Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model of Models”

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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Modeling of Synfuel Production Process: ASPEN Model of FT production with electricity demand provided at LWR scale

Synfuels, or electro-fuels (e-fuels) have the unique potential to significantly reduce greenhouse gas (GHG) emissions across the transportation sector. This is especially true for applications with substantial payloads and daily miles traveled, such as long-haul heavy-duty vehicles, rail locomotives, marine vessels and aviation aircrafts that are challenging to directly electrify via battery or fuel cell powertrain technologies. Synfuels, or electro-diesel/electro-jet fuels, have similar properties with the incumbent petroleum fuels, compatible with current infrastructure but have much lower GHG emissions relative to the petroleum counterpart, because they utilize waste carbon dioxide (CO2) streams and green hydrogen (H2) sourced from electrolysis. To achieve substantial reductions in GHG emissions, electricity sources must be zero carbon or near-zero carbon, which is the case with solar, wind, hydro and nuclear power. Compared to the intermittency of solar, wind and hydro, nuclear energy provides a steady energy source. In addition, it’s advantageous for nuclear power to produce synfuels because it provides not only near-zero carbon electricity to displace grid electricity, but also near-zero carbon steam to displace carbon-intensive natural gas combustion for steam generation. The availability of electricity and steam also enables more efficient green hydrogen production by using high-temperature electrolysis. In this work, Argonne National Laboratory (ANL) models a synfuel production process via the Fischer- Tropsch (FT) reaction by using nuclear power to provide electricity and steam. In 2021, using ASPEN Plus software, ANL established a detailed process model of a stand-alone FT production facility, assuming feedstocks of pure CO2 and H2. This stand-alone model can be expanded to integrate H2 production from nuclear power via low-temperature and high-temperature electrolysis at light-water reactor (LWR) scale. This report summarizes the stand-alone ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 351 MT/day (130,000 gal/day) of FT fuel (a mixture of naphtha, jet fuel, and diesel) by converting 223 MT/day of H2 and 2,387 MT/day of CO2. The FT fuel production energy efficiency is 58% and the carbon conversion efficiency (from CO2 to FT fuel) is 46%. The production of green hydrogen requires 390–470 MWe of electricity, which is compared with the capacity of an LWR plant. For the stand-alone FT process, the detailed energy demand (electricity and heat) is summarized in the table below. Based on the energy supply source and the required temperature, potential insertion points of nuclear energy are identified. Based on the potential nuclear energy utilization, this report discusses potential modification options for expanding the system boundary to integrate nuclear power use, for example on-site hydrogen production via water electrolysis. Modeling of the integrated system is conducted by closely working with ANL and Idaho National Laboratory (INL) collaborators to harmonize design parameters of nuclear plants and the FT production process.

Zang, Guiyan↗
DOE OSTI2022

The Modeling of the Synfuel Production Process: Process models of Fischer-Tropsch production with electricity and hydrogen provided by various scales of nuclear plants

Synthetic fuels (synfuels), also known as electro-fuels (E-fuels), are hydrocarbon fuels produced from waste CO2 streams and water electrolysis, with electricity as the primary source of energy. To achieve substantial reductions in greenhouse gas (GHG) emissions, electricity sources must release zero carbon or near-zero carbon, as is the case with solar, wind, hydro, and nuclear power. Nuclear power is one of the largest and steadiest domestic sources of clean energy in the United States. Moreover, nuclear power has the potential to produce hydrogen economically for less than $2/kg, reaching the DOE near-term target price. Thus, using nuclear power to produce synfuels has the unique potential to significantly reduce the GHG emissions of hydrocarbon fuels production and end-use applications. Fisher-Tropsch or FT fuel (a mixture of naphtha, jet fuel, and diesel) is of great interest because it is a drop-in fuel that can be blended with conventional petroleum counterparts and is compatible with existing infrastructure. By using the ASPEN Plus model, this report develops FT fuel production models on three scales, corresponding to nuclear plants with capacities of 1000 MWe, 437 Mwe, and 100 MWe, respectively. The FT model case with energy from a 437-MWe nuclear plant is used as a baseline case. This report summarizes the baseline ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 507 MT/day (185,000 gal/day) of FT fuel by converting 255 MT/day of hydrogen and 1,580 MT/day of CO2. The FT fuel production energy efficiency from hydrogen and electricity energy inputs is 70% (lowerheating-value or LHV-based). Including the high-temperature electrolyzer in the system boundary, the FT fuel production LHV efficiency from electricity and thermal energy inputs is 51%, considering 39.8 kWh/kg of electricity and 6.86 kWh/kg of thermal energy use from a nuclear plant for hydrogen production. The FT production efficiency can potentially be increased by further integrating the heat exchange between nuclear plant and FT process, and this study is underway. The carbon conversion ratio in the baseline case is 99%, with process CO2 capture and recirculation and oxy-combustion using the oxygen by-product from water electrolysis. The hydrogen consumption is 1.38 kg/gal-FT fuel and the CO2 consumption is 8.56 kg/gal-FT fuel in the baseline case. With different FT production scales determined by the nuclear plant capacity, the FT model was scaled using the same operating parameters, which led to the same conversion efficiency regardless of scale. However, the different FT plant scales will impact the economics of FT fuel production; this impact will be examined in the next phase of this study.

Zang, Guiyan↗
DOE OSTI2024

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

Here, this work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

42 ENGINEERING↗
DOE OSTI2025

Jacobian-based model diagnostics and application to equation oriented modeling of a carbon capture system

It can be difficult to identify the specific variables or equations responsible for convergence issues in large mathematical programming models. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms. A singular value decomposition is then performed to identify degenerate sets of equations and remaining scaling issues. Here, this work presents a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. This work takes the reader through the entire process of model diagnostics and reformulation, from a basic introduction to the mathematics behind these model diagnostics to the reformulations necessary to make the model numerically robust, including a significantly modified enhancement factor model.

IDAES↗
DOE OSTI2022

Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining

Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. Here, this paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.

Bayesian optimization↗
DOE OSTI2024

Modeling the Effects of Artificial Drainage on Agriculture-Dominated Watersheds Using a Fully Distributed Integrated Hydrology Model

In agriculture-dominated watersheds where natural drainage is poor, agricultural ditches (narrow engineered channels) and tile drains (perforated pipes) are widely employed to enhance surface and subsurface drainage, respectively. Despite their relatively small scale, these features exert substantial control over the hydro-biogeochemical function of watersheds and their effects need to be represented in the models. We introduce a novel strategy to incorporate the effects of artificial agricultural drainage into a fully distributed basin-scale integrated surface-subsurface hydrology models. In our approach, narrow agriculture ditches for surface drainage are resolved efficiently using ditch-aligned computational meshes that are hydrologically conditioned to ensure connectivity in the stream/ditch network. For tile drainage in the subsurface, we use the physically based Hooghoudt's drainage equation as a subgrid model and route the water drained through tiles to the nearest ditch. Without site-specific calibration, this model reproduced observed streamflow in the Portage River Watershed (>1,000 km 2 ) as recorded by a USGS gauge with good accuracy (normalized KGE = 0.81) and outperformed a calibrated SWAT model (normalized KGE = 0.68). Numerical experiments confirm that artificial drainage reduces surface inundations and effectively controls the water table. At the watershed scale, artificial drainage increases baseflow but has little effect on watershed discharges above the 90th percentile. The strong physical underpinnings and reduced need for calibration allow us to study the impacts of artificial drainage on distributed hydrological response in terms of fluxes and states and provide a platform for investigating watershed-scale nutrient transport.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2024

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

Here, the urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗
DOE OSTI2026

Hacking Kilometer-Scale Models: A Participative Model for Climate Information

In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world’s highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participative models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike.

Climate models↗
DOE OSTI2023

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

304L stainless steel↗
DOE OSTI2020

A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Bayesian network↗
DOE OSTI2024

Forward and inverse predictive transient models of TREAT using surrogate reactivity models

In this work, we present two novel approaches for predicting the power evolution and control rod height of the Transient Reactor Test Facility (TREAT) to support experiment modeling; specifically, transient analysis of the NASA-sponsored Sirius series of experiments. These approaches utilize steady-state Monte Carlo model, point kinetics model, and surrogate models to predict power evolution and control rod axial position during transient experiments. Both approaches were tested and validated against several Sirius experiments that were performed in the TREAT facility at different power levels. The validation test results show very good agreement with the experimental data, and the models were able to accurately predict the power evolution and the axial control rod position with an average error within 3.0%. Finally, this indicates that these approaches will help the reactor engineering team of the TREAT facility in preparing and predicting the power and temperature of the experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Stochastic modeling and statistical calibration with model error and scarce data

This paper introduces a procedure to assess the predictive accuracy of stochastic models subject to model error and sparse data. Model error is introduced as uncertainty on the coefficients of appropriate polynomial chaos expansions (PCE). The error associated with finite sample size allows us to conceive of these coefficients as statistics of the data that we describe as random variables whose influence on output quantities of interest is evaluated through the extended polynomial chaos expansion (EPCE). A Bayesian data assimilation scheme is introduced to update these expansions by considering the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest (QoI) are thus constructed and efficiently evaluated. Here, the Metropolis–Hastings Markov chain Monte Carlo procedure is used to sample the posterior. Two illustrative analytical and numerical problems are used to demonstrate the proposed approach.

Bayesian inference↗
DOE OSTI2020

Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models

In this work, we propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.

42 ENGINEERING↗
DOE OSTI2021

Modeling dissociation of ionic liquids with electrolyte NRTL model

Dissociation of ionic liquids (ILs) is critical to electrochemical process performance by affecting ionic conductivity. Prior studies of thermodynamic modeling either assumed no dissociation or complete dissociation that were not consistent with experimental observations. This work presented an eNRTL model for three aqueous imidazolium-based IL systems: [Emim][EtSO 4 ], [Emim][TFA], and [Emim][TfO]. The model accurately correlates the IL dissociation and VLE data in the entire concentration range from pure IL to infinite dilution aqueous solution. The system non-ideality is dominated by the dissociation chemistry. Dissociation behavior is driven by strong short-range interaction of the molecular IL-water pair relative to the dissociated IL-water pair, consistent with prior studies indicating that molecular IL and water can form an energetically favorable complex. The interaction energy parameters calculated in this work qualitatively agrees with the hydrogen bonding ability of the anions previously reported. This research not only provides a model that well describes the IL dissociation but also demonstrates the physical significance of eNRTL model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗