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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Scalable and Distributed Algorithm for Managing Residential Demand Response Programs using Alternating Direction Method of Multipliers (ADMM)

For effective engagement of residential demand-side resources and to ensure efficient operation of distribution networks, we must overcome the challenges of controlling and coordinating residential components and devices at scale. To overcome this challenge, we present a distributed and scalable algorithm with a three-level hierarchical information exchange architecture for managing the residential demand response programs. First, a centralized optimization model is formulated to maximize community social welfare. Then, this centralized model is solved in a distributed manner with alternating direction method of multipliers (ADMM) by decomposing the original problem to utility-level and house-level problems. The information exchange between the different layers is limited to the primary residual (i.e., supply-demand mismatch), Lagrangian multipliers, and the total load of each house to protect each customer’s privacy. Simulation studies are performed on the IEEE 33 bus test system with 605 residential customers. The results demonstrate that the proposed approach can save customers’ electricity bills and reduce the peak load at the utility level without much affecting customers’ comfort and privacy. Finally, a quantitative comparison of the distributed and centralized algorithms shows the scalability advantage of the proposed ADMM-based approach, and it gives benchmarking results with achievable value for future research works.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Resilience Evaluation and Enhancement for Island City Integrated Energy Systems

Extreme natural hazards, such as hurricanes or earthquakes, have a high probability of threatening energy supply security and causing high-order contingencies to island city-integrated energy systems (IC-IESs). To better evaluate and enhance resilience, a novel approach is proposed in this work for IC-IESs. The resilience of an IC-IES is analyzed from both the system level and the component level. At the system level, the impacts of extreme natural disasters are quantified. At the component level, the importance of individual components is analyzed through pre-failure and post-failure indices. The pre-failure index identifies the system’s weak links before an energy interruption, and the post-failure index determines the optimal repair strategy to restore the service. The proposed indices are solved by the impact increment method (IIM), which significantly improves computational efficiency without much affecting result accuracy. Numerical simulation studies are conducted on the modified Barry Island IES and IES E123-G48-H32 test systems. Furthermore, the results validate the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Model Predictive Fault-Tolerant Tracking Control for PDF Control Systems With Packet Losses

In this article, a fault-tolerant tracking control strategy is investigated for nonlinear probability density function (PDF) control systems with the actuator fault, uncertainties, unknown disturbance, and random packet losses. The control input signal dropout and measurement signal dropouts are described as the independent Bernoulli distribution. An adaptive fault diagnosis (FD) observer based on the Lyapunov function is given to simultaneously estimate the fault, disturbance, and state with packet losses. Furthermore, different from the traditional robust fault-tolerant control (FTC), a new active fault-tolerant tracking controller is designed based on the model predictive control framework, which has better adaptive fault-tolerant performance. Finally, the validity of the proposed FTC method has been proved by a simulation study of a papermaking process.

42 ENGINEERING↗
DOE OSTI2021

Pursuing sources of heterogeneity in modeling clustered population

Abstract Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity , that is, to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. We develop an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

Li, Yan↗
DOE OSTI2019

A Selective Overview and Comparison of Robust Mixture Regression Estimators

Summary Mixture regression models have been widely used in business, marketing and social sciences to model mixed regression relationships arising from a clustered and thus heterogeneous population. The unknown mixture regression parameters are usually estimated by maximum likelihood estimators using the expectation–maximisation algorithm based on the normality assumption of component error density. However, it is well known that the normality‐based maximum likelihood estimation is very sensitive to outliers or heavy‐tailed error distributions. This paper aims to give a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies.

Yu, Chun↗
DOE OSTI2019

Call Center Arrivals: When to Jointly Forecast Multiple Streams?

We consider call centers that have multiple (potentially inter‐dependent) demand arrival streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly forecast future arrivals of the multiple streams. We first develop a general statistical model to simultaneously forecast multi‐stream arrival rates. The model takes into account three types of inter‐stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi‐stream forecasting model vary by the type, direction, and strength of inter‐stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi‐stream arrivals (instead of separately forecasting each stream), when there exists inter‐stream lag dependence among daily arrival rates. Empirical studies, using two real call center datasets further demonstrate our findings, and provide operational insights into how one chooses forecasting models for multi‐stream arrivals.

Ye, Han↗
DOE OSTI2018

Peptoid-based membrane-mimetic two dimensional nanomaterials

This paper presents a review of membrane-mimetic two dimensional (2D) nanomaterials assembled from sequence-defined, diblock-like peptoids through an evaporation-induced crystallization method. Similar to those associated with cell membranes, these peptoid-based nanomembranes exhibit thicknesses in the 3.5 - 5.6 nm range, spontaneous assembly at interfaces, thickness variations in response to changes in Na+ concentrations, and the ability to self-repair. Moreover, they are highly stable, free-standing, and atomically ordered. Both experimental and simulations studies showed that these nanomembranes were formed through an anisotropic formation process. We further demonstrated the incorporation and patterning of a broad range of functional groups within peptoid membranes through large side-chain diversity and/or co-crystallization approaches. By tuning the peptoid hydrophobic domains which determine the stability of nanomembranes, we demonstrated the assembly of single-walled crystalline nanotubes through folding peptoid-based 2D nanomaterials. Given peptoids are biocompatible and easy to synthesize, we anticipate this new class of peptoid-based 2D nanomaterials will provide a robust platform for development of biomimetic materials tailored to specific applications.

Chen, Chunlong↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

TRANSP-based Optimization Towards Tokamak Scenario Development

An optimization approach that incorporates the predictive transport code TRANSP is proposed for tokamak scenario development. Optimization methods are often employed to develop open-loop control strategies to aid access to high performance tokamak scenarios. In general, the optimization approaches use control-oriented models, i.e. models that are reduced in complexity and prediction accuracy as compared to physics-oriented transport codes such as TRANSP. In the presented approach, an optimization procedure using the TRANSP code to simulate the tokamak plasma is considered for improved predictive capabilities. As a test case, the neutral beam injection (NBI) power is optimized to develop a control strategy that maximizes the non-inductive current fraction during the ramp-up phase for NSTX-U. Simulation studies towards the achievement of non-inductive ramp up in NSTX-U have already been carried out with the TRANSP code. The optimization-based approach proposed in this work is used to maximize the non-inductive current fraction during ramp-up in NSTX-U, demonstrating that the scenario development task can be automated. An additional test case considers optimization of the current ramp rate in DIII-D for obtaining a stationary plasma characterized bya flat loop voltage profile in the flattop phase.

DIII-D↗
DOE OSTI2018

Predictive Control of Residential Heat Pump Water Heaters [SWR-13-35]

Heat pump water heaters (HPWH) in the United States typically feature both a heat pump and at least one electric resistance element for heating. The heat pump is much more efficient than the element but cannot heat the water as quickly. The combination of these two heat sources makes HPWH a perfect candidate for implementing advanced control algorithms to achieve additional energy savings. NREL's Predictive Control of Residential Heat Pump Water Heaters introduces a novel predictive control method that provides optimal setpoint profile for both heat sources based on the patterns discovered from users' hot water use data. A simulation study indicates the Predictive Control of Residential Heat Pump Water Heaters is able to achieve significant energy savings without impacting users' thermal comfort.

Christensen, Dane↗
DOE OSTI2023

A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior

A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for 3 and 5 treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving precision and accuracy of genetic mapping with genotyping-by-sequencing data in outcrossing species

This dataset contains all data and supplementary materials from "Improving precision and accuracy of genetic mapping with genotyping-by-sequencing data in outcrossing species". An Excel file a list of all QTLs and linkage group length (in cM) obtained with two different SNP-calling methods (Tassel-Uneak and Tassel-GBS), genetic map-construction method (linkage-only and reference order-corrected) and depth filters (12x, 20x, 30x and 40x) for genetic mapping of 18 biomass yield traits in a biparental Miscanthus sinensis population using RAD-Seq SNPs is provided as "Supplementary file 1". A Perl script with the code for filtering VCF and HapMap-formatted data files is provided as “Supplementary file 2”. Phenotype data used for QTL mapping is provided as “Supplementary File 3”. A Perl script with the code for the simulation study is provided as “Supplementary file 4”.

GenotypingSimulator↗
DOE OSTI2018

X-ray tomography of extended objects: a comparison of data acquisition approaches

The penetration power of x-rays allows one to image large objects while their short wavelength allows for high spatial resolution. As a result, with synchrotron sources one has the potential to obtain tomographic images of centimeter-sized specimens with sub-micrometer pixel sizes. However, limitations on beam and detector size make it difficult to acquire such data of this sort in a single take, necessitating strategies for combining data from multiple regions. One strategy is to acquire a tiled set of local tomograms by rotating the specimen around each of the local tomogram center positions. Another strategy, sinogram oriented acquisition, involves the collection of projections at multiple offset positions from the rotation axis followed by data merging and reconstruction. We have carried out a simulation study to compare these two approaches in terms of radiation dose applied to the specimen, and reconstructed image quality. Local tomography acquisition involves an easier data alignment problem, and immediate viewing of subregions before the entire dataset has been acquired. Sinogram oriented acquisition involves a more difficult data assembly and alignment procedure, and it is more sensitive to accumulative registration error. Furthermore, sinogram oriented acquisition is more dose-efficient, it involves fewer translation motions of the object, and it avoids certain artifacts of local tomography.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Modeling energy balance while correcting for measurement error via free knot splines

Measurements of energy balance components (energy intake, energy expenditure, changes in energy stores) are often plagued with measurement error. Doubly-labeled water can measure energy intake (EI) with negligible error, but is expensive and cumbersome. An alternative approach that is gaining popularity is to use the energy balance principle, by measuring energy expenditure (EE) and change in energy stores (ES) and then back-calculate EI. Gold standard methods for EE and ES exist and are known to give accurate measurements, albeit at a high cost. We propose a joint statistical model to assess the measurement error in cheaper, non-intrusive measures of EE and ES. We let the unknown true EE and ES for individuals be latent variables, and model them using a bivariate distribution. We try both a bivariate Normal as well as a Dirichlet Process Mixture Model, and compare the results via simulation. Our approach, is the first to account for the dependencies that exist in individuals’ daily EE and ES. We employ semiparametric regression with free knot splines for measurements with error, and linear components for error free covariates. We adopt a Bayesian approach to estimation and inference and use Reversible Jump Markov Chain Monte Carlo to generate draws from the posterior distribution. Based on the semiparameteric regression, we develop a calibration equation that adjusts a cheaper, less reliable estimate, closer to the true value. Along with this calibrated value, our method also gives credible intervals to assess uncertainty. A simulation study shows our calibration helps produce a more accurate estimate. Furthermore, our approach compares favorably in terms of prediction to other commonly used models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Reduction of Coherent Betatron Oscillations Using RF Electric Fields in the Fermilab Muon g-2 Experiment

The Fermilab Muon g-2 experiment aims to measure the muon anomalous magnetic moment with a 140 parts-per-billion precision to investigate the greater than 3 standard deviation difference between the Standard Model prediction and the previous measurement by the BNL Muon g-2 experiment. The coherent betatron oscillation (CBO) beam effects must be corrected for in the decay-positron time spectra fits used in high precision muon storage ring based anomalous magnetic moment measurements. This MC simulation study indicates that the application of radio frequency (RF) electric fields to the muon storage ring beam can reduce the CBO amplitude by up to a factor of 10, as well to increase the symmetry of the beam phase space. This is achieved by correcting the mismatched oscillation phases between the high and low momentum muon populations by modulating the muon beam betatron oscillation frequencies with off-resonance RF fields.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

The Relation Between Field Flatness and the Passband Frequency in the Elliptical Cavities

A technique that predicts the field flatness of the operating pi-mode based on the passband frequency is highly desirable when the direct measurement of the field is not available. Such a technique was developed for the SNS-PPU cavity, a 6-cell SRF cavity whose field flatness is important for cold operation. In this paper, we will present the theory on the relations between field profile and passband frequencies of the arbitrary deformed cavities, the simulation studies, and comparison with the experimental measurements.

Park, G. -T.↗