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Conceptual Design of a Gulled-Wing Commercial Transport

Open rotor powerplants have long been discussed as a potential solution to reduce aircraft fuel burn by increasing effective bypass ratio. However, this promising technology presents unique integration challenges. A gulled-wing approach for commercial transports is proposed to accommodate large-diameter powerplants. Integration effects of open rotor powerplants are modeled, analyzed, and discussed. Aerodynamic analysis and refined structural weight estimates are used to inform a preliminary conceptual design. Mission analysis of this model is performed, and the results are compared against NASA’s 154-passenger N+3 Conventional Configuration advanced technology baseline. Results indicate that gulled wings prove a viable solution for integrating large powerplants. Application of an open rotor engine model resulted in a fuel burn benefit on the order of 15% compared to the conventional turbofan configuration.

Conceptual Design↗

DOE OSTI2017

Performance Modeling of Materials-Based Hydrogen Storage Systems for Automotive Applications

One of the challenges of fuel cell vehicles is storage of the hydrogen fuel. In an effort to reduce the temperature and pressure limitations of storing neat hydrogen either as a high pressure gas or cryogenic liquid, material-based hydrogen storage is being developed. Hydrogen stored either as an adsorbent or as a chemical compound not only provides more reasonable pressures and temperatures but also improves the storage density of the hydrogen. Modeling tools have been developed as part of the U.S. Department of Energy's Hydrogen Storage Engineering Center of Excellence to assess the performance of these materials in light duty vehicle applications. The purpose of these modeling tools is to evaluate newly developed hydrogen storage materials and ensure that they meet customer expectations for driving range and performance before further investment. Recent advances in these modeling tools have been made to allow direct material property inputs. Such material properties as hydrogen capacity, isotherms and other thermodynamic and kinetic properties can reasonably be measured by materials researchers. Using these properties as inputs, the models can then provide estimates of the mass and volume of the storage system and the driving range and fuel efficiency of the vehicle during transient operation with four different drive cycles. The system sizing estimator and the framework model used to perform this analysis will be described and results presented for adsorbents and chemical based storage materials.

25 ENERGY STORAGE↗

DOE OSTI2019

3-D Modeling off the CFR Engine for the Investigation of Knock on Natural Gas

Engine knock and misfire are barriers to pathways leading to high-efficiency Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG (65< MN<95). In order to realize diesel-like efficiencies, spark-ignited NG engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding combustion-engine interactions important in flame propagation and end-gas autoignition. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior with NG. The CFR engine model is used to investigate fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). Cylinder wall, piston, valves, and port surface temperatures are estimated with a 1-D Three-Pressure Analysis (TPA) model. Model validation was performed by comparing model predictions with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction and BMEP.

Bestel, Diego↗

DOE OSTI2022

Electrochemical Mitigation of Corrosion in Molten Chloride Salts During CSP Plant Operation

We are designing an electrochemical flow-cell for removal of corrosive impurities from molten chloride salt Gen3 Concentrating Solar Power (CSP) plants during plant operation. Corrosive impurities will inevitably form in molten chloride salts upon exposure to air and moisture. We previously showed that even small amounts of these impurities, especially MgOHCl, will be detrimental in Gen3 CSP plants, necessitating prohibitively expensive containment alloys and frequent replacement of corroded components. Pre-purification of salt with Mg metal at temperatures above 650 degrees C is the current method for removing corrosive impurities from chloride salts before they are introduced to CSP systems. However, this is not a suitable method for impurity removal during plant operation. First, this method will produce MgO particulates which will damage plant components. Second, Mg metal is solid at the low temperature point (500 degrees C), so the purification will not proceed at a fast rate. At the high temperature point, Mg is soluble. In this case, fast purification may proceed, but dissolved metal is likely to precipitate out in cold-temperature point components, causing damage. In contrast, our electrochemically driven method allows fast Mg-based purification to proceed at the low temperature point, without formation of harmful particulates and without the risk of Mg metal precipitation. This novel approach is inspired by electrorefining techniques that are widely employed in industrial metallurgy for removal of impurities from metals. Impurities in the incoming molten salt will be reduced to inert MgO at the cathode, which can be removed by periodically washing the cell with acid. Simultaneously, Mg dissolution at the anode will ensure salt composition is maintained, with no net removal of Mg2+. We have validated this electrochemical approach at lab scale under static conditions with batch rectors. Furthermore, we have performed analytical modeling and technoeconomic analysis to produce a preliminary engineering design for the purification flow cell.

CSP↗

DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗

DOE OSTI2022

Fourier analysis of continuous fractional diffusion synthetic acceleration schemes in slab geometry

We propose two fractional extensions of continuous diffusion synthetic acceleration (DSA) with fractional derivative order α varying over the interval 2 ≥ α ≥ 1 . We investigate the spectral properties of the corresponding continuous families of fractional preconditioners by performing Fourier analysis for a model infinite homogeneous medium problem in slab geometry. The first family results in a fractional acceleration scheme, FrDSAo, that reduces to traditional DSA for .α = 2 and scattering ratio c limiting to a unit value (c → 1) but is otherwise optimized via the Fourier analysis, to obtain the smallest possible spectral radius, for c < 1 and 2 ≥ α ≥ 1. The second family corresponds to a fractional acceleration scheme, FrDSAs, that reduces to traditional DSA for α = 2 for all values of c. The latter scheme is not optimized but has the advantage of lending itself to a more straightforward implementation. For high values of c, the results of the Fourier analysis point to the existence of an interval 2 > α > ∼1.8 where both FrDSAo and FrDSAs can achieve a lower spectral radius than DSA. For example, DSA has a spectral radius of ∼0.2246 for c = 0.9999 while FrDSAo produces a value of ∼0.1616 at α = 1.92 and FrDSAs results in ∼0.2116 at α =1.93. (author)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

High-Temperature Latent Heat Storage System using Transportable Heat Pipes for Versatile Integration with Emerging Microreactors

With the growing interest in the development of micro nuclear reactors and their application to microgrid, high-temperature thermal energy storage (TES) has also been receiving great attention as a technology to reinforce the energy utilization and profitability of this class of reactors. Given the importance and research needs of the TES technology, a research project is underway at Idaho National Laboratory (INL) to develop high-temperature TES that can be easily interfaced with emerging micro nuclear reactors. Currently under development is the Heat pipe-Integrated Thermal Battery (HITB), a novel latent heat storage design that enables flexible and robust integration with any design of currently considered micro nuclear reactors. The development of detailed design based on the proposed TES concept, HITB, requires not only development and validation of modeling and simulation capabilities, but also research that spans thermal performance analysis, component- and system-scale design optimization, and experimental demonstrations including high-temperature feasibility testing. The current research efforts (and plans) are divided into the following four areas: (i) Thermal and structural modeling and analysis at both system- and component-scales for evaluating performance and conducting design optimization, (ii) Small-scale experimental demonstrations for the key components of the proposed HITB design, (iii) Modelica-based dynamic model development for developing a control strategy under dynamic operation within integrated energy systems configuration, (iv) Integrated experimental testing and demonstration. This paper introduces the overall research efforts that have been and are being conducted to develop and demonstrate the novel design of the high-temperature latent heat storage device, HITB. The key design features, ongoing research efforts including the development of modeling and simulation capabilities, small-scale demonstrations of the HITB’s key functionalities prior to scaled-up experimental demonstration, are discussed. Then, this paper concludes with a remark on the future research plans for this project.

25 ENERGY STORAGE↗

DOE OSTI2025

Assessment of CTF for Steady-state and Transient Post-CHF Conditions in Support of Time-at-Temperature Modeling Applications

The US nuclear industry is exploring options to improve operational economics and uprate the current fleet of light-water reactors by investigating transitioning to cladding performance–based safety criteria as opposed to the current limit, which requires complete avoidance of critical heat flux (CHF)/dryout. Past experience has shown that not all events leading to a dryout are severe enough to cause fuel performance degradation. Allowing temporary dryout of the fuel—that is, using a time-at-temperature (TaT) strategy—could allow for economic improvements via large power uprates and enhanced operational flexibility for current plants without compromising fuel integrity. To support this effort, the US Department of Energy is executing a comprehensive program that includes generating cladding material data under TaT conditions, developing new mechanistic models, and demonstrating modeling and simulation capabilities for transients of interest. This paper presents work performed to assess the CTF thermal-hydraulics subchannel code. CTF is a package used in the VERA core simulator, which will ultimately be used for TaT analysis. CTF will provide the thermal-hydraulic boundary conditions that will be needed for fuel performance analysis in the BISON code. Quantifying both the accuracy and uncertainty of post-CHF models will therefore be necessary. This paper outlines the strategy for the assessment of TaT and presents the results of using the steady-state and transient dryout experiments of the Boiling Fine-mesh Bundle Tests for CTF validation. The results show that the current model tends to overpredict steady-state critical power. This behavior translates to the transient tests, in which CTF is unable to capture transient dryout behavior. Some discussion of sensitivity analysis work being performed is provided to indicate which models must be further analyzed to properly model transient dryout and its uncertainty.

Salko Jr, Robert [ORNL] (ORCID:0000000253566679)↗

DOE OSTI2021

Apparatus and method for safety analysis evaluation with data-driven workflow

An apparatus and method for system safety analysis evaluation is provided, the apparatus including processing circuitry configured for generating a calculation matrix for a system, generating a plurality of models based on the calculation matrix, performing a benchmarking or convolution analysis of the plurality of models, identifying a design envelope based on the benchmarking or convolution analysis, deriving uncertainty models from the benchmarking or convolution analysis, deriving an assessment judgment based on the uncertainty models and acceptance criteria, defining one or more limiting scenarios based on the design envelope, and determining a safety margin in at least one figure-of-merit for the system based on the design envelope and the acceptance criteria.

Martin, Robert P.↗

DOE OSTI2023

Apparatus and method for safety analysis evaluation with data-driven workflow

Martin, Robert P.↗

NASA NTRS1985

Box truss analysis and technology development. Task 1: Mesh analysis and control

An analytical tool was developed to model, analyze and predict RF performance of box truss antennas with reflective mesh surfaces. The analysis system is unique in that it integrates custom written programs for cord tied mesh surfaces, thereby drastically reducing the cost of analysis. The analysis system is capable of determining the RF performance of antennas under any type of manufacturing or operating environment by integrating together the various disciplines of design, finite element analysis, surface best fit analysis and RF analysis. The Integrated Mesh Analysis System consists of six separate programs: The Mesh Tie System Model Generator, The Loadcase Generator, The Model Optimizer, The Model Solver, The Surface Topography Solver and The RF Performance Solver. Additionally, a study using the mesh analysis system was performed to determine the effect of on orbit calibration, i.e., surface adjustment, on a typical box truss antenna.

Bachtell, E. E.↗

DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Signature Analysis Utilizing a Dynamic Molten Salt Reactor Model for MC&A

Moving from traditional fixed fuel nuclear reactor systems to a mobile, dynamic fuel system that has dissolved special nuclear material in a molten salt is a paradigm shift in several respects. One major consideration is how to develop effective nuclear material controls and accounting for these novel reactor systems. The Molten Salt Reactor Experiment is one example of a critical molten salt system and provided a significant reference library based on the documented effort. But it was low thermal power that was not intended to reflect a commercial scale electricity production design. Therefore, to facilitate and assist vendors with domestic licensing considerations, research is underway to identify methods that could be used for domestic safeguards approaches for these novel reactor systems. This research presents the results of a signature analysis of data generated from three simulated scenarios using the molten salt demonstration reactor model defined in the Transient Simulation Framework of Reconfigurable Modules. Each scenario provides 1 hour isotope inventories over a 180 day period. The scenarios investigated provide test cases to examine if direct gamma-ray spectroscopy of the fuel can be used to identify changes comparing a base case (no reactivity control and fixed fission contribution) to an insertion of reactivity (10 pcm no change in fission composition) and a change in fission composition. The analysis demonstrates that monitoring the total count rate in a highly collimated high-resolution photon energy spectrum is sensitive to perturbations imposed into the reactor model. The total photon count rate changes ≈2% for the fission composition change and ≈4.5% for the reactivity insertion compared to the base case. However, both scenarios show an increase in the total photon count rate. The total photon count rate can be used to identify changes due to power (number of fissions) but not due to a change in the material undergoing fission. To distinguish between the two cases of increased power, the photon spectrum would require an intensive analysis technique. A photon peak count rate ratio analysis could be used to identify changes in the fissile material fission generation in the core through identification of a static peak that shows little variation to the source of fission and a highly varying peak. The photon peak strength will ultimately be determined by the isotope’s fission yield. A preliminary analysis investigating the coupling of the isotope’s fission yield and its concentration in the fuel salt derived from the modeling has been performed. A ratio analysis of the photon peak count rates of 140 La to 99 Mo demonstrated that the reactivity insertion creates a distinct difference in the ratio compared to the fission composition change scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Gauge constrained algorithm of variational discrete action theory at N = 3 for the multiorbital Hubbard model

The recently developed variational discrete action theory (VDAT) provides a systematic variational approach to the ground state of the quantum many-body problem, where the quality of the solution is controlled by an integer N, and increasing N monotonically approaches the exact solution. VDAT can be exactly evaluated in the d = ∞ multiorbital Hubbard model using the self-consistent canonical discrete action theory (SCDA), which requires a self-consistency condition for the integer time Green's functions. Previous work demonstrates that N = 3 accurately captures multiorbital Mott/Hund physics at a cost similar to the Gutzwiller approximation. Here we employ a gauge constraint to automatically satisfy the self-consistency condition of the SCDA at N = 3, yielding an even more efficient algorithm with enhanced numerical stability. We derive closed form expressions of the gauge constrained algorithm for the multiorbital Hubbard model with general density-density interactions, allowing VDAT at N = 3 to be straightforwardly applied to the seven-orbital Hubbard model. We present results and a performance analysis using N = 2 and N = 3 for the SU⁡(2⁢N orb ) Hubbard model in d = ∞ with N orb = 2–8, and compare to numerically exact dynamical mean-field theory solutions where available. Finally, the developments in this work will greatly facilitate the application of VDAT at N = 3 to strongly correlated electron materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

pvplr-python: Python package implementation of PVplr for Performance Loss Rate (PLR) analysis

Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.

Kumar, Suraj↗

DOE OSTI2020

System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States

We assess the performance of a fleet of 411 utility-scale (i.e., >5 MWAC and ground-mounted) photovoltaic (PV) projects totaling 21.1 GWDC (16.3 GWAC) of capacity, which achieved commercial operations in the United States from 2007 to 2016. This fleet of projects contributed more than 50% of all solar electricity generated in the United States in 2017. Using detailed information on individual project characteristics, in conjunction with modeled irradiance data, we assess the extent to which actual first-year performance has lived up to both modeled and stated expectations. We then analyze system-level performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on system performance. We find that this fleet of utility-scale PV projects has generally lived up to ex ante expectations for first-year performance but that subsequent system-level degradation—found to be −1.3%/year (±0.2%)—has, on average, been worse than both ex ante expectations (commonly −0.5%/year) and results from past studies (ranging from −0.8%/year to −1.0%/year). We emphasize that −1.3%/year is a system-level estimate that captures more than just module degradation (e.g., including soiling, balance of plant degradation, and downtime for maintenance and/or other events). A side analysis of a variety of project characteristics suggests that system-level degradation rates tend to be of lower magnitude among newer projects and larger projects and at sites with lower long-term average temperatures.

14 SOLAR ENERGY↗

DOE OSTI2016

Bison Modeling of SiC/SiC Cladding Including Fuel-Pellet Interaction

SiC/SiC composites are considered leading candidates for accident tolerant fuel cladding in light water reactors. However, when SiC-based materials are exposed to neutron irradiation, they experience significant changes in dimensions and physical properties. In the extreme nuclear reactor environment, SiC-based fuel cladding will see neutron damage, significant heat flux, as well as a corrosive environment. In this report, we evaluate the effect of non-uniform changes in the dimensions caused by neutron irradiation with variable temperatures, along with the closing of the fuel – clad gap, on the stress development in the clad over the course of time. To ensure reliable and safe operation of accident tolerant fuel cladding concepts such as SiC-based materials, it is important to assess its thermo-mechanical performance under in-reactor conditions of irradiation and realistic temperature distributions. In this work, we have used the BISON fuel performance modeling code to perform the thermo-mechanical analysis of SiC/SiC cladding. A constitutive model is constructed and solved numerically to predict the stress distribution and variation in the cladding under normal operating conditions. The dependence of dimensions and physical properties variation with irradiation and temperature has been incorporated. These initial results from the analyses provides insights about the stress distribution and variation with time in the cladding as well as the interaction of fuel pellet with the cladding under different conditions of initial fuel pellet-cladding gap and steady state power levels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗