SEARCH · Engineering Papers

Results for “integration study”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Is the Adoption of Electric Vehicles (EVs) and Solar Photovoltaics (PVs) Interdependent or Independent? An Integrated EVs-PVs Modeling Framework

Transportation and residential energy consumption account for more than one-half of the overall energy consumption in the United States. Adoption of electric vehicles (EVs) can play a key role in decarbonizing the transportation sector, while the adoption of renewable energy sources (e.g., solar photovoltaics or PVs) could bring similar benefits to the residential energy sector and in turn support transport electrification. Although the market share for both EVs and PVs continue to grow, both these emerging technologies are deployed rather disjoint without considering the existence of potential similarities among users who own (or aspire to own) EVs and PVs. This might be due to lack of understanding of the behavioral interdependence in consumer preferences towards these technologies. To fill this gap in knowledge, this study utilizes data from the 2018 WholeTraveler Transportation Behavior Study to develop an integrated model system that explores EV and PV adoption behaviors. A structural equations model (SEM) is employed that incorporates direct effects as well as error correlations among the adoption behaviors for EVs and PVs. Model results indicate that the adoption behavior for both these technologies is indeed interconnected and significantly influenced by attitudes, values and personality traits. Findings from this research suggest that incentives (e.g., subsidies) that drive 'bundled' adoption of EVs and PVs could accelerate the adoption of both these sustainable technologies. The results highlights the need to consider transport and building energy efficient technology adoption behavior in a single integrated structure.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2024

Zeolite‐Based Catalysts for Conversion of Oxygenated Polymer Waste by Positron Annihilation

Positron Annihilation Lifetime Spectroscopy (PALS) has been employed to investigate the catalysts HZSM-5 and MESO−Y, which play a pivotal role in catalyzing and upgrading plastics, with a primary focus on oxygenated polymers, thereby transforming existing plastic materials into simpler, higher-quality value-added products. Here, in this study, PALS was systematically compared with other complementary analytical techniques. The research outcomes have successfully demonstrated the efficacy of PALS in elucidating the morphology and topology of zeolites at micro/meso-meter scales. The first experiment focuses on H-ZSM-5 zeolite subjected to treatments involving polyurethane and polypropylene. The second experiment delves into H-ZSM-5 zeolites with varying Si/Al ratios, both before and after conversion. The third experiment investigates Y zeolites that are surfactant templated to induce meso-porosity, examining their fresh state as well as their post-conversion condition. The PALS analysis was supplemented by BET (Brunauer-Emmett-Teller) analysis and NMR (Nuclear Magnetic Resonance) spectroscopies. Notably, PALS exhibits superior sensitivity, at the sub-nanometer scale, suggesting its potential as a preferred complementary method for catalysis studies. In conclusion, the integration of PALS into the repertoire of analytical tools enhances our understanding of catalyst behavior and catalytic processes, offering valuable insights for the advancement of plastic recycling and catalysis research.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

DOE OSTI2024

Modeling the impact of measured and projected climate and management systems on agricultural fields: Surface runoff, soil moisture, and soil erosion

Abstract As global climate change poses a challenge to crop production, it is imperative to prioritize effective adaptation of agricultural systems based on a scientific understanding of likely impacts. In this study, we applied an integrated watershed modeling framework to examine the impacts of projected climate on runoff, soil moisture, and soil erosion under different management systems in Central Oklahoma. The proposed model uses measured climate data and three downscaled ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at the water resources and erosion watershed to understand the impact of climate change and various climate conditions under three management systems: (1) continuous winter wheat (Triticum aestivum) under conventional tillage (WW‐CT; baseline system), (2) continuous winter wheat under no‐till (WW‐NT), and (3) cool and warm season forage cover crop mixes under no‐till (CC‐NT). The study indicates that the occurrence of agricultural drought is projected to increase while erosion rates will remain unchanged under the WW‐CT. In contrast, climate simulations imposed on the WW‐NT and CC‐NT systems significantly reduce runoff and sediment while preserving soil moisture levels. Especially, implementing the CC‐NT system can bolster food security and foster sustainable farming practices in Central Oklahoma in the face of a changing climate.

Environmental Sciences & Ecology↗

DOE OSTI2024

The center for nonlinear studies: A personal history

The Center for Nonlinear Studies (CNLS) was an integral part of my scientific career starting as a Postdoctoral Fellow in 1983 up to my tenure as CNLS Director from 2004 to 2015. As such, I experienced a number of scientific phases of CNLS through almost four decades of foundation, evolution, and transition. Throughout this entire interval, the inspiration and influence of David Campbell guided my way. A proper history of CNLS encompassing all of the many contributors to the CNLS story is beyond my means or purpose here. Instead, I present the history as I experienced it. I emphasize the main scientific accomplishments achieved at CNLS over more than 40 years, but I will also attempt to describe and quantify the attributes that made and continue to make the Center for Nonlinear Studies a special institution of remarkable impact and longevity. Throughout its existence, CNLS owes much to the enduring legacy of David Campbell who laid down the foundations and operating principles that have made it so successful.

Ecke, Robert E. (ORCID:0000000177725876)↗

DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

BISON Capability to Account for Dopant Sensitivity in Relevant UO 2 Material Models

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program aims to develop predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity and high-resolution fuel performance tool at the engineering scale. Incorporation of more physics-based models in BISON for the accident tolerant fuel applications motivated this study. This document details integration of new modeling capabilities in BISON, including: a tensile strength model for uranium dioxide (UO 2 ) fuel to incorporate the microstructural effects (e.g., grain size, fabrication pore size, and porosity), and atomistic-informed creep model for UO 2 fuel that is developed by Los Alamos National Laboratory. Sensitivity analyses are conducted on these models separately as well as a two-dimensional full rod application under normal operating conditions. Lastly, these new modeling capabilities in BISON are exercised in Halden IFA-677.1 and IFA-716.1 assessment cases.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Extracting dynamical frequencies from invariants of motion in finite-dimensional nonlinear integrable systems

Integrable dynamical systems play an important role in many areas of science, including accelerator and plasma physics. An integrable dynamical system with n degrees of freedom possesses n nontrivial integrals of motion, and can be solved, in principle, by covering the phase space with one or more charts in which the dynamics can be described using action-angle coordinates. To obtain the frequencies of motion, both the transformation to action-angle coordinates and its inverse must be known in explicit form. However, no general algorithm exists for constructing this transformation explicitly from a set of n known (and generally coupled) integrals of motion. In this study we describe how one can determine the dynamical frequencies of the motion as functions of these n integrals in the absence of explicitly known action-angle variables, and we provide several examples.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Performance of explicit and IMEX MRI multirate methods on complex reactive flow problems within modern parallel adaptive structured grid frameworks

Large-scale multiphysics simulations are computationally challenging due to the coupling of multiple processes with widely disparate time scales. The advent of exascale computing systems exacerbates these challenges since these systems enable ever-increasing size and complexity. In recent years, there has been renewed interest in developing multirate methods as a means to handle the large range of time scales, as these methods may afford greater accuracy and efficiency than more traditional approaches of using implicit-explicit (IMEX) and low-order operator splitting schemes. However, to date there have been few performance studies that compare different classes of multirate integrators on complex application problems. In this work, we study the performance of several newly developed multirate infinitesimal (MRI) methods, implemented in the SUNDIALS solver package, on two reacting flow model problems built on structured mesh frameworks. The first model revisits prior work on a compressible reacting flow problem with complex chemistry that is implemented using BoxLib but where we now include comparisons between a new explicit MRI scheme with the multirate spectral deferred correction (SDC) methods in the original paper. The second problem uses the same complex chemistry as the first problem, combined with a simplified flow model, but runs at a large spatial scale where explicit methods become infeasible due to stability constraints. Two recently developed IMEX MRI multirate methods are tested. These methods rely on advanced features of the AMReX framework on which the model is built, such as multilevel grids and multilevel preconditioners. The results from these two problems show that MRI multirate methods can offer significant performance benefits on complex multiphysics application problems and that these methods may be combined with advanced spatial discretization to compound the advantages of both.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Evaluating the Impact of Tidal Energy in the Cook Inlet on Alaska's Railbelt Electrical Grid

This report presents the findings of a case study that evaluates the impact of integrating significant tidal energy generation in the Cook Inlet in Alaska. The case study is part of a series within the "Quantifying the Grid Value of MRE [Marine Renewable Energy] in Early U.S. Markets" project funded by the U.S. Department of Energy. This study takes a scenario-based approach to evaluate the tidal energy potential in the Cook Inlet, in which 100-500 megawatts (MW) of tidal energy are integrated into the grid under different infrastructure scenarios. These scenarios include increased energy storage and transmission line upgrades, a "Basecase" scenario with no additional upgrades, and a reference case with no tidal energy. We concluded that tidal energy at an installed capacity of 200-300 MW has the potential to reduce fuel costs in Alaska while also reducing carbon emissions and increasing the energy independence of the state. This analysis and the key findings should be viewed as a starting point for additional research and used to inform investment and policy options.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗

DOE OSTI2024

Demonstration of the carbon capture with building make-up air unit

Building-integrated carbon capture technology has the potential to reduce the cost of CO 2 capture while improving indoor air quality (IAQ). To promote the adoption of CO 2 capture in a building environment, this study investigated the possibility of integrating carbon capture technology with an existing rooftop make-up air unit (MAU) system to trap CO 2 . Here, in this study, a modular compact CO 2 capture system containing amine-functionalized polymer fibers was examined. The system, which was installed at the exhaust of the MAU, captures CO 2 before it leaves the building to enter the atmosphere as a greenhouse gas. The demonstrated average amount of CO 2 captured was 1.1–1.4 mmol/g of adsorbent material. Techno-economic analysis (TEA) was further performed on the CO 2 capture system, considering material costs, energy costs, as well as transportation and regeneration costs. These results were then used to estimate the levelized cost per ton CO 2 captured (LCOC). To achieve LCOC below $\$$100/t-CO 2 , adsorbents should have working capacities of 4.9 t and 3 t-CO 2 /year for 5 years and 10 years of operation, respectively. In summary, this study highlights a viable path toward the decarbonization of the commercial buildings sector and provides quantitative performance and economic insight on the suitability of building-integrated carbon capture technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Non-Reciprocity, Metastability, and Dynamic Reconfiguration in Co-Assembly of Active and Passive Particles

Living organisms often exhibit non-reciprocal interactions where the forces acting on the objects are not equal in magnitude or opposite in direction. The combination of reciprocal and non-reciprocal interactions between synthetic building blocks remains largely unexplored. Here, out-of-equilibrium assemblies of non-motile isotropic passive and metal-patched motile active particles are formed by overlapping bulk interactions with directed self-propulsion. An external alternating current (AC) electric field generates concurrent dipolar and induced-charge electrophoretic forces between the particles which are evaluated using microscopy. The interaction force measurements allow to determine the degree of reciprocity in interactions, which is tunable by designing the active particle and its trajectory. While linearly-propelled active particles evade assembly with passive particles, helically propelled active particles form active-passive clusters with dynamic reconfiguration and long-lived metastability. Large clusters display programmable fluctuations and reconfigurability by controlling the fraction of active particles. The study establishes principles of integrating reciprocal and non-reciprocal interactions in guided colloidal assembly of reconfigurable metastable structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Chiral limit of 2d QCD revisited with lightcone conformal truncation

We study the chiral limit of 2d QCD with a single quark flavor at finite Nc using LCT. By modifying the LCT basis according to the quark mass in a manner motivated by ’t Hooft’s analysis, we are able to restore convergence for quark masses much smaller than the QCD strong coupling scale. For such small quark masses, the IR of the theory is expected to be well described by the Sine-Gordon model. We verify that LCT numerics are able to capture in detail the spectrum and correlation functions of the Sine-Gordon model. This opens up the possibility for studying deformations of various integrable CFTs using LCT by considering the chiral limit of QCD like theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

A detailed experimental and modeling comparison of molecular radiative heat loss in a spark-ignition engine

Radiative heat transfer has been chiefly considered negligible in internal combustion engines, except for Diesel engines where soot radiation was recognized as a significant radiative transfer source. Only more recently, detailed simulations have shown that molecular radiation can be substantial as well. In extension to this, molecular radiative heat transfer can reach detectable levels of about 5–10% of the total heat transfer in spark-ignited engines. The broadband radiative nature of the significant emitting molecules, carbon dioxide and water, makes it necessary to address whether radiative trapping plays a substantial role in either total heat loss or energy redistribution within the cylinder. An experimental setup that allows measurements of the infrared emissions at a 2-crank-angle-degree resolution was developed to determine the importance of radiative trapping by carbon dioxide and water. Measurements were conducted in the well-characterized and documented TCC-III engine at the University of Michigan. The engine operated on a stoichiometric propane/air mixture at a speed of 1,300 RPM. Large Eddy Simulations with added line-by-line photon Monte-Carlo simulations of the molecular radiation were an integral part of this study to plan and devise the measurements. Post-processing of the simulation data included an accurate representation of the experimental volume from where infrared signals are collected. Additionally, the experimental data were used for validation of the photon Monte-Carlo simulations. Joint analysis of experimental and simulated spectra allowed quantifying the significance of radiative trapping. Results show the significant role of radiative trapping when predicting radiative heat transfer in the TCC-III engine.

42 ENGINEERING↗

DOE OSTI2024

Impact of cement composition, brine concentration, diffusion rate, reaction rate and boundary condition on self-sealing predictions for cement-CO 2 systems

Geological CO 2 storage (GCS) plays an important role in curbing CO 2 emissions by reducing the carbon footprint of difficult to decarbonize operations and achieve negative CO 2 emissions through activities like Bioenergy with Carbon Capture and Storage (BECCS) and Direct Air Carbon Capture and Storage (DACCS). Leakage of CO 2 through wells is an important concern when it comes to deployment of large-scale GCS. Existing wells in sites that are otherwise suitable for GCS can act as conduits for stored CO 2 to escape the reservoir. There is broad consensus that the main risk of leakage through wellbores is via fractures/damaged pathways. The results from several studies evaluating the permeability evolution of cement fractures in wells upon leakage of CO 2 agree that smaller fracture apertures, slower brine velocities and higher brine residence times promote self-sealing of fractures by mineral precipitation. Quantitatively, however, the differences in sealing conditions are significant and are typically attributed to differences in experimental conditions or model assumptions. Here we examine the sensitivity of our model, describing CO 2 leakage through wellbores, to cement composition, brine concentration, diffusion rates, and reaction rates. We also evaluate the impact of the boundary condition to allow comparisons between observations from experiments performed at constant flow rate and model predictions made at constant pressure conditions. Our results show that diffusion and reactions rates have the most impact on the self-sealing criteria for cement-CO 2 systems. In addition, conditions associated with self-sealing of fractures at constant flow rate require longer fractures, smaller fracture apertures, and slower velocities than under constant pressure.

58 GEOSCIENCES↗