Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model based development”

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.

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

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of Numerical Model of Metal Foam with PCM for the Estimation of Effective Thermal Conductivity

Global warming due to climate change is a threat to humankind. Nuclear energy is one of the promising solutions to reduce fossil fuel usage. Nuclear energy can handle the base load, compensating for the volatility of renewable energy. If nuclear energy could achieve load following capability, the combination with renewable energy would be more suitable. Thermal energy storage (TES) is one of the options for enabling load following of nuclear reactors. The TES makes it possible to store surplus nuclear thermal energy and release it later as needed. In Idaho National Laboratory (INL), a new concept of latent heat TES integrated with high-temperature heat pipe has been proposed and is under development, which is called Heat pipe-Integrated Thermal Battery (HITB). HITB exchanges thermal energy between the reactor system and TES via heat pipe. The heat transferred to TES medium, made of phase change material (PCM), stores energy as sensible heat and/or latent heat. As PCM typically has poor thermal conductivity, however, various heat transfer enhancement techniques are required to achieve a rapid charging cycle. There are many techniques to enhance the heat transfer ability of TES medium such as disk, fin, and metal foam. Among them, metal foam is an appropriate option to enhance the heat transfer because it maximizes the heat transfer area through metal wicks. Metal foam is a lightweight metal structure that has a high porosity of over 0.9. The typical materials for metal foam are Aluminum, Copper, Nickel, and Silicon Carbide (SiC). Metal foam not only enhances heat transfer via conduction but also increases contact surface area. In the HITB design , the metal foam is being considered as one of the options to enhance the heat transfer of TES medium (PCM) [1]. To predict the enhanced thermal performance of TES, one should properly estimate the effective thermal conductivity of metal foam combined with PCM material or calculate heat transfer in distributed model. There are many experimental works that provides effective thermal conductivity of metal foam with various PCM [2,3]. Also, many theoretical models were developed based on the unit cell model of metal foam [4,5]. With a distributed model, on the other hand, detail heat transfer characteristics between metal foam and PCM material can be analyzed considering the geometry or buoyancy effect. However, due to the complex geometry of metal foam pores, the computational cost for three-dimensional modeling highly increases. Therefore, if metal foam structure can be modeled in simple and repetitive design, the computational cost would decrease Among the various metal foam models [2], lattice model is one of the simple and extendable design. The porosity and pores per inch (PPI) can be characterized by the size and spatial distance of lattice structure. If the three-dimensional metal foam model consists of lattice structure could properly estimate the heat transfer, which is characterized by effective thermal conductivity, it would be a good option to assess the thermal performance of metal foam with PCM. In this study, a three-dimensional numerical model was developed to simulate conductive heat transfer between metal foam and PCM. The three-dimensional lattice structure of square pillars was selected as a basic structure of the metal foam. The calculation result was characterized by the effective thermal conductivity of the whole domain. A sensitivity study was conducted for mesh size, domain size, and PPI to check whether the calculation result gives a converged result or not. Lastly, the effective thermal conductivity from the lattice model was compared with existing experimental data to validate the model result

25 ENERGY STORAGE↗
DOE OSTI2019

Development and Calibration of an Online Energy Model for AHU Fan

The model development is necessary for the study of the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. To predict the HVAC energy consumption accurately, one needs to model the individual HVAC components either from the measured data or based on the knowledge of the underlying physical phenomenon. Online model characterization is critical for improving the performance of real-time model-based fault detection and diagnosis (FDD) strategies. For HVAC control, models can be used to optimize the supervisory and local feedback control strategies to improve the energy consumption efficiency, or for providing ancillary services to the grid. It has been reported that, fans in HVAC systems of commercial buildings alone can provide substantial frequency regulation service, with little change in the indoor environment. In this paper, a real-time data-driven Air Handling Unit (AHU) fan model was developed based on recursive multi regression model. A generic nonlinear polynomial model has been studied to cover scenarios with different combinations of measurement variables, variable orders as well as different training and prediction horizons. Typical measurements including static pressure, mass flow rate, and damper positions are utilized as inputs to model the power consumption of the fan. The developed models have been validated both with simulation data from EnergyPlus-Dymola co-simulation model and with field measurement data for small to medium commercial buildings. The validation results show that the online model proposed can provide an effective prediction of the AHU fan power consumption.

Dong, Jin↗
DOE OSTI2013

Constitutive Modeling of High Temperature Uniaxial Creep-Fatigue and Creep-Ratcheting Responses of Alloy 617

Inconel Alloy 617 is a high temperature creep and corrosion resistant alloy and is a leading candidate for use in Intermediate Heat Exchangers (IHX) of the Next Generation Nuclear Plants (NGNP). The IHX of the NGNP is expected to experience operating temperatures in the range of 800 degrees - 950 degrees C, which is in the creep regime of Alloy 617. A broad set of uniaxial, low-cycle fatigue, fatigue-creep, ratcheting, and ratcheting-creep experiments are conducted in order to study the fatigue and ratcheting responses, and their interactions with the creep response at high temperatures. A unified constitutive model developed at North Carolina State University is used to simulate these experimental responses. The model is developed based on the Chaboche viscoplastic model framework. It includes cyclic hardening/softening, strain rate dependence, strain range dependence, static and dynamic recovery modeling features. For simulation of the alloy 617 responses, new techniques of model parameter determination are developed for optimized simulations. This paper compares the experimental responses and model simulations for demonstrating the strengths and shortcomings of the model.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Development of a functional thyroid model based on an organoid culture system

Highlights: • We developed a culture system for murine thyroid organoids. • TSH stimulated thyroid organoid formation and growth. • TSH-responsive thyroid organoids formed functional thyroid-like tissue in vivo. • NRAS{sup Q61R}-positive, p53{sup −/−} organoids gave rise to thyroid cancer. The low turnover rate of thyroid follicular cells and the lack of a long-term thyroid cell culture system have hampered studies of thyroid carcinogenesis. We have now established a thyroid organoid culture system that supports thyroid cell proliferation in vitro. The established mouse thyroid organoids performed thyroid functions including thyroglobulin synthesis, iodide uptake, and the production and release of thyroid hormone. Furthermore, transplantation of the organoids into recipient mice resulted in the formation of normal thyroid–like tissue capable of iodide uptake and thyroglobulin production in vivo. Finally, forced expression of oncogenic NRAS (NRAS{sup Q61R}) in thyroid organoids established from p53 knockout mice and transplantation of the manipulated organoids into mouse recipients generated a model of poorly differentiated thyroid cancer. Our findings suggest that this newly developed thyroid organoid culture system is a potential research tool for the study of thyroid physiology and pathology including thyroid cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Resource Aware Intelligent Network Services (RAINS) Final Technical Report

The Resource Aware Intelligent Network Services (RAINS) project conducted research and developed technologies in the area of cyber infrastructure resource modeling and computation. The goal of this work was to provide a foundation to enable intelligent, software defined services which spanned the network AND the resources which connect to the network. A Multi-Resource Service Plane (MRSP) was defined, which allows resource owners/managers to locate and place themselves from a topology and service availability perspective within the dynamic networked cyberinfrastructure ecosystem. The MRSP enables the presentation of integrated topology views and computation results which can include resources across the spectrum of compute, storage, and networks. The RAINS project developed MSRP includes the following key components: i) Multi-Resource Service (MRS) Ontology/Multi-Resource Markup Language (MRML), ii) Resource Computation Engine (RCE), iii) Modular Driver Framework (to allow integration of a variety of external resources). The MRS/MRML is a general and extensible modeling framework that allows for resource owners to model, or describe, a wide variety of resource types. All resources are described using three categories of elements: Resources, Services, and Relationships between the elements. This modeling framework defines a common method for the transformation of cyber infrastructure resources into data in the form of MRML models. In order to realize this infrastructure datification, the RAINS project developed a model based computation system, i.e. “RAINS Computation Engine (RCE)”. The RCE has the ability to ingest, process, integrate, and compute based on automatically generated MRML models. The RCE interacts with the resources thru system drivers which are specific to the type of external network or resource controller. The RAINS project developed a modular and pluggable driver system which facilities a variety of resource controllers to automatically generate, maintain, and distribute MRML based resource descriptions. Once all of the resource topologies are absorbed by the RCE, a connected graph of the full distributed system topology is constructed, which forms the basis for computation and workflow processing. The RCE includes a Modular Computation Element (MCE) framework which allows for tailoring of the computation process to the specific set of resources under control, and the services desired. The input and output of an MCE are both model data based on MRS/MRML ontology and schema. Some of the RAINS project accomplishments include: Development of general and extensible multi-resource modeling framework; Design of a Resource Computation Engine (RCE) system which includes the following key capabilities; Absorb a variety of multi-resource model types and build integrated models; Novel architecture which uses model based communications across the full stack for all Flexible provision of abstract or intent based user facing interfaces; Workflow processing based on model descriptions; Release of the RCE as an open source software; Deployment of RCE in the University of Maryland/Mid-Atlantic Crossroad ScienceDMZ in prototype mode with a plan under way to transition to production; Deployment at the Argonne National Laboratory DTN Facility in prototype mode; Selection of RCE by the DOE SENSE (SDN for End-to-end Networked Science at the Exascale) project as the basis for their orchestration service.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Ab initio-informed maximum entropy modeling of rovibrational relaxation and state-specific dissociation with application to the O{sub 2} + O system

Quasi-classical trajectory (QCT) calculations are used to study state-specific ro-vibrational energy exchange and dissociation in the O{sub 2} + O system. Atom-diatom collisions with energy between 0.1 and 20 eV are calculated with a double many body expansion potential energy surface by Varandas and Pais [Mol. Phys. 65, 843 (1988)]. Inelastic collisions favor mono-quantum vibrational transitions at translational energies above 1.3 eV although multi-quantum transitions are also important. Post-collision vibrational favoring decreases first exponentially and then linearly as Δv increases. Vibrationally elastic collisions (Δv = 0) favor small ΔJ transitions while vibrationally inelastic collisions have equilibrium post-collision rotational distributions. Dissociation exhibits both vibrational and rotational favoring. New vibrational-translational (VT), vibrational-rotational-translational (VRT) energy exchange, and dissociation models are developed based on QCT observations and maximum entropy considerations. Full set of parameters for state-to-state modeling of oxygen is presented. The VT energy exchange model describes 22 000 state-to-state vibrational cross sections using 11 parameters and reproduces vibrational relaxation rates within 30% in the 2500–20 000 K temperature range. The VRT model captures 80 × 10{sup 6} state-to-state ro-vibrational cross sections using 19 parameters and reproduces vibrational relaxation rates within 60% in the 5000–15 000 K temperature range. The developed dissociation model reproduces state-specific and equilibrium dissociation rates within 25% using just 48 parameters. The maximum entropy framework makes it feasible to upscale ab initio simulation to full nonequilibrium flow calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The response of plasma density to breaking inertial gravity wave in the lower regions of ionosphere

We present a three-dimensional numerical study for the E and lower F region ionosphere coupled with the neutral atmosphere dynamics. This model is developed based on a previous ionospheric model that examines the transport patterns of plasma density given a prescribed neutral atmospheric flow. Inclusion of neutral dynamics in the model allows us to examine the charge-neutral interactions over the full evolution cycle of an inertial gravity wave when the background flow spins up from rest, saturates and eventually breaks. Using Lagrangian analyses, we show the mixing patterns of the ionospheric responses and the formation of ionospheric layers. The corresponding plasma density in this flow develops complex wave structures and small-scale patches during the gravity wave breaking event.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Applicability of available Li-ion battery degradation models for system and control algorithm design

Within electrified vehicle powertrains, lithium-ion battery performance degrades with aging and usage, resulting in a loss in both energy and power capacity. As a result, models used for system design and control algorithm development would ideally capture the impact of those efforts on battery capacity degradation, be computationally efficient, and simple enough to be used for algorithm development. This paper provides an assessment of the state-of-the-art in lithium-ion battery degradation models, including accuracy, computational complexity, and amenability to control algorithm development. Various aging and degradation models have been studied in the literature, including physics-based electrochemical models, semi-empirical models, and empirical models. Some of these models have been validated with experimental data; however, comparisons of pre-existing degradation models across multiple experimental data sets have not been previously published. Three representative models, a 1-d electrochemical model (a combination of performance model and degradation model), a semi-empirical degradation model (the performance is predicted by an equivalent circuit model) and an empirical degradation model (the performance is predicted by an equivalent circuit model), are compared against four published experimental data sets for a 2.3-Ah commercial graphite/LiFePO 4 cell. Based on simulation results and comparisons to experimental data, the key differences in the aging factors captured by each of the models are summarized. Here the results show that the physics-based model is best able to capture results across all four representative data sets with an error less than 10%, but is 20x slower than the empirical model, and 134x slower than the semi-empirical model, making it unsuitable for powertrain system design and model-based algorithm development. Despite being computationally efficient, the semi-empirical and empirical models, when used under conditions that lie outside the calibration data set, exhibit up to 71% error in capacity loss prediction. Such models require expensive experimental data collection to recalibrate for every new application. Thus, in the author’s opinion, there exists a need for a physically-based model that generalizes well across operating conditions, is computationally efficient for model-based design, and simple enough for control algorithm development.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

High-fidelity multiphysics load following and accidental transient modeling of microreactors using NEAMS tools: Application of NEAMS codes to perform multiphysics modeling analyses of micro-reactor concepts

The feasibility of modeling microreactors using high-fidelity models with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) tools is investigated in this report. Three overarching questions guided this research: can NEAMS tools readily be applied for high-fidelity multiphysics modeling of different types of transients in microreactor designs; how accurate are the results obtained; and are improvements needed in accuracy or user experience of NEAMS tools, especially considering newly developed capabilities? This work builds upon FY-2022 work, and two microreactor concepts considering heat pipe (HP-MR) and gas-cooled (GC-MR) technologies were further analyzed using high-fidelity multiphysics simulations. The NEAMS tools considered and coupled within the MultiApp environment are Griffin for neutronics, BISON for thermo-mechanics, Sockeye for heat pipe modeling (in HP-MR), SAM for 1D Fluid – 3D solid modeling of coolant channels and system modeling of balance of plant components (in GC-MR), and the SWIFT code for hydrogen redistribution in hydride moderator. The Heat Pipe MicroReactor (HP-MR) concept was further analyzed in FY-2023 to demonstrate the stochastic TRISO failure modeling capability in BISON to check operational limits of the TRISO fuel. A new full-core Gas-Cooled MicroReactor (GC-MR) model was developed based on the initial assembly-model used in Y-2022 and used for steady-state and accidental depressurization transient simulations. Accuracy of the simulations performed was assessed through 1) verification analyses completed on the different physics with code-to-code comparison, and 2) validation of the multiphysics simulations based on modeling of the Kilopower Reactor Using Stirling Technology (KRUSTY) experiment. In FY-2023, the mesh and model of KRUSTY was updated to closely match publicly available data, and the neutronic model was verified and validated against experimental control rod worth measurements. The multiphysics model of KRUSTY was developed and used for steady-state analysis and for modeling reactivity insertion transient. The calculated power increase and stabilization agrees well with experimental data following adjustment in fuel thermal expansion coefficient. As an important component of this project, the ANL team gathered experience with a wide range of NEAMS tools: the MOOSE Mesh System, Griffin, BISON, SWIFT, Sockeye, SAM, Workbench, and the MOOSE MultiApp System, and provided assessment of new capabilities. Noteworthy are the user assessment of the “vapor-only” flow model in Sockeye and development of a multiphysics startup transient in HP-MR unit cell for use as tutorial in Sockeye. The full-core GC-MR model was used for assessment of SAM for balance of plant modeling and for demonstrating the SWIFT code capability for hydrogen redistribution modeling in multiphysics transient analyses. In this process, several bugs/issues were identified and reported to developers. Finally, the assembly GC-MR model developed in FY-2022 coupling Griffin, BISON and SAM through flow blockage and rod ejection transients was published to the National Reactor Innovation Center (NRIC) Virtual Test Bed (VTB). The Heat Pipe MicroReactor (HP-MR) concept high-fidelity multiphysics coupling of Griffin/BISON/Sockeye in load-following and heat pipe failure transients was also published on the VTB. Those submissions are enabling thorough review of these models as well as wide distribution to industry, regulator, and university users. In this analysis, several new research questions were uncovered, and follow-up analyses are recommended to further improve some models, consider additional transients, and continue development of VTB models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Chapter 22 - Pathways Toward Sustainable Development

Model-based scenario analysis can help in identifying ways to achieve the environmental targets of the Sustainable Development Goals (SDGs) and related multilateral environmental agreements (MEAs) (well established). Target-seeking scenarios provide insight into the required level of effort, promising measures, and possible synergies and trade-offs between these measures and a range of targets. The usefulness of scenarios can be illustrated by the successful use of such scenarios in the literature on climate policy. Scenarios can be used to explore different pathways for achieving long-term targets and provide insights into the costs and benefits of these pathways. There are important interrelations (synergies and trade-offs) between the achievement of the various SDGs and related MEAs.. This means that strategies that aim to achieve sets of targets will have to take account for these interrelations. At the moment, scenarios that explore the fulfilment of a large set of SDG targets simultaneously are mostly lacking. An assessment of possible pathways must therefore rely on more narrowly focused scenarios in the literature. This does lead to a higher level of uncertainty and some clear knowledge gaps. {22.2}

Van Vuuren, Detlef↗
DOE OSTI2013

CREST Cost of Renewable Energy Spreadsheet Tool: A Model for Developing Cost-Based Incentives in the United States; User Manual Version 4, August 2009 - March 2011 (Updated July 2013)

The objective of this document is to help model users understand how to use the CREST model to support renewable energy incentives, FITs, and other renewable energy rate-setting processes. This user manual will walk the reader through the spreadsheet tool, including its layout and conventions, offering context on how and why it was created. This user manual will also provide instructions on howto populate the model with inputs that are appropriate for a specific jurisdiction's policymaking objectives and context. Finally, the user manual will describe the results and outline how these results may inform decisions about long-term renewable energy support programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Novel Magnetically Fluidized Bed Reactor Development for the Looping Process: Coal to Hydrogen Production R&D

The coal to hydrogen project utilizes the iron/iron oxide looping process to produce high purity hydrogen. The input energy for the process is provided by syngas coming from gasification process of coal. The reaction pathways for this process have been studied and favorable conditions for energy efficient operation have been identified. The Magnetically Stabilized Porous Structure (MSPS) is invented. It is fabricated from iron and silica particles and its repeatable high performance has been demonstrated through many experiments under various conditions in thermogravimetric analyzer, a lab-scale reactor, and a large scale reactor. The chemical reaction kinetics for both oxidation and reduction steps has been investigated thoroughly inside MSPS as well as on the surface of very smooth iron rod. Hydrogen, CO, and syngas have been tested individually as the reducing agent in reduction step and their performance is compared. Syngas is found to be the most pragmatic reducing agent for the two-step water splitting process. The transport properties of MSPS including porosity, permeability, and effective thermal conductivity are determined based on high resolution 3D CT x-ray images obtained at Argonne National Laboratory and pore-level simulations using a lattice Boltzmann Equation (LBE)-based mesoscopic model developed during this investigation. The results of those measurements and simulations provide necessary inputs to the development of a reliable volume-averaging-based continuum model that is used to simulate the dynamics of the redox process in MSPS. Extensive efforts have been devoted to simulate the redox process in MSPS by developing a continuum model consist of various modules for conductive and radiative heat transfer, fluid flow, species transport, and reaction kinetics. Both the Lagrangian and Eulerian approaches for species transport of chemically reacting flow in porous media have been investigated and verified numerically. Both approaches lead to correct prediction of hydrogen production rates over a large range of experimental conditions in the laboratory scale reactor and the bench-scale reactor. In the economic analysis, a comparison of the hydrogen production plants using iron/iron oxide looping cycle and the conventional process has been presented. Plant configurations are developed for the iron/iron oxide looping cycle. The study suggests a higher electric power generation but a lower hydrogen production efficiency comparing with the conventional process. Additionally, it was shown that the price of H 2 obtained from our reactor can be as low as $\$$1.7/kg, which is 22% lower than the current price of the H 2 obtained from reforming plants.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

concrete NDE↗
DOE OSTI2015

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well s health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.

Almansouri, Hani↗
DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗