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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems

We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Crystal structure of glycoside hydrolase family 127 β-L-arabinofuranosidase from Bifidobacterium longum

Graphical abstract: - Highlights: • HypBA1 β-L-arabinofuranosidase belongs to glycoside hydrolase family 127. • Crystal structure of HypBA1 was determined. • HypBA1 consists of a catalytic barrel and two additional β-sandwich domains. • The active site contains a Zn{sup 2+} coordinated by glutamate and three cysteines. • A possible reaction mechanism involving cysteine as the nucleophile is proposed. - Abstract: Enzymes acting on β-linked arabinofuranosides have been unknown until recently, in spite of wide distribution of β-L-arabinofuranosyl oligosaccharides in plant cells. Recently, a β-L-arabinofuranosidase from the glycoside hydrolase family 127 (HypBA1) was discovered in the newly characterized degradation system of hydroxyproline-linked β-L-arabinooligosaccharides in the bacterium Bifidobacterium longum. Here, we report the crystal structure of HypBA1 in the ligand-free and β-L-arabinofuranose complex forms. The structure of HypBA1 consists of a catalytic barrel domain and two additional β-sandwich domains, with one β-sandwich domain involved in the formation of a dimer. Interestingly, there is an unprecedented metal-binding motif with Zn{sup 2+} coordinated by glutamate and three cysteines in the active site. The glutamate residue is located far from the anomeric carbon of the β-L-arabinofuranose ligand, but one cysteine residue is appropriately located for nucleophilic attack for glycosidic bond cleavage. The residues around the active site are highly conserved among GH127 members. Based on biochemical experiments and quantum mechanical calculations, a possible reaction mechanism involving cysteine as the nucleophile is proposed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Structural and biochemical studies on the role of active site Thr166 and Asp236 in the catalytic function of D-Serine deaminase from Salmonella typhimurium

Highlights: • The crystal structure of T166A determined at 2.7 Å resolution represents the first PLP bound form of the StDSD enzyme. • Mutation of Thr166 to Ala and Asp236 to Leu results in the inactivation of the enzyme. • Thr166 might abstract Cα proton of D-Ser to carry out catalysis and Asp236 might stabilize the internal aldimine form. • Conformational changes are important for bringing Thr166 to a position favorable for proton abstraction. D-Serine deaminase (DSD) degrades D-Ser to pyruvate and ammonia. Uropathogenic bacteria survive in the toxic D-Ser containing mammalian urine because of DSD activity. The crystal structure of the apo form of Salmonella typhimurium DSD (StDSD) has been reported earlier. In the present work, we have investigated the role of two active site residues, Thr166 and Asp236 by site directed mutagenesis (T166A and D236L). The enzyme activity is lost upon mutation of these residues. The 2.7 Å resolution crystal structure of T166A DSD with bound PLP reported here represents the first structure of the holo form of StDSD. PLP binding induces small changes in the relative dispositions of the minor and major domains of the protein and this inter-domain movement becomes substantial upon interaction with the substrate. The conformational changes bring Thr166 to a position at the active site favorable for the degradation of D-Ser. Examination of the different forms of the enzyme and comparison with structures of homologous enzymes suggests that Thr166 is the most probable base abstracting proton from the Cα atom of the substrate and Asp236 is crucial for binding of the cofactor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations

Here, this work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. These approximate solutions may arise from early termination of an iterative method, a lower-fidelity model, or a projection-based reduced-order model, for example. The proposed statistical model comprises the sum of a deterministic regression-function model and a stochastic noise model. The method constructs the regression-function model by applying regression techniques from machine learning (e.g., support vector regression, artificial neural networks) to map features (i.e., error indicators such as sampled elements of the residual) to a prediction of the approximate-solution error. The method constructs the noise model as a mean-zero Gaussian random variable whose variance is computed as the sample variance of the approximate-solution error on a test set; this variance can be interpreted as the epistemic uncertainty introduced by the approximate solution. This work considers a wide range of feature-engineering methods, data-set-construction techniques, and regression techniques that aim to ensure that (1) the features are cheaply computable, (2) the noise model exhibits low variance (i.e., low epistemic uncertainty introduced), and (3) the regression model generalizes to independent test data. Finally, numerical experiments performed on several computational-mechanics problems and types of approximate solutions demonstrate the ability of the method to generate statistical models of the error that satisfy these criteria and significantly outperform more commonly adopted approaches for error modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A framework for modeling and optimizing dynamic systems under uncertainty

Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ADAM: A web platform for graph-based modeling and optimization of supply chains

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Metallic phase change material thermal storage for Dish Stirling

Dish-Stirling systems provide high-efficiency solar-only electrical generation and currently hold the world record at 31.25%. This high efficiency results in a system with a high possibility of meeting the DOE SunShot goal of $0.06/kWh. However, current dish-Stirling systems do not incorporate thermal storage. For the next generation of non-intermittent and cost-competitive solar power plants, we propose adding a thermal energy storage system that combines latent (phase-change) energy transport and latent energy storage in order to match the isothermal input requirements of Stirling engines while also maximizing the exergetic efficiency of the entire system. This paper reports current findings in the area of selection, synthesis and evaluation of a suitable high performance metallic phase change material (PCM) as well as potential interactions with containment alloy materials. The metallic PCM's, while more expensive than salts, have been identified as having substantial performance advantages primarily due to high thermal conductivity, leading to high exergetic efficiency. Systems modeling has indicated, based on high dish Stirling system performance, an allowable cost of the PCM storage system that is substantially higher than SunShot goals for storage cost on tower systems. Several PCM's are identified with suitable melting temperature, cost, and performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Corrigendum to ‘Under-capacitated and over-powered? Rural austerity and asymmetrical negotiating relationships in US wind energy development’ [J. Rural Stud., 119 (2025) 1–14]

The authors regret that there is an incomplete sentence in the abstract of the article, and request that the portion “, and counties incre” be deleted from the abstract (found at the end of the sentence beginning with “However …”). The portion to be deleted is underlined and bolded below. The authors would like to apologise for any inconvenience caused. Current abstract: Though rural local governments are central actors in renewable energy development, local governments in the United States (US) remain systematically under-funded. This paper considers what the manifestations of austerity in local governments broadly and rural localities specifically mean for renewable energy development and for energy transitions. Drawing on a survey of 262 elected county officials with experience with wind energy in eight US states, this paper asks how local officials understand the impacts of wind development, how local governments are involved in wind energy negotiations, how the resources and expertise needed to navigate negotiations are distributed among counties, and analyze the relationship between local capacity, access to resources, and involvement in negotiations. We find that local officials express simultaneously affective and material concerns with the impacts of wind development and see negotiations with the developer as central to realizing local benefits. However, the expertise and staffing needed to negotiate with developers is less accessible to poorer or sparsely populated counties, and counties with lower overall revenues have narrower scopes of negotiation, and counties incre. Our results suggest that uneven rural capacity heightens an already asymmetrical relationship between localities and developers. In analyzing how infrastructure developments are shaped by relationships between localities and developers that are conditioned by austerity and (under)capacity, this paper contributes to and bridges scholarly discussions on rural austerity, rescaling, and renewable energy transitions. These results challenge conventional wisdoms around centralizing energy siting processes, contextualize popular and academic debates about opposition to renewable energy development, and highlight the need for rural reinvestment to realize meaningfully participatory energy developments.

Elmallah, Salma↗
DOE OSTI2012

Introduction of oxygen vacancies and fluorine into TiO{sub 2} nanoparticles by co-milling with PTFE

Solid-state processes of introducing oxygen vacancies and transference of fluorine to n-TiO{sub 2} nanoparticles by co-milling with poly(tetrafluoroethylene) (PTFE) powder were examined by diffuse reflectance spectroscopy (DRS) of UV, visual, near- and mid-IR regions, thermal analyses (TG-DTA), energy-dispersive X-ray spectroscopy (EDXS), X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD). The broad absorption peak at around 8800 cm{sup -1} (1140 nm) was attributed to the change in the electronic states, viz. electrons trapped at the oxygen vacancies (Vo) and d-d transitions of titanium ions. Incorporation of fluorine into n-TiO{sub 2} was concentrated at the near surface region and amounted to ca. 40 at% of the total fluorine in PTFE, after co-milling for 3 h, as confirmed by the F1s XPS spectrum. The overall atomic ratio, F/Ti, determined by EDXS was 0.294. By combining these analytical results, a mechanism of the present solid state processes at the boundary between PTFE and n-TiO{sub 2} was proposed. The entire process is triggered by the partial oxidative decomposition of PTFE. This is accompanied by the abstraction of oxygen atoms from the n-TiO{sub 2} lattices. Loss of the oxygen atoms results in the formation of the diverse states of locally distorted coordination units of titania, i.e. TiO{sub 6-n}Vo{sub n}, located at the near surface region. This leads subsequent partial ligand exchange between F and O, to incorporate fluorine preferentially to the near surface region of n-TiO{sub 2} particles, where local non-crystalline states predominate. - Graphical abstract: Scheme of the reaction processes: (a) pristine mixture, (b) oxygen abstraction from TiO{sub 2} and (c) fluorine migration from PTFE to TiO{sub 2}. Highlights: Transfer of fluorine from PTFE to n-TiO{sub 2} in a dry solid state process was confirmed. Black-Right-Pointing-Pointer 40% of F in PTFE was incorporated to the near surface region of n-TiO{sub 2} nanoparticles. Black-Right-Pointing-Pointer The transfer process is triggered by the oxidative decomposition of PTFE. Black-Right-Pointing-Pointer Fluorine incorporation is mediated by the formation of oxygen vacancies. Black-Right-Pointing-Pointer The sequential mechanisms are verified by XPS, EDXS, HRTEM, TG and DRS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗