generation of particle accelerators that study high-energy physics at the energy and intensity frontiers. In this paper we study a possible implementation of muon ionization cooling, Parametric-resonance Ionization Cooling (PIC), in the twin helix channel. The resonant cooling method of PIC offers the potential to reduce emittance beyond that achievable with ionization cooling with ordinary magnetic focusing. We examine optimization of a variety of parameters, study the nonlinear dynamics in the twin helix channel and consider possible methods of aberration correction.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

High-Q silicon photonic crystal cavity for enhanced optical nonlinearities

We fabricate and experimentally characterize an H0 photonic crystal slab nanocavity with a design optimized for maximal quality factor, Q = 1.7 × 10{sup 6}. The cavity, fabricated from a silicon slab, has a resonant mode at λ = 1.59 μm and a measured Q-factor of 400 000. It displays nonlinear effects, including high-contrast optical bistability, at a threshold power among the lowest ever reported for a silicon device. With a theoretical modal volume as small as V = 0.34(λ/n){sup 3}, this cavity ranks among those with the highest Q/V ratios ever demonstrated, while having a small footprint suited for integration in photonic circuits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗