A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration
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Engineering↗
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Results for “FRAMEWORK”
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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A Codesign Framework for Online Data Analysis and Reduction
In this paper we discuss our design of a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for performing parametric studies on coupled applications. Cheetah facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage vs. compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next-generation simulations that make use of online processing techniques. We illustrate the design choices of Cheetah by using a reaction-diffusion simulation (Gray-Scott) paired with an analysis application to demonstrate initial results of fine-grained process placement on Summit, a pre-exascale supercomputer at Oak Ridge National Laboratory.
Mehta, Kshitij↗
An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems
The potential expansion of biofuel production raises food, energy, and environmental challenges that require careful assessment of the impact of biofuel production on greenhouse gas (GHG) emissions, soil erosion, nutrient loading, and water quality. In this study, we describe a spatially explicit integrative modeling framework (SEIMF) to understand and quantify the environmental impacts of different biomass cropping systems. This SEIMF consists of three major components: (1) a geographic information system (GIS)-based data analysis system to define spatial modeling units with resolution of 56 m to address spatial variability, (2) the biophysical and biogeochemical model Environmental Policy Integrated Climate (EPIC) applied in a spatially-explicit way to predict biomass yield, GHG emissions, and other environmental impacts of different biofuel crops production systems, and (3) an evolutionary multiobjective optimization algorithm for exploring the trade-offs between biofuel energy production and unintended ecosystem-service responses. Simple examples illustrate the major functions of the SEIMF when applied to a nine-county Regional Intensive Modeling Area (RIMA) in SW Michigan to (1) simulate biofuel crop production, (2) compare impacts of management practices and local ecosystem settings, and (3) optimize the spatial configuration of different biofuel production systems by balancing energy production and other ecosystem-service variables. Potential applications of the SEIMF to support life cycle analysis and provide information on biodiversity evaluation and marginal-land identification are also discussed. The SEIMF developed in this study is expected to provide a useful tool for scientists and decision makers to understand sustainability issues associated with the production of biofuels at local, regional, and national scales.
bioenergy↗
Reman Co-Design: A Combined Design and Remanufacturing Optimization Framework for the Sustainable Design of High-Value Components
Remanufacturing is a process that returns end-of-life equipment to as-new conditions and offers numerous environmental and economic benefits. To fully capitalize on remanufacturing, its synergistic interactions with design must be fully realized and addressed during the design stage. Although this fact is widely recognized in the literature, most of the current studies focus primarily either on the design or remanufacturing aspects of design for remanufacturing (DfRem). In an effort to offer a more integrated DfRem approach than those reported in the literature, we propose a new combined design and remanufacturing optimization (reman co-design) framework that takes a holistic approach by leveraging the intricate interplay between design and remanufacturing. Here, the aim of this formulation is to identify the optimal decisions that maximize the benefits of remanufacturing throughout the entire lifespan of a product. To showcase the utility of the new formulation, we are using a case study of a hydraulic manifold, (re)manufactured by John Deere. Using this industry example, we compare the results of reman co-design to the ones from a decoupled remanufacturing design approach. Results reveal that remanufacturing benefits are better realized and improved upon when using the developed reman co-design approach.
design for X↗
Seeded growth of single-crystal two-dimensional covalent organic frameworks
Polymerizing monomers into periodic two-dimensional (2D) networks provides structurally precise, layered macromolecular sheets that exhibit desirable mechanical, optoelectrotronic, and molecular transport properties. 2D covalent organic frameworks (COFs) offer broad monomer scope but are generally isolated as powders comprised of aggregated nanometer-scale crystallites. Here we control 2D COF formation using a two-step procedure, in which monomers are added slowly to pre-formed nanoparticle seeds. The resulting 2D COFs are isolated as single-crystalline, micron-sized particles. Transient absorption spectroscopy of the dispersed COF nanoparticles provides two to three orders of magnitude improvement in signal quality relative to polycrystalline powder samples and suggests exciton diffusion over longer length scales than those obtained through previous approaches. These findings will enable a broad exploration of synthetic 2D polymer structures and properties.
Evans, Austin M. (ORCID:0000000235972454)↗
Zeolitic imidazolate framework membranes made by ligand-induced permselectivation
Zeolitic imidazolate framework (ZIF) membranes are emerging as a promising energy-efficient separation technology. However, their reliable and scalable manufacturing remains a challenge. We demonstrate the fabrication of ZIF nanocomposite membranes by means of an all-vapor-phase processing method based on atomic layer deposition (ALD) of ZnO in a porous support followed by ligand-vapor treatment. After ALD, the obtained nanocomposite exhibits low flux and is not selective, whereas after ligand-vapor (2-methylimidazole) treatment, it is partially transformed to ZIF and shows stable performance with high mixture separation factor for propylene over propane (an energy-intensive high-volume separation) and high propylene flux. Membrane synthesis through ligand-induced permselectivation of a nonselective and impermeable deposit is shown to be simple and highly reproducible and holds promise for scalability.
Science & Technology - Other Topics↗
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.
Bayesian inverse problem↗
QASMTrans: A QASM Quantum Transpiler Framework for NISQ Devices
In quantum computing, transpilation plays a crucial role in converting high-level, machine-independent quantum circuits into circuits specially for a quantum device, considering factors such as basis gate set, topology, error profile, etc. Yet, the efficiency of transpilation remains a significant bottleneck, particularly when dealing with very large QASM level input files. In this paper, we present QASMTrans, a C++ based high-performance quantum transpiler framework that can demonstrate on average 50-100× speedups compared to the internal transpiler of Qiskit. Particularly, for large dense circuits such as ’uccsd n24’ and ’qft n320’ incorporating millions of gates, QASMTrans can successfully transpile in 69s and 31s, respectively, while Qiskit failed to finish in one hour. Using QASMTrans as the baseline, it becomes more feasible to explore much larger design space and impose more comprehensive compiler optimizations.
Hua, Fei↗
Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
AI/ML↗
Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency
This paper develops and utilizes an optimization based framework to investigate the maximal energy efficiency potentially attainable by HVAC system operation in a non-predictive context. Performance is evaluated relative to the existing state of the art set-point reset strategies. The expected efficiency increase driven by operation constraints relaxations is evaluated.
Ramachandran, Thiagarajan↗
IBPSA Project 2 BOPTEST: An update on the test cases available in the framework for testing advanced control strategies in buildings
Project 2 develops software infrastructure, test cases, and extensions for the Building Optimization Testing Framework (BOPTEST) to address the expanding needs of building and urban energy system controls through open international collaboration. This paper provides an overview of the new test cases available as of BOPTEST version 0.7.1. Each test case is developed using open-source Modelica libraries and Spawn of EnergyPlus, enabling the creation of high-fidelity building models that incorporate envelope dynamics, Heating Ventilation and Air Conditioning (HVAC) systems, and explicit control representations. Currently, eight test cases are available, with five additional cases under development. These test cases cover a wide range of climates, building types, and HVAC systems. This paper compiles and summarizes test case descriptions, cites original manuscripts that developed them for a more detailed description, and reports baseline control performance metrics. Furthermore, two example applications are presented: one illustrating different levels of control, from supervisory to low-level, and another demonstrating how Model Predictive Control (MPC) solutions must be adapted from continuous to integer to control some building actuators.
Zanetti, Ettore↗
MATLAB/Simulink Framework for Modeling Complex Coolant Flow Configurations of Advanced Automotive Thermal Management Systems
The National Renewable Energy Laboratory's (NREL's) CoolSim MATLAB/Simulink modeling framework was extended by including a newly developed coolant loop solution method aimed at reducing the simulation effort for arbitrarily complex thermal management systems. The new approach does not require the user to identify specific coolant loops and their flow. The user only needs to connect the fluid network elements in a manner consistent with the desired schematic. Using the new solution method, a model of NREL's advanced combined coolant loop system for electric vehicles was created that reflected the test system architecture. This system was built using components provided by the MAHLE Group and included both air conditioning and heat pump modes. Validation with test bench data and verification with the previous solution method were performed for 10 operating points spanning a range of ambient temperatures between -2 degrees C and 43 degrees C. The largest root mean square difference between pressure, temperature, energy and mass flow rate data and simulation results was less than 7%.
30 DIRECT ENERGY CONVERSION↗
A Framework for Benchmarking Land Models
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Luo, Yiqi↗
Framework Programmable Platform for the Advanced Software Development Workstation (FPP/ASDW). Demonstration framework document. Volume 1: Concepts and activity descriptions
The Framework Programmable Software Development Platform (FPP) is a project aimed at effectively combining tool and data integration mechanisms with a model of the software development process to provide an intelligent integrated software development environment. Guided by the model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated. The Advanced Software Development Workstation (ASDW) program is conducting research into development of advanced technologies for Computer Aided Software Engineering (CASE).
Mayer, Richard J.↗
Negative Thermal Expansion Design Strategies in a Diverse Series of Metal–Organic Frameworks
Abstract not provided
Burtch, Nicholas C.↗
A Redox-Active 2D Metal-Organic Framework for Efficient Lithium Storage with Extraordinary High Capacity
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99 GENERAL AND MISCELLANEOUS↗
Unveiling the Effects of Linker Substitution in Suzuki Coupling with Palladium Nanoparticles in Metal–Organic Frameworks [Unveiling the Effects of Linker Substitution in Suzuki Coupling Reaction with Palladium Nanoparticles in Metal–Organic Frameworks]
The establishment of structure–property relationships in heterogeneous catalysis is of prime importance but remains a formidable challenge. Metal–organic frameworks (MOFs) featuring excellent chemical tunability are emerging as an auspicious platform for the atomic-level control of heterogeneous catalysis. Herein, we encapsulate palladium nanoparticles (Pd NPs) in a series of isoreticular mixed-linker MOFs, and the obtained MOF-Pd NPs catalysts were used to unveil the electronic and steric effects of linker substitution on the activity of these catalysts in the Suzuki–Miyaura cross-coupling reactions. Significantly, m-6,6'-Me2bpy-MOF-Pd exhibits a remarkable enhancement in the activity compared to non-functionalized m-bpy-MOF-Pd and m-4,4'-Me 2 bpy-MOF-Pd. This study unambiguously demonstrates that the stereoelectronic properties of linker units are crucial to the catalytic activity of nanoparticles encapsulated in MOFs. More interestingly, the trend of activity change is consistent with our previous work on catalytic sites generated in situ from Pd(II) coordinated in MOFs bearing the same functional groups, which suggests that both MOF-Pd NPs and MOF-Pd(II) catalysts generate similar active centers during Suzuki–Miyaura coupling reactions. Lastly, this work paves a new avenue to the fabrication of advanced and tunable MOF-based catalysts through rational linker engineering.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗