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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 433 records · Page 24

JEDI: Jobs and Economic Development Impact Model; NREL (National Renewable Energy Laboratory)

The Jobs and Economic Development Impact (JEDI) models are user-friendly tools that estimate the economic impacts of constructing and operating power generation and biofuel plants at the local (usually state) level. First developed by NREL’s researchers to model wind energy jobs and impacts, JEDI has been expanded to also estimate the economic impacts of biofuels, coal, conventional hydro, concentrating solar power, geothermal, marine and hydrokinetic power, natural gas, photovoltaics, and transmission lines. This fact sheet focuses on JEDI for wind energy projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Here, we discuss the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Lastly, initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.

42 ENGINEERING↗

Development of Models to Simulate Tracer Tests for Characterization of Enhanced Geothermal Systems

A recent report found that power and heat produced from enhanced (or engineered) geothermal systems (EGSs) could have a major impact on the U.S energy production capability while having a minimal impact on the environment. EGS resources differ from high-grade hydrothermal resources in that they lack sufficient temperature distribution, permeability/porosity, fluid saturation, or recharge of reservoir fluids. Therefore, quantitative characterization of temperature distributions and the surface area available for heat transfer in EGS is necessary for the design and commercial development of the geothermal energy of a potential EGS site. The goal of this project is to provide integrated tracer and tracer interpretation tools to facilitate this characterization. This project was initially focused on tracer development with the application of perfluorinated tracer (PFT) compounds, non-reactive tracers used in numerous applications from atmospheric transport to underground leak detection, to geothermal systems, and evaluation of encapsulated PFTs that would release tracers at targeted reservoir temperatures. After the 2011 midyear review and subsequent discussions with the U.S. Department of Energy Geothermal Technology Program (GTP), emphasis was shifted to interpretive tool development, testing, and validation. Subsurface modeling capabilities are an important component of this project for both the design of suitable tracers and the interpretation of data from in situ tracer tests, be they single- or multi-well tests. The purpose of this report is to describe the results of the tracer and model development for simulating and conducting tracer tests for characterizing EGS parameters.

EGS↗

Building America Case Study: Predicting Envelope Leakage in Attached Dwellings (Fact Sheet)

'The cost for blower testing is high, because it is labor intensive, and it may disrupt occupants in multiple units. This high cost and disruption deters program participants, and dissuades them from pursuing energy improvements that would trigger air leakage testing, such as improvements to the building envelope.' This statement found in a 2012 report by Heschong Mahone Group emphasizes the importance of reducing the cost and complexity of blower testing in multifamily buildings. Energy efficiency opportunities are being bypassed. The cost of single blower testing is on the order of $300. The cost for guarded blower door testing, the more appropriate test for assessing energy savings opportunities, could easily be six times that and that's only if you have the equipment and simultaneous access to multiple apartments. Thus, the proper test is simply not performed. The objective of the 2013 research project was to develop the model for predicting fully guarded test results (FGT), using unguarded test data and specific building features of apartment units. The model developed has a coefficient of determination R2 value of 0.53 with a root mean square error (RMSE) of 0.13. Both statistical metrics indicate that the model is relatively strong. When tested against data that was not included in the development of the model, prediction accuracy was within 19%, which is reasonable given that seasonal differences in blower door measurements can vary by as much as 25%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Rate expressions and kinetic parameters for metal ferrites in relation to applications of fossil fuel conversion to hydrogen: Part 1 of 2

Here, the goal of the present work was to provide the necessary reaction emulation information to enable detailed process simulation of a chemical looping H 2 production system from fossil fuels using CaFe 2 O 4 . This specifically pertained to the necessary kinetic data, reaction model development, and model rate parameters required for reaction emulation in both reducing and oxidizing environments. A logical methodology was defined, which included discretization of the reaction network, establishing a core model for reaction emulation that could be adapted based on the system phenomena, and development of a rate parameter regression tool designed around the core model. An extensive array of data sets was acquired by which parametric regressions were performed. The work presented and tabulated a comprehensive set of rate parameters for the reduction and oxidation reactions of CaFe 2 O 4 and descendent phases of Ca 2 Fe 2 O 5 , FeO, Fe 3 O 4 , Fe, and CaO to emulate reaction behavior in a looping-based process environment. This included direct reduction using CH 4 , H 2 , and CO, and direct oxidation reactions with steam, CO 2 and O 2 . Dynamic equilibrium was quantified for reactions that could utilize H 2 O and CO 2 as soft oxidants to re-saturate lattice oxygen in the depleted structure/phases. The kinetics associated with the oxidative mechanisms with the soft oxidants were quantified and compared to those of the reducing counterparts. The analysis provided critical insight to emulate reactions for a process that seeks to use natural gas (NG) or other fossil fuels as a direct reductant for the end goal of H 2 production.

calcium ferrite oxygen carriers↗

Gasification Characteristics of Coal/Biomass Mixed Fuels

A research project was undertaken that had the overall objective of developing the models needed to accurately predict conversion rates of coal/biomass mixtures to synthesis gas under conditions relevant to a commercially-available coal gasification system configured to co-produce electric power as well as chemicals and liquid fuels. In our efforts to accomplish this goal, experiments were performed in an entrained flow reactor in order to produce coal and biomass chars at high heating rates and temperatures, typical of the heating rates and temperatures fuel particles experience in real systems. Mixed chars derived from coal/biomass mixtures containing up to 50% biomass and the chars of the pure coal and biomass components were subjected to a matrix of reactivity tests in a pressurized thermogravimetric analyzer (TGA) in order to obtain data on mass loss rates as functions of gas temperature, pressure and composition as well as to obtain information on the variations in mass specific surface area during char conversion under kinetically-limited conditions. The experimental data were used as targets when determining the unknown parameters in the chemical reactivity and specific surface area models developed. These parameters included rate coefficients for the reactions in the reaction mechanism, enthalpies of formation and absolute entropies of adsorbed species formed on the carbonaceous surfaces, and pore structure coefficients in the model used to describe how the mass specific surface area of the char varies with conversion. So that the reactivity models can be used at high temperatures when mass transport processes impact char conversion rates, Thiele modulus – effectiveness factor relations were also derived for the reaction mechanisms developed. In addition, the reactivity model and a mode of conversion model were combined in a char-particle gasification model that includes the effects of chemical reaction and diffusion of reactive gases through particle pores and energy exchange between the particle and its environment. This char-particle gasification model is capable of predicting the average mass loss rates, sizes, apparent densities, specific surface areas, and temperatures of the char particles produced when co-firing coal and biomass to the type environments established in entrained flow gasifiers operating at high temperatures and elevated pressures.

01 COAL, LIGNITE, AND PEAT↗

Prediction of softening kinetics and recrystallization texture in non-isothermally annealed bulged tubes using CPFEM and CA models

A hierarchically coupled cellular automata (CA) model, crystal plasticity finite element method (CPFEM), and thermal finite element (FE) model is developed to predict the softening kinetics of the bulged steel tube during non-isothermal annealing. Through the developed model, the kinetics of softening mechanisms including static recovery (SRV) and static recrystallization (SRX), as well as the recrystallization texture are predicted. Later, the Johnson-Mehl-Avrami-Kohnogorov (JMAK) model based on the predicted SRX data is developed to interpret the recrystallization behavior of the material. To perform this study, diverse experimental tests including tube hydroforming (THF), annealing, uniaxial tensile test, hardness test, as well as microstructure observations through optical microscopy and Electron Backscatter Diffraction (EBSD) tests on steel tube are performed. The obtained experimental data are utilized to calibrate and verify the implemented CPFEM model for simulation of THF process, thermal FE model for prediction of the local temperature over annealing time, and CA algorithm for modeling of the softening kinetics and texture evolution throughout the annealing process. The study shows that the predicted deformation characteristics, softening kinetics, recrystallization texture and temperature profile during non-isothermal annealing are in good agreement with experimental data. During the annealing process, a total of four stages for the kinetics of softening mechanisms is observed: No softening; SRV only; SRV dominant; and SRX dominant. During the progress of SRX, the behavior of recrystallization is abruptly changed, confirming that two different mechanisms are controlling the kinetics of transformation.

36 MATERIALS SCIENCE↗

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗

High Performance PEFC Electrode Structures

Raytheon Technologies Research Center (RTRC) in collaboration with Ion Power Inc. and the University of Arkansas at Little Rock and the FC-PAD consortium executed a project that: (1) developed comprehensive models of oxygen and proton transport in cathode catalyst layers that provided new insights about losses associated with reducing platinum loading to 0.1 mg/cm 2 , (2) designed, fabricated, characterized, and tested cells featuring carbon-supported catalyst layers with low platinum loadings that met efficiency and power density targets set forth by the Hydrogen and Fuel Cells Technology Office, and (3) designed, fabricated, characterized, and tested novel catalyst structures. The modeling efforts produced eight scientific publications that shared new descriptions of oxygen and proton transport in catalyst layers with the broader technical community. The key physical insights from the models are expressed in straightforward algebraic expressions, facilitating easy adoption by other researchers. The models developed during the project explain why resistance to oxygen transport is inversely proportional to interfacial area of platinum – an observation that eluded quantitative explanation for a decade. The models quantitatively predict polarization when measured morphological parameters and transport properties are input. Membrane-electrode assemblies made during the project met stated goals for current at high voltage and power density at rated voltage for transportation fuel cells at the end of budget period 2. High performance was achieved by developing a diffusion layer with low resistance to oxygen transport and a PtCo/C cathode catalyst layer capable of supporting rapid proton and oxygen transport. The project was subsequently directed to curtail experimental work and focus on modeling during budget period 3. Ion Power Inc. developed new expertise in fabricating catalyst layers containing platinum-cobalt catalysts, and processing very thin membranes. The University of Arkansas at Little Rock developed novel nanocolumnar self-supported thin film electrocatalysts that can be grown on carbon supports using a simple high pressure sputter deposition method. The nanocolumnar microstructure provides adequate surface-to-volume ratio for efficient platinum utilization, and the conformal platinum shell with larger crystal grain sizes covering the carbon support surface may eliminate durability issues associated with catalyst dissolution, agglomeration, and carbon corrosion.

08 HYDROGEN↗

Stability of Fissile Solution Systems

A technique has been developed to model the dynamics of fissile solution systems. This technique, which can be characterized as Dynamic System Simulation (DSS), is a set of coupled nonlinear differential equations that may be solved in the time domain to simulate the dynamic behavior of the overall system. The details of model development and results have previously been documented, hence, herein only a summary is presented. Herein an extension of the modeling technique is described that uses output of the simulation models to examine stability. Simplified versions of the simulation model have been linearized and transformed into the complex plane. Transfer functions for each of the system components were developed and combined to generate overall system closed- and open-loop transfer functions. Standard linear stability analysis techniques, such as frequency response and pole configuration, were used to evaluate the stability of the system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Design and Testing of a Liquid Nitrous Oxide and Ethanol Fueled Rocket Engine

A small-scale, bi-propellant, liquid fueled rocket engine and supporting test infrastructure were designed and constructed at the Energetic Materials Research and Testing Center (EMRTC). This facility was used to evaluate liquid nitrous oxide and ethanol as potential rocket propellants. Thrust and pressure measurements along with high-speed digital imaging of the rocket exhaust plume were made. This experimental data was used for validation of a computational model developed of the rocket engine tested. The developed computational model was utilized to analyze rocket engine performance across a range of operating pressures, fuel-oxidizer mixture ratios, and outlet nozzle configurations. A comparative study of the modeling of a liquid rocket engine was performed using NASA CEA and Cantera, an opensource equilibrium code capable of being interfaced with MATLAB. One goal of this modeling was to demonstrate the ability of Cantera to accurately model the basic chemical equilibrium, thermodynamics, and transport properties for varied fuel and oxidizer operating conditions. Once validated for basic equilibrium, an expanded MATLAB code, referencing Cantera, was advanced beyond CEAs capabilities to predict rocket engine performance as a function of supplied propellant flow rate and rocket engine nozzle dimensions. Cantera was found to comparable favorably to CEA for making equilibrium calculations, supporting its use as an alternative to CEA. The developed rocket engine performs as predicted, demonstrating the developedMATLAB rocket engine model was successful in predicting real world rocket engine performance. Finally, nitrous oxide and ethanol were shown to perform well as rocket propellants, with specific impulses experimentally recorded in the range of 250 to 260 seconds.

33 ADVANCED PROPULSION SYSTEMS↗

Development and Validation of Accident Models for FeCrAl Cladding

The purpose of this milestone report is to present the work completed in regards to material model development for FeCrAl cladding and highlight the results of applying these models to Loss of Coolant Accidents (LOCA) and Station Blackouts (SBO). With the limited experimental data available (essentially only the data used to create the models) true validation is not possible. In the absence of another alternative, qualitative comparisons during postulated accident scenarios between FeCrAl and Zircaloy-4 cladded rods have been completed demonstrating the superior performance of FeCrAl.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528–0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models’ applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

Research & Experimental Medicine↗

Development and Validation of Accident Models for U 3 Si 2

The purpose of this milestone report is to present the work completed in regards to material model development for U 3 Si 2 fuel and highlight the results of applying these models to Reactivity Initiated Accidents (RIA), Loss of Coolant Accidents (LOCA), and Station Blackouts (SBO). With the limited experimental data available (essentially only the data used to create the models) true validation is not possible. In the absence of another alternative, code-to-code comparisons have been completed. Qualitative comparisons during postulated accident scenarios between U 3 Si 2 and UO 2 fueled rods have also been completed demonstrating the superior performance of U 3 Si 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Charging and Heating Dynamics of Nanoparticles in Nonthermal Plasmas

The focus of this award was to understand the interactions of nanometer-sized particles with ionized gases, also called plasmas. Plasmas are widely used in the fabrication of electronic circuits such as microprocessors and memory devices, in plasma display panels, as well as in medical applications. Recently, these ionized gases are finding applications in the synthesis of advanced nanomaterials with novel properties, which are based on nanometer-sized particulate (nanoparticles) building blocks. As these nanoparticles grow in the plasma environment, they interact with the plasmas species such as electrons and ions which critically determines the nanoparticle properties. The University of Minnesota researchers conducting this project performed numerical simulations and developed analytical models that described the interaction of plasma-bound nanoparticles with the plasma ions. The plasma ions bombard the nanoparticle surface with substantial energy, which can result in the rearrangement of the nanoparticles’ atoms, giving them often desirable structures at the atomic scale. Being able to tune the ion energies allows to control the properties of nanoparticles produced in order to tailor their attributes for certain applications. For instance, when used in high efficiency light emitting devices, nanoparticles produced under high fluxes of highly energetic ions may show superior light emission to particles produced under low fluxes of less energetic ions. The analytical models developed by the University of Minnesota researchers enable the research community to easily determine the energy of ions bombarding the nanoparticles. The researchers extensively tested the validity of the analytical models by comparing them to sophisticated computer simulations based on stochastic particle modeling, also called Monte Carlo modeling, which simulated the motion of hundreds of thousands of ions and their interaction with the nanoparticle surfaces. Beyond the scientific intellectual merits, this award had significant broader impacts. Two graduate students received their doctoral degrees and both have joined a U.S. manufacturer of plasma-based semiconductor processing equipment. Four undergraduate students participated in research conducted under this grant and gained valuable hands-on laboratory experience. A middle school science teacher observed research conducted under this grant and developed three new course modules that introduce middle school students to the concepts of nanometer scale, the atomic structure of matter, and the composition of matter of different chemical elements.

42 ENGINEERING↗

Development and Validation of MALAMUTE model for Electric Field Assisted Sintering of Structural Materials

Fusion power plant designs feature extreme material performance requirements for structural material candidates. In addition to conventional alloys, more advanced composites and oxide dispersion strengthened (ODS) alloys are being explored, however, achieving the desired microstructures to maximize performance using traditional manufacturing methods can be challenging. The advanced manufacturing (AM) electric field-assisted sintering (EFAS) technique offers improved control over the final microstructure through higher heating and cooling rates and moderate pressures. Modeling and simulation tools show promise in elucidating the process-structure-property-performance (PSPP) correlation for AM-produced parts, including the EFAS process. An inherently multiscale process, the EFAS technique aligns well with the multiscale modeling capability of the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE)[cite]. We present here an electro-thermo-mechanical approach to modeling the EFAS process using the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) code. Prediction of the field and gradient distributions across the EFAS tooling is required to accurately describe the conditions for the lower-scale microstructural evolution models. In this work we present the MALAMUTE model developed to predict the electrical potential, temperature, and mechanical stress distribution across the EFAS graphite tooling and part at the larger engineering-scale. Validation of the MALAMUTE engineering-scale model is completed using data from experimental densification and pre-densified runs of iron powder via EFAS at 1000oC. These runs were conducted using a Thermal Technology DCS-5 EFAS system. Data collected during the experiment runs include the direct current (DC) supplied to the graphite tooling, the temperature of the graphite tooling as measured with a pyrometer, and the force applied to the top of the graphite tooling stack, and the data were recorded every 10 seconds. Our validation approach used the current and force data from the EFAS run as boundary condition inputs to the MAMALUTE simulation; the temperature data were used to evaluate the MALAMUTE EFAS model prediction. Results of the MALAMUTE simulations are employed to connect the external pyrometer temperature measurement to the temperature profile across the part undergoing consolidation. We investigate the impact of material property variation and mesh deformation on the temperature profile as predicted by MALAMUTE. We conclude by highlighting projects where the MALAMUTE EFAS modeling and simulation capabilities will be used to assist experimental design.

36 - MATERIALS SCIENCE↗

Development of a New Chelation Model: Bioassay Data Interpretation and Dose Assessment after Plutonium Intake via Wound and Treatment with DTPA

The administration of chelation therapy to treat significant intakes of actinides, such as plutonium, affects the actinide’s normal biokinetics. In particular, it enhances the actinide’s rate of excretion, such that the standard biokinetic models cannot be applied directly to the chelation-affected bioassay data in order to estimate the intake and assess the radiation dose. Here we propose a new chelation model that can be applied to the chelation-affected bioassay data after plutonium intake via wound and treatment with DTPA. In the proposed model, chelation is assumed to occur in the blood, liver, and parts of the skeleton. Ten datasets, consisting of measurements of 14 C-DTPA, 238 Pu, and 239 Pu involving humans given radiolabeled DTPA and humans occupationally exposed to plutonium via wound and treated with chelation therapy, were used for model development. The combined dataset consisted of daily and cumulative excretion (urine and feces), wound counts, measurements of excised tissue, blood, and post-mortem tissue analyses of liver and skeleton. The combined data were simultaneously fit using the chelation model linked with a plutonium systemic model, which was linked to an ad hoc wound model. The proposed chelation model was used for dose assessment of the wound cases used in this study.

60 APPLIED LIFE SCIENCES↗