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DOE OSTI2022

ParaEMT (NLR's (formerly known as NREL's) EMT Simulator: An Open Source, and HPC-Compatible Large-Scale Power System Electro-Magnetic Transient (EMT) Simulator) [SWR-22-16]

This open-source tool aims to provide a powerful and flexible platform for simulating electromagnetic transients (EMT) of large-scale inverter-based resource (IBR)-dominated power systems. The purpose of making it open source is to foster a community of EMT simulation and assist in exploring new EMT algorithms and applying advanced computational techniques to EMT simulation.

Tan, Jin↗

DOE OSTI2025

Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

autonomous vehicle↗

DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

The execution of agent-based simulations (ABSs) on hardware accelerator devices such as graphics processing units (GPUs) has been shown to offer great performance potentials. However, in heterogeneous hardware environments, it can become increasingly difficult to find viable partitions of the simulation and provide implementations for different hardware devices. To automate this process, we present OpenABLext, an extension to OpenABL, a model specification language for ABSs. By providing a device-aware OpenCL backend, OpenABLext enables the co-execution of ABS on heterogeneous hardware platforms consisting of central processing units, GPUs, and field programmable gate arrays (FPGAs).We present a novel online dispatching method that efficiently profiles partitions of the simulation during run-time to optimize the hardware assignment while using the profiling results to advance the simulation itself. In addition, OpenABLext features automated conflict resolution based on user-specified rules, supports graph-based simulation spaces, and utilizes an efficient neighbor search algorithm. We show the improved performance of OpenABLext and demonstrate the potential of FPGAs in the context of ABS. We illustrate how co-execution can be used to further lower execution times. OpenABLext can be seen as an enabler to tap the computing power of heterogeneous hardware platforms for ABS.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Quantum Communication Networks for Energy Applications: Review and Perspective

Abstract The energy sector is expected to undergo significant changes in the coming decades with the advent of new technologies, including smart grid development, microgrid expansion, increasing electric vehicle and renewable energy usage, and enhanced measures to minimize greenhouse gas emission, among others. In tandem, these changes are expected to create new opportunities for the deployment of quantum technologies within the energy sector. Building on the authors' previous reviews on the current state of and future opportunities for quantum sensing, quantum computing and quantum simulations for energy sector applications, this work provides an overview of recent progress in quantum networking and communications for the energy industry, with a focus on platforms, devices, and protocols, including quantum teleportation and quantum key distribution. Specific areas of relevance to the energy sector are then analyzed, including the role of quantum networks for greenhouse gas monitoring, secure data collection and transmission in smart grids, nuclear power plants’ safety, facilitating oil and gas exploration, and other energy‐relevant applications. This review concludes with a brief overview of areas for future innovation, including the need for platforms for simulating quantum networks, quantum material and platform design, and computational approaches to accelerate quantum protocol discovery and development.

Paudel, Hari P.↗

DOE OSTI2024

Developing and Running Quantum Algorithms for Chemistry and Materials (QAChMat) (Final Technical Report)

The goal of the project is to design, develop, and execute new computational methods on practical quantum computing platforms to simulate hard problems in chemical and materials sciences. We exploited the two leading quantum computing platforms of trapped atomic ion and superconducting qubits, established in laboratories at Duke University and the University of Maryland, to discover new simulation and computational methods for the study of quantum chemistry and materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multiscale Ecosystem for solving Maxwell-Schrodinger equations of open quantum systems (OpenMS)

Light-matter interactions play an important role in many branches of physics, chemistry, energy, and materials science. In the strong coupling regime, light-matter interactions are able to tune the materials properties via the formation of new quasiparticles, such as plasmons and polaritons. However, theoretical and numerical modeling of the light-matter interaction-mediated processes remain a big challenge because light-matter interactions are fundamentally multiscale and multiphysics problems involving multiple interactions between electrons, nuclei, and photons at different time/length scales. In the current community, light-matter interactions were treated at different levels of theoretical complexity in quantum chemistry and quantum optics. In quantum optics or quantum photonics, the matter is usually simplified as a few-level system, and the light is treated quantum-mechanically. On the other hand, quantum chemistry explores first-principles methods, including both single-particle and many-body-based techniques, to describe the electronic properties of matter in detail. However, light is usually prescribed as a classical electromagnetic field, and the light-matter interaction is taken into account as an external potential via classical approximations. This software is designed to fill current modeling shortcomings by delivering a first-ever scalable multiscale platform for simulating light-matter interactions in realistic electromagnetic environments. The software solves Maxwell and Schrodinger equations self-consistent on the heterogeneous platforms. It implements HF/DFT, TDDFT, and coupled-cluster counterparts for light-matter interactions and adopts modular programming to offload massively parallel algorithms on a large number of CPU/GPUs.

Zhang, Yu↗

DOE OSTI2020

COVID-19 Joint Pandemic Modeling and Analysis Platform

The non-pharmaceutical intervention to reduce the impact and spread of COVID-19 requires the development of policies and guidance through a collaborative effort among government, academia, medicine, and citizens. To operationalize this effort, we have developed an all-encompassing situational awareness platform that can process multi-modal and multi-source data allowing informed decision making. Besides, showing the current spread of infection, the platform also captures the impact of human dynamics on the infection spread, location, and availability of critical infrastructure, prediction, and high-performance computing driven simulation. The platform is extensible, allowing third-party integration and services to consume the curated data and analytics in near real-time. We believe the platform will augment critical decision making for reducing the impact and spread of the pandemic.

Thakur, Gautam↗

DOE OSTI2023

Interpolation Methods to Enable Fast and Accurate EMT Simulation of PV Inverters

In this paper, interpolation methods to enable fast simulation of photovoltaic (PV) inverters are proposed. The interpolation methods are based on the event-driven interpolating method that can be introduced in the simulation model to run the model at a higher timestep while maintaining accuracy. The proposed methods are tested on a neutral point clamp (NPC) converter and a two-level converter (2LC) that are typically used in large-scale PV plants. The interpolation methods are integrated into the NPC and 2LC simulation models in PSCAD/Fortran and RSCAD C platforms. These models are tested at different simulation timesteps in off-line and real-time simulation platforms PSCAD and RSCAD respectively.

Marthi, Phani Ratna Vanamali↗

DOE OSTI2022

Quantifying the effects of varietal types × management on the spatial variability of sorghum biomass across US environments

Abstract Regional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation ( ir ), mean of temperature stress factor ( tp ) and water stress factor ( sw ). Simulated biomass ranged from 5.8 t ha −1 (GS‐rainfed) to 27.5 t ha −1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw ( r = 0.64–0.86) and irrigated biomass with ir ( r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw ( r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios.

Ojeda, Jonathan J.↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020

With the surge in electric vehicle (EV) adoption and the need for extended driving ranges, optimizing energy efficiency, particularly through thermal management, is critical, especially in extreme weather. Managing the substantial energy needed for cabin climate control and battery temperature regulation can increase energy demands by over 50 %, severely limiting range. This study conducts a comparative analysis of thermal management systems (TMS) in three popular EV vehicles, 2020 Chevrolet Bolt, 2019 Nissan Leaf Plus, and 2020 Tesla Model 3, evaluating their distinct TMS configurations and performance under varied weather conditions. Using both numerical simulations and experimental data collected on a controlled test bench at Argonne National Laboratory, we assess how TMS architecture and operational modes influence energy consumption and range. A comprehensive TMS model was developed, integrating cabin and battery thermal sub-models in the Autonomie software platform, to simulate temperature fluctuations and range impacts. Cabin climate was modeled using a mono-zonal approach, while battery cell temperature distribution was estimated through a 2D nodal structure. Each vehicle's distinct TMS setup was evaluated: the Chevrolet Bolt and Tesla Model 3 use a dual evaporator vapor compression cycle with a PTC heater for the cabin and a coolant loop for battery thermal management; the Nissan Leaf Plus employs a heat pump with a PTC heater for the cabin and air-cooling for the battery. Tests conducted at ambient temperatures of 35°C, 22°C, -7°C, and -18°C reveal significant differences in energy use and range reduction across both configurations and conditions. At 35°C, the Tesla Model 3, Chevrolet Bolt, and Nissan Leaf Plus have a range reduction of 8%, 9%, and 13%, respectively, due to air conditioning. In winter, heating technology is paramount; at -7°C, the Nissan Leaf's heat pump configuration achieves a lower range reduction (19.3%) compared to the Tesla and Chevrolet Bolt PTC heaters, which reduce range by 28.3% and 31%, respectively. Further, this study provides valuable insights for automotive engineers, EV technology researchers, and thermal management system designers aiming to enhance electric vehicle performance by understanding how different weather conditions and TMS architectures impact energy consumption and driving range.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Heterojunction Si solar cells exhibit notable performance degradation. Here, we modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, Voc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times t normal ∝ t accel T(accel)/T(normal) . (6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. Here we also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Integrated Modeling Report for CO 2 Storage with Enhanced Oil Recovery in Northern Michigan

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. MRCSP has been investigating various reservoir characterization and modeling technologies related to Carbon Capture, Utilization, and Storage (CCUS) in conjunction with carbon dioxide enhanced oil recovery (CO 2 -EOR) operations in multiple Silurian-age (Niagaran), oil-bearing, carbonate pinnacle reefs in northern Michigan, USA. This report provides a comprehensive discussion of reservoir modeling studies that were conducted for tracking oil production, forecasting CO 2 plume migration, and estimating associated storage in a number of reefs that were at different stages of their CO 2 -EOR life cycle. The modeling process for simulating oil production, CO 2 injection, and associated storage in these reefs entails two phases. The first phase, geologic framework modeling, integrates all pertinent geological and geophysical data (from logs, cores and seismic surveys) about reservoir structure, geometry, rock types, and property distributions (porosity, permeability, water saturation) into a 3-D distributed grid-based static earth model (SEM). The second phase, dynamic reservoir modeling, uses the SEM as a platform to simulate the movement of oil, gas, water, and CO 2 within the reservoir during primary hydrocarbon production, as well as during subsequent phases such as CO 2 -injection assisted EOR, plume migration, and associated storage. In addition, an assessment of coupled process effects is also carried out, where the impacts of geochemical and geo-mechanical processed induced by CO 2 injection are studied.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

A Power Takeoff Device for a Small Marine Hydrokinetic Turbine Deployed from an Unmanned Floating Platform

The design, development, and field-testing of a power takeoff (PTO) device equipped with a ball-type continuously-variable transmission (B-CVT) for a small marine hydrokinetic (MHK) turbine deployed from a floating unmanned autonomous mobile catamaran platform is described. The turbine is a partially-submerged multi-blade undershot waterwheel (USWW). The objective is to develop a PTO for the optimal conversion of the MHK energy harnessed by the turbine to electric power, which is stored in battery banks onboard the MHK platform. Modeling, simulation, and bench testing of the USWW and PTO show the feasibility of utilizing the B-CVT's variable gear ratio to decouple the USWW and generator speeds, maintaining the waterwheel within its optimal tip speed ratio (TSR) while varying the generator speed, thereby increasing the efficiency of the PTO. Results of bench and field testing in support of characterizing the power conversion capabilities of the PTO are described. The system being developed is in support of potential self-powered autonomous mobile recharge stations for unmanned aerial vehicles (U A V s) operating in coastal zones. Field tests of the complete MHK platform with 9 blades on the waterwheel and wheel sub mergences of 10 and 12 inches (full blade submergence) were performed. The overall proof-of-concept was successfully demonstrated, with the system satisfactorily capturing and converting water flow energy into electricity. The feasibility of utilizing the B-CVT as a means of increasing the PTO power capture capabilities and efficiency is analyzed.

Pimentel, Hugo↗

DOE OSTI2022

Integrated Optimization and Control of a Hybrid Gas Turbine/sCO 2 Power System

During phase-I, the project team led by Echogen Power Systems (EPS) had two primary objectives based on investigating the application of gas turbines with supercritical carbon dioxide (sCO 2 ) power cycles. The first objective was to improve the overall efficiency and performance of a hybrid gas turbine/sCO 2 power system through a joint optimization of the two subsystems (gas turbine and sCO 2 power cycle) using non-linear optimization techniques that simultaneously evaluate thermal performance of the combined cycle. The hybrid power system included several points of interaction, including (but not limited to) gas turbine exhaust, fuel heating, inlet chilling and turbine cooling. The second objective was to establish a baseline transient response model of the hybrid power system and a notional microgrid and begin steps to integrate the control systems of the three major elements (gas turbine, sCO 2 cycle and grid controller). The project team established a baseline performance for a combined cycle power plant using a production gas turbine and scaled sCO 2 power cycle only utilizing exhaust heat recovery. Echogen’s non-linear techno-economic optimization code was extended by adding gas turbine component models derived from a in-house developed gas turbine design code. With the two cycles coupled by the gas turbine exhaust, design parameters of both cycles were allowed to vary simultaneously to determine performance opportunity versus isolated designs. Returning to the baseline gas turbine/sCO 2 power cycle transient models: Echogen had in-house developed sCO 2 cycle transient model in GT-Suite system simulation software, and had partnered with Siemens Finspång for gas turbine transient model, and Siemens PTI group to provide micro-grid load profile as well as hybrid power cycle generated load (power and frequency) analysis. The transient model for the SGT-750 Siemens gas turbine was a “black-box” functional mock-up interface (FMI) model developed by Siemens Industrial Turbomachinery in Finspång, Sweden. The SGT-750 is a twin-shaft gas turbine that produces 40 MW electricity with an efficiency of about 40% at ISO conditions. At 100% gas turbine throttle (load), the SGT-750 has average exhaust conditions of 114.6 kg/s and 469.8°C. The transient model for sCO 2 power cycle was developed by Echogen in GT-SUITE 1D system simulation software platform. The basic CO 2 flow circuit has single-shaft turbomachinery with net 11.5 MW electrical power output at design conditions. The power turbine has a double-ended shaft with one end connected to synchronous generator through a fixed-ratio gearbox. The other end of power turbine is connected to the compressor through a continuously variable transmission. The major components of the sCO 2 power cycle modeled include air cooled condenser/cooler, CO 2 compressor, recuperator, two waste heat exchanger coils, power turbine, continuous variable transmission, gearbox and generator. Integration of SGT-750 transient model and sCO 2 power cycle transient model was done in Matlab Simulink. In the integrated model, the gas turbine and sCO 2 power cycle interacted at two points, first one being the gas turbine exhaust gas flow rate and temperature, which were inputs to sCO 2 power cycle model. The second point was the distribution of micro-grid load demand signal between the SGT-750 generator and sCO 2 cycle generator. For a given combined-cycle load demand, the gas turbine load demand was equal to the total demand minus the sCO 2 cycle power generated. In the present study the integrated model was simulated for two cases of grid load demand: (i) for a step change, both positive-step and negative-step, in grid load demand (ii) for a micro-grid load demand curve provided by Siemens PTI group. Finally, the time series plots representing load demand versus integrated system response were presented including the sCO 2 power cycle control system performance plots. The actual generated power and frequency of both the generators, gas turbine and sCO 2 power cycle, was supplied to Siemens PTI group for dynamic grid assessment, results of which are provided in appendices.

03 NATURAL GAS↗