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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗
DOE OSTI2016

UO 2 Grain Growth: Developing Phase Field Models for Pore Dragging, Solute Dragging and Anisotropic Grain Boundary Energies

A detailed phase field model for the effect of pore drag on grain growth kinetics was implemented in MARMOT. The model takes into consideration both the curvature-driven grain boundary motion and pore migration by surface diffusion. As such, the model accounts for the interaction between pore and grain boundary kinetics, which tends to retard the grain growth process. Our 2D and 3D simulations demonstrate that the model capture all possible pore-grain boundary interactions proposed in theoretical models. For high enough surface mobility, the pores move along with the migrating boundary as a quasi-rigid-body, albeit hindering its migration rate compared to the pore-free case. For less mobile pores, the migrating boundary can separate from the pores. For the pore-controlled grain growth kinetics, the model predicts a strong dependence of the growth rate on the number of pores, pore size, and surface diffusivity in agreement with theroretical models. An evolution equation for the grain size that includes these parameters was derived and showed to agree well with numerical solution. It shows a smooth transition from boundary-controlled kinetics to pore-controlled kinetics as the surface diffusivity decreases or the number of pores or their size increases. This equation can be utilized in BISON to give accurate estimate for the grain size evolution. This will be accomplished in the near future. The effect of solute drag and anisotropy of grain boundary on grain growth will be investigated in future studies.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Early Career: In-situ Monitoring of Dynamic Phenomena during Solidification

Solidification is critical to processes like casting and additive manufacturing and the manufacture of metallic alloy components we use in our everyday lives. State-of-the-art characterization techniques, now available at U.S. DOE User Facilities and in the laboratory, are not only enabling fundamental studies of metallic alloy solidification dynamics, but also in-operando, in-situ deformation, and manufacturing studies. Here we use x-ray, proton, and electron imaging to study solidification dynamics from the micro-scale to the macro-scale, at times ranging from microseconds to minutes. Our experimental results are used to inform, develop, and validate computational models at the same length and time-scales. Integrating in-situ characterization and modeling will yield the prediction and control of metallic alloy solidification dynamics and the creation of microstructures and properties by design with advanced manufacturing. Major research accomplishments during this period include: 1. Exploring dynamical microstructure selection by means of real-time synchrotron x-ray imaging of controlled directional solidification of aluminum alloys and quantitative phase field modeling. 2. In-situ and ex-situ electron microscopy of rapid solidification after laser melting of metallic alloys. 3. Exploring the impact of microstructural heterogeneities that develop during solidification on solid-state phase transitions, with an emphasis on phase growth, (meta)stability, and transition pathways in aluminum alloys. 4. Demonstrations of four-dimensional (three-dimensions plus time) x-ray microscopy during controlled directional solidification and the first quasi-static/dynamic transmission high energy electron microscopy of metallic alloy solidification dynamics. 5. Experiments and modeling of solidification structure evolution and forward modeling and testing of micromechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF B z , Dst , and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst ), AE , and wave activity.

58 GEOSCIENCES↗
DOE OSTI2021

Comparison of Probabilistic Forecasts for Predictive Voltage Control

This paper explores predictive cooperative voltage control in distribution systems with highly variable sources such as photovoltaics (PV). The goal is to maintain the voltage profile within the limits despite the fluctuations due to sudden changes in solar power generation. The predictive voltage control method relies on probabilistic solar power and load forecasts to select the optimum Voltage Regulator (VR) taps appropriately. VR taps are selected to minimize the risk of voltage violation. A modified version of the IEEE 123 system is used as the case study. A 100% penetration of solar power is assumed for the distribution system with profiles for solar generation and loads added to the system. Three different probabilistic forecast models (Quantile Regression (QR), Gaussian distribution and volatility forecasting using Generalized Autoregressive Conditional Heteroskedasticity (GARCH)) are explored in this study. The results for the VR taps and Voltage Deviation Index (VDI) are compared to find the most effective forecast model.

Panamtash, Hossein↗
DOE OSTI2020

Multiscale Investigation of the Mechanisms Controlling the Corrosion of Borosilicate Glasses in Hyper-Alkaline Media

The overarching goal of the present multiscale investigation is to unearth the kinetics and mechanisms of corrosion of borosilicate glasses in hyper-alkaline (pH = 13) environments as a function of their chemical composition. Accordingly, a series of 3- to 6-component borosilicate glasses have been designed starting from Na 2 O–B 2 O 3 –SiO 2 ternary, wherein the compositional complexity has been added in a systematic tiered approach, finally resulting in the composition of the well-known international simple glass (ISG). Tetramethylammonium hydroxide (TMAH), one of the most widely used alkaline etchant in the glass and electronics industry, has been used as the corrosion media. A series of state-of-the-art characterization techniques including magic angle spinning nuclear magnetic resonance spectroscopy, X-ray photoelectron spectroscopy, inductively coupled plasma optical emission spectroscopy, elastic recoil detection analysis, and high-resolution transmission electron microscopy have been employed to unearth the compositional dependence of glass corrosion in hyper-alkaline environments. The glass compositions underwent congruent corrosion in the forward rate regime, whereas the controlling mechanism of corrosion in the residual rate regime depends on the presence/absence of Ca in the surrounding environment and can be explained on the basis of the dissolution–reprecipitation model. The dependence of corrosion kinetics and the chemistry of alteration products (in the residual rate regime) on the glass composition have been discussed. Here, the results presented in this contribution will ultimately supplement the scientific literature attempting to understand the fundamental science governing the aqueous corrosion of silicate-based glass chemistries and add to the growing database required to develop non-empirical predictive models for designing glasses with controlled dissolution rates.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the Ci PA Initiative

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (Ci PA ) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on Ci PA training drugs. In this study, we report the application of the prespecified model and metric to independent Ci PA validation drugs. Over two validation datasets, the Ci PA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current Ci PA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Statistical and machine learning models for optimizing energy in parallel applications

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. Here, we demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations

This report is a progress report of the accomplishments of the research grant “Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations” during the period 1 May 2011- 31 August 2013. This project is a collaborative one between Columbia University and George Mason University. George Mason University will submit a final technical report at the conclusion of their no-cost extension. The purpose of the proposed research is to identify unforced predictable components on decadal time scales, distinguish these components from forced predictable components, and to assess the reliability of model predictions of these components. Components of unforced decadal predictability will be isolated by maximizing the Average Predictability Time (APT) in long, multimodel control runs from state-of-the-art climate models. Components with decadal predictability have large APT, so maximizing APT ensures that components with decadal predictability will be detected. Optimal fingerprinting techniques, as used in detection and attribution analysis, will be used to separate variations due to natural and anthropogenic forcing from those due to unforced decadal predictability. This methodology will be applied to the decadal hindcasts generated by the CMIP5 project to assess the reliability of model projections. The question of whether anthropogenic forcing changes decadal predictability, or gives rise to new forms of decadal predictability, also will be investigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Dispersal and fire limit Arctic shrub expansion

Abstract Arctic shrub expansion alters carbon budgets, albedo, and warming rates in high latitudes but remains challenging to predict due to unclear underlying controls. Observational studies and models typically use relationships between observed shrub presence and current environmental suitability (bioclimate and topography) to predict shrub expansion, while omitting shrub demographic processes and non-stationary response to changing climate. Here, we use high-resolution satellite imagery across Alaska and western Canada to show that observed shrub expansion has not been controlled by environmental suitability during 1984–2014, but can only be explained by considering seed dispersal and fire. These findings provide the impetus for better observations of recruitment and for incorporating currently underrepresented processes of seed dispersal and fire in land models to project shrub expansion and climate feedbacks. Integrating these dynamic processes with projected fire extent and climate, we estimate shrubs will expand into 25% of the non-shrub tundra by 2100, in contrast to 39% predicted based on increasing environmental suitability alone. Thus, using environmental suitability alone likely overestimates and misrepresents shrub expansion pattern and its associated carbon sink.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations

This work derives a residual-based a posteriori error estimator for reduced models learned with non-intrusive model reduction from data of high-dimensional systems governed by linear parabolic partial differential equations with control inputs. It is shown that quantities that are necessary for the error estimator can be either obtained exactly as the solutions of least-squares problems in a non-intrusive way from data such as initial conditions, control inputs, and high-dimensional solution trajectories or bounded in a probabilistic sense. Here, the computational procedure follows an offline/online decomposition. In the offline (training) phase, the high-dimensional system is judiciously solved in a black-box fashion to generate data and to set up the error estimator. In the online phase, the estimator is used to bound the error of the reduced-model predictions for new initial conditions and new control inputs without recourse to the high-dimensional system. Numerical results demonstrate the workflow of the proposed approach from data to reduced models to certified predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Design of Accelerator Online Simulator Server Using Structured Data

Model based control plays an important role for a modern accelerator during beam commissioning, beam study, and even daily operation. With a realistic model, beam behaviour can be predicted and therefore effectively controlled. The approach used by most current high level application environments is to use a built-in simulation engine and feed a realistic model into that simulation engine. Instead of this traditional monolithic structure, a new approach using a client-server architecture is under development. An on-line simulator server is accessed via network accessible structured data. With this approach, a user can easily access multiple simulation codes. This paper describes the design, implementation, and current status of PVData, which defines the structured data, and PVAccess, which provides network access to the structured data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem—solution of the underlying mathematical model to yield output quantities of interest, given input parameters—is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal configuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems—requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models—entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure.

58 GEOSCIENCES↗
DOE OSTI2015

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem-solution of the underlying mathematical model to yield output quantities of interest, given input parameters--is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision-making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal con figuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems--requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models--entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure. DiaMonD is a multi-institutional DOE MMICC effort that aims to address the challenges of end-to-end, data-to-decisions modeling and simulation for complex CS&E problems in a unifi ed, systematic, and integrated fashion. Institutions involved are Colorado State, Florida State, Los Alamos, MIT, Oak Ridge, Stanford, and UT-Austin. The goals of DiaMonD are (1) to develop advanced mathematical methods and analysis for multimodel, multiphysics, multiscale model problems driven by frontier DOE applications, including those in subsurface energy and environmental flows, materials for energy storage and conversion, and ice sheet dynamics; (2) to create theory and algorithms for integrated inversion, optimization, and uncertainty quantification for these complex problems; and (3) to disseminate the philosophy of an integrated end-to-end, data-to-decisions approach to modeling and simulation of complex problems to the broader applied math and computational science communities through workshops and other outreach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Phenotype heterogeneity in cancer cell populations

Phenotype heterogeneity in cancer cell populations, be it of genetic, epigenetic or stochastic origin, has been identified as a main source of resistance to drug treatments and a major source of therapeutic failures in cancers. The molecular mechanisms of drug resistance are partly understood at the single cell level (e.g., overexpression of ABC transporters or of detoxication enzymes), but poorly predictable in tumours, where they are hypothesised to rely on heterogeneity at the cell population scale, which is thus the right level to describe cancer growth and optimise its control by therapeutic strategies in the clinic. We review a few results from the biological literature on the subject, and from mathematical models that have been published to predict and control evolution towards drug resistance in cancer cell populations. We propose, based on the latter, optimisation strategies of combined treatments to limit emergence of drug resistance to cytotoxic drugs in cancer cell populations, in the monoclonal situation, which limited as it is still retains consistent features of cell population heterogeneity. The polyclonal situation, that may be understood as “bet hedging” of the tumour, thus protecting itself from different sources of drug insults, may lie beyond such strategies and will need further developments. In the monoclonal situation, we have designed an optimised therapeutic strategy relying on a scheduled combination of cytotoxic and cytostatic treatments that can be adapted to different situations of cancer treatments. Finally, we review arguments for biological theoretical frameworks proposed at different time and development scales, the so-called atavistic model (diachronic view relying on Darwinian genotype selection in the coursof billions of years) and the Waddington-like epigenetic landscape endowed with evolutionary quasi-potential (synchronic view relying on Lamarckian phenotype instruction of a given genome by reversible mechanisms), to represent evolution towards heterogeneity, possibly polyclonal, in cancer cell populations and propose innovative directions for therapeutic strategies based on such frameworks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Evaluation of nano/submicro pores in suspension plasma sprayed YSZ coatings

Nano-submicro pores could considerably influence the coating performances and thus should be carefully designed for the intended applications. However, it is challenging to characterize accurately such small pores in coatings. In this study, YSZ coatings were firstly manufactured by suspension plasma spray (SPS) and the nano-submicro pores in as-prepared coatings were investigated using Ultra-small-angle X-ray scattering (USAXS). Afterwards, a multivariate analysis on the effect of five different process parameters was carried out. The two main results showed that: 1) the nano-submicro pores content in coatings has a negative correlation with suspension mass load and powder size, and a positive correlation with spray distance, spray step, and substrate surface roughness; 2) suspension mass load is the main factor affecting the content of nano-submicro pores. Then, a mathematical model for the prediction of nanosubmicro pores was developed, and the control of nano-submicro pores content by using the predictive model was presented. Finally, the sintering effect on the evolution of nano-submicro pores was studied using USAXS as well.

36 MATERIALS SCIENCE↗