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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

An Easy Algorithm to Generate Colored Noise Sequences

The need for accurate and efficient simulation of the noise background arises in statistical significance tests for periodic signals buried in colored noise. This paper discusses techniques for generating colored-noise sequences which simulate processes with a given spectral density. Matlab routines based on the fast fractional difference algorithm are presented. These routines can create various stochastic models (e.g., first-order autoregressive (AR(1)), power law (PL), autoregressive fractionally integrated moving average ((1, d, 0)), and generalized Gauss Markov (GGM)) that serve as possible candidate null hypotheses to test against in various scenarios. Allan variance and power spectral density (PSD) show that our algorithms are accurate and efficient, and can be easily implemented for stationary noise models and non-stationary PLs with spectral indices up to 2. Our algorithms can also be extended to produce a non-stationary PL with a range-limited steeper PSD (spectral indices up to about 4) using a GGM approximation with a proper break frequency. The red-noise leakage effect on the periodogram is further discussed. The result shows that our GGM approximation has a potential for alleviating the red-noise leakage in the PSD estimates. The Timmer and Koenig procedure is also included for comparison.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Sequential Quadratic Programming Algorithm for Nonsmooth Problems with Upper- \({\boldsymbol{\mathcal{C}^2}}\) Objective

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper- \({\boldsymbol{\mathcal{C}^2}}\) objective functions is proposed and analyzed. Upper- \({\boldsymbol{\mathcal{C}^2}}\) is a weakly concave property that exists in difference of convex (DC) functions and arises naturally in many applications, particularly certain classes of solutions to parametric optimization problems e.g., recourse of stochastic programming and projection onto closed sets. The algorithm can be viewed as an extension of sequential quadratic programming (SQP) to nonsmooth problems with upper- \({\boldsymbol{\mathcal{C}^2}}\) objectives or a simplified bundle method. It is globally convergent with bounded algorithm parameters that are updated with a trust-region criterion. The algorithm handles general smooth constraints through linearization and uses a line search to ensure progress. The potential inconsistencies from the linearization of the constraints are addressed through a penalty method. In conclusion, the capabilities of the algorithm are demonstrated by solving both simple upper- \({\boldsymbol{\mathcal{C}^2}}\) problems and a real-world optimal power flow problem used in current power grid industry practices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Geostatistical interpolation of streambed hydrologic attributes with addition of left censored data and anisotropy

Spatial geostatistical interpolation of point measurements of streambed attributes in the hyporheic zone may be constrained by the streambed anisotropy, and data density and spatial distribution may significantly impact the results. Spatial clustering and low spatial data density can be caused by bedrock outcropping at the streambed limiting installation of in-stream piezometers. This study examines parameter error variability of the geostatistical interpolation using anisotropic interpolation methods and increasing the data density by adding left censored values (i.e., data below measurement limit) to locations where measurements were limited by exposed bedrock lining the streambed. The reduction in relative standard error of the interpolation was determined for the spatial distributions of streambed attributes including hydraulic conductivity, seepage flux, and mercury solute flux measured in two different years along a study reach in East Fork Poplar Creek, Tennessee, USA. Here, two methods to impute the left censored values were compared including the conventional half the detection limit substitution method, and the Stochastic Approximation of Expectation-Maximization (SAEM) algorithm, which both had comparable results. Imputing left censored data increased the data density to recommended ranges, reduced data clustering, increased the spatial dependence for some attributes, and reduced the standard error for each of the three attributes. For the reach considered herein, addition of the left censored values resulted in a larger error reduction than the consideration of anisotropy within the interpolation, which confirms the benefit of data addition to increase data density within data-limited river corridors.

58 GEOSCIENCES↗
DOE OSTI2023

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control

Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to decentralized paradigm, LFC needs to consider the decentralized scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing the convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

Kwon, Kyung-bin↗
DOE OSTI2024

A Vibrational Energy Harvesting Sensor Based on Linear and Rotational Electromechanical Effects

In this investigation, a magnetically coupled double-spring design is presented for harvesting low-level non-stationary random vibrational energy. The sensor relies on multimodal coupling between the translation and rotation of a two-spring magnet and coil system to widen the harvesting bandwidth. Energy methods are used to develop a model to characterize the electromechanical response of the system, the solution of which is obtained using stochastic techniques based on a particle swarm algorithm. This approach provides an efficient method to estimate system parameters that otherwise are difficult or impossible to determine with independent measurements. The experimental results demonstrate agreement with the theoretical predictions over a limited bandwidth. The sensor can effectively harvest non-stationary vibration energy down to 10 -4 g within a limited bandwidth of 130–150 Hz. The sensor prototype has an operational volume of 2.6 cm 3 with a calculated power density of 0.2 W/cm 3 . The sensor’s small size results in a coupling efficiency of approximately 6% across the tested bandwidth.

42 ENGINEERING↗
NASA NTRS1988

Maximum likelihood identification for large space structures

This paper examines the use of on-orbit identification based on Maximum Likelihood Estimation (MLE) to provide these high-order, high-accuracy control design models for large space structures (LSS's). First, it outlines a general MLE identification algorithm, together with a covariance-analysis procedure to assess algorithm performance in terms of systematic and stochastic errors. Next, it examines various simplifications appropriate for the LSS identification application. Simplified analytical performance results are presented, as are numerical results to support these analyses. Finally, a graphical interpretation of these results is given.

Barrett, Michael F.↗
NASA NTRS2003

Global and Local Helioseismic Studies of Solar Convection Zone Dynamics Using SOI-MDI on SOHO

Our joint collaborative analyses of global mode data to characterize the solar differential rotation (e.g. Thompson et al. 1996, Schou et al. 1998), and most recently to detect and analyze temporal variations in angular velocity Omega profiles both within the convection zone and in the deeper radiative interior (e.g. Howe et al 2000a,b; Toomre et al. 2000), have led to a series of fascinating discoveries. These should be pursued further as the solar cycle continues. The physical deductions being made from these studies have been greatly strengthened by utilizing both SOI-MDI and GONG data in order to have two independent observational realizations of Doppler images spanning a five-year interval, using two separate procedures to determine global mode splittings, and then analyzing those splitting data sets using both RLS and SOLA inversion procedures. There are considerable subtleties in the effects of instrumental response functions and calibrations, sensitivity of peak finding algorithms and their mode leakage estimates, and stochastic variations in mode amplitudes that can all contribute to apparent changes in the Omega profiles being inferred from sequences of helioseismic data. We have come to understand the implications of many of these calibration and analysis steps, greatly aided by frequent multi-week collaborative working sessions in our Helioseismic Analysis Facility (HAF) at JILA involving many members of the SO1 dynamics and inversion team, including most of our Co-Is during the summer months when we hold intensive working sessions. Considerable further focused attention is required in a collaborative setting on such global mode issues as we continue studying the changing sun.

Toomre, Juri↗
NASA NTRS2003

Scheduling Earth Observing Satellites with Evolutionary Algorithms

We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.

Globus, Al↗
NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗
DOE OSTI2021

An Integrated High-performance Computing and Digital Real-time Simulation Testbed to Benchmark Closed-loop Load Shedding Algorithms in Power Systems

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Will Stochastic Devices Play Nice With Others in Neuromorphic Hardware?: There’s More to a Probabilistic System Than Noisy Devices

Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.

42 ENGINEERING↗
DOE OSTI2024

Community detection in hypergraphs via mutual information maximization

Abstract The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗
NASA NTRS1993

Stochastic Parameters in Lunar Laser Ranging

This paper summarizes the underlying Kalman approach and highlights the techniques, algorithms, and results of its implementation.

lunar laser ranging dynamical astronomy stochastic↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2023

Hybrid stochastic synapses enabled by scaled ferroelectric field-effect transistors

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution and emergence of stochastic non-idealities. While prior work has mainly focused on binary transitions, in this work, we leverage the stochastic switching of a three-state ferroelectric field-effect transistor to implement a long-term and short-term two-tier stochastic synaptic memory with a single device. Experimental measurements are performed on a scaled 28 nm high-k metal gate technology-based device to develop a probabilistic model of the hybrid stochastic synapse. In addition to the advantage of ultra-low programming energies afforded by scaling, our hardware–algorithm co-design analysis reveals the efficacy of the two-tier memory in comparison to binary stochastic synapses in on-chip learning tasks—paving the way for algorithms exploiting multi-state devices with probabilistic transitions beyond deterministic ones.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty

This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, and Hurricane Harvey) severely disrupted the U.S. freight transportation network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider three types of natural disasters that have different risks and impacts on transportation networks: earthquakes, hurricanes, and floods. It is found that for all disaster scenarios, freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is because there are more states in the flood-risk areas, and they are scattered throughout the U.S.

42 ENGINEERING↗
DOE OSTI2020

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗