Search NASA⌕ Search

SEARCH · Search NASA

Results for “load calibration”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Van de Graaff Irradiation Studies of Oxygen Sensor and Hydrated Zirconia Sorbent

Commercial-scale production of Mo-99 demands a facility-wide assessment of radiation-induced degradation across process equipment, instrumentation, and purification media. Key factors— total ionizing dose, dose rate, radiolysis-driven chemistry, thermal loads, activation, and any neutron-related effects—should inform material selection, shielding, and component layout. Purification materials (sorbents, resins, filters, seals, housing) require particular attention to ensure that irradiation does not alter selectivity or capacity, generate fines, or introduce leachables that could compromise Mo-99 purity or downstream Tc-99m generator performance. Dose mapping, accelerated irradiation testing, and studies of extractables/leachables together provide the basis for setting maintenance, calibration, and replacement intervals and for qualifying materials that retain mechanical integrity, chemical compatibility, and radiopurity over their service life.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Lean-Stratified Combustion System with Miller Cycle for Downsized Boosted Application - Part 2

Automotive manufacturers relentlessly explore engine technology combinations to achieve reduced fuel consumption under continued regulatory, societal and economic pressures. For example, technologies enabling advanced combustion modes, increased expansion to effective compression ratio and reduced parasitics continue to be developed and integrated within conventional and hybrid propulsion strategies across the industry. Here, a high-efficiency gasoline engine capable for use in conventional or hybrid electric vehicle platforms is highly desirable. This paper is the second of two papers describing the multi-cylinder integration of a technology package combining lean-stratified combustion with Miller cycle for downsized boosted applications. The first paper describes the design, analysis and single-cylinder testing conducted to down-select the combustion system deployed to the multi-cylinder engine. This paper defines a light-duty engine package capable of achieving a 35% fuel economy improvement at US Tier 3 emission standards over a naturally aspirated stoichiometric baseline vehicle. A multi-mode combustion system was developed enabling highly efficient lean-stratified operation at light-load and stoichiometric Miller-cycle operation at mid- to high-loads. A central direct-injection four-valve layout was designed with high-tumble ports and a bowl-in-piston capable of stable operation under highly dilute mixtures when properly matched to fuel spray characteristics, injection strategy, and a high-energy ignition system. Boost system layout and base engine architecture were selected to minimize parasitics and maximize after treatment effectiveness. A multi-cylinder engine was developed and calibrated over a steady-state map. Results fed vehicle simulations over the EPA city and highway test cycles to predict the fuel economy improvement and tailpipe emissions potential. Fuel economy improvements over the baseline vehicle were projected at 42% and 39% for active-urea and passive-ammonia SCR after treatment systems respectively. Controls complexity to achieve regulatory compliance and the cost of the lean after treatment system were identified as the primary challenges to commercial viability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading

We study the deformation of tantalum under extreme loading conditions. Experimental velocity data are drawn from both ramp loading experiments on Sandia’s Z-machine and gas gun compression experiments. The drive conditions enable the study of materials under pressures greater than 100 GPa. We provide a detailed forward model of the experiments including a model of the magnetic drive for the Z-machine. Utilizing these experiments, we simultaneously infer several different types of physically motivated parameters describing equation of state, plasticity, and anelasticity via the computational device of Bayesian model calibration. Characteristics of the resulting calculated posterior distributions illustrate relationships among the parameters of interest via the degree of cross correlation. The calibrated velocity traces display good agreement with the experiments up to experimental uncertainty as well as improvement over previous calibrations. Examining the Z-shots and gun-shots together and separately reveals a trade-off between accuracy and transferability across different experimental conditions. Implications for model calibration, limitations from model form, and suggestions for improvements are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2020

Real-Time Dynamic Brake Assessment for Heavy Commercial Vehicle Safety

This paper summarizes initial results and findings of a model developed to determine the braking performance of commercial motor vehicles in motion regardless of brake type or gross weight. Real-world data collected by Oak Ridge National Laboratory for a U.S. Department of Energy study was used to validate the model. Expanding on previous proof-of-concept research showing the linear relationship of brake application pressure and deceleration additional parameters such as elevation were added to the model. Outputs from the model consist of coefficients calculated for every constant pressure braking event from a vehicle that can be used to calculate a deceleration and thus compute a stopping distance for a given scenario. Using brake application pressure profiles derived from the dataset, stopping distances for light and heavy loads of the same vehicle were compared for various speed and road grades. For a constant brake application pressure profile (i.e. the pressure is relatively constant throughout the entire stop) it was shown that the lighter vehicle was able to stop in a shorter distance than the heavier vehicle in most scenarios other than large uphill grades. Significant changes in weight can be observed through the fuel efficiency of the vehicle to determine if the model needs to be calibrated with new or additional braking events. The model was initially developed for tracking brake performance to inform routine maintenance and the ordering of vehicles in a platoon but can be expanded to other connected and automated research areas.

Siekmann, Adam↗
DOE OSTI2020

Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel

Here, predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A new generation of uranium coincidence fast neutron collars for assay of LWR fresh fuel assemblies

The active uranium neutron coincidence collar provides a means of non-destructively assaying the fissile linear density of Light Water Reactor fresh fuel assemblies containing low enriched uranium. These neutron collars can operate in two modes: a thermal and a fast mode. In fast mode, a neutron collar has an added cadmium (Cd) liner in the sample cavity of the detector to reduce the impact of the burnable poison (thermal neutron absorber) on the detector signal (doubles). The main advantage for operating in fast mode is a detected signal that is less dependent of the burnable neutron poison content and thus less dependent on facility operator declarations. The drawback is that operating in fast mode requires a longer measurement time (~hour vs tens of minutes for thermal mode) to achieve the statistically needed precision in the measurements. The trend in the modern reactor fuel assemblies is moving to higher burnup by using higher initial enrichment and, consequently, a higher number of burnable poison rods to compensate the initial neutron reactivity. The increase of the burnable poison loading has motivated the development of a new generation of high efficiency fast neutron collars to allow practical measurements in-field by nuclear inspectors. This paper describes the development and performance evaluation of a new generation of neutron collars, for both boiling water reactor (BWR) and pressurized water reactor (PWR) fuels, jointly developed between the US Department of Energy, through Los Alamos National Laboratory, and the Euratom Safeguards Directorate of the European Commission. In this work, we present here calibrations with reference fuel assemblies at Los Alamos National Laboratory as well as the results of in-field measurement campaigns in fuel fabrication plants with modern commercial fuel assemblies. The experimental results show that a typical PWR verification can be made in a total time of 30 min with an uncertainty in the measured mass of 2% at one standard deviation (1σ). A BWR verification can be made in 47 min with an uncertainty in the measured mass of 1.9% at 1σ, or a total time of 20 min with 1σ uncertainty in the measured mass of 2.5%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A description of structured waves in shock compressed particulate composites

Dynamic compression of composite materials is of scientific interest because the mechanical mismatch between internal phases challenges continuum theories. Typical assumptions about steady wave propagation and quasi-instantaneous state changes require reexamination along with the need for time-dependent models. To that end, data and models are presented here for the shock compression of an idealized particulate composite. To serve as a generic representative of this material class, a polymer matrix was filled with tungsten particles, ranging from 1 to 50 vol. %. This creates a simple microstructure containing randomly scattered particles with an extreme impedance mismatch to the binding matrix. These materials were parallel plate impact loaded by Al flyers traveling at 1.8–5.0 km/s. Velocimetry provided records of the equilibrium state and the compression wave structure for each case with trends quantified by an empirical fit. The same quantities were also studied as a function of the wave's propagation distance. A homogenized viscoelastic model then made it possible to progress from cataloging trends to making predictions. Starting from a Mie–Gruneisen equation of state, additional time varying terms were added to capture the transient response. After calibration, accurate predictions of the steady wave structure were possible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2020

Simulation of emergent compaction banding fronts caused by frictional boundaries

This study examines the role of boundary friction in promoting heterogeneous compaction in soft rock specimens loaded at high confining pressure outside the domain of compaction localisation. An elastoplastic constitutive model characterised by tunable hardening/softening behaviour is used to conduct the analyses. Finite-element simulations suggest that material instability is a non-necessary condition for the emergence of compaction fronts. Such fronts propagated as a result of a severe deviation in the local responses induced by frictional constraints. Overall, these findings suggest that boundary effects can bias the assessment of the extent of the compaction localisation domain. Experimental countermeasures and informed model calibration procedures are therefore necessary to minimise such bias and enable more accurate predictions of soft rock compaction.

42 ENGINEERING↗
DOE OSTI2020

Combined Effort to De-Inventory the New Brunswick Laboratory of Plutonium Certified Reference Materials - 20504

To de-inventory plutonium Certified Reference Materials standards (CRMs) from the New Brunswick National Laboratory (NBL), the Argonne National Laboratory (ANL) and Savannah River National Laboratory (SRNL) formed a unique team. This teaming arrangement leveraged the different capabilities and expertise of both laboratories, helping to ensure the effort would be performed both safely and cost-effectively. With the NBL then physically housed on the ANL site, ANL served as the host-site, providing the necessary facilities and support services (e.g., supplying everything from hoods, loading equipment, and scales to Health Physics supports). From a packaging perspective, ANL's responsibilities focused mainly on characterizing and preparing the contents for shipment, followed by performing the Shipper of Record activities. With SRNL being the Package Design Authority of the 9978, the Type B packaging used for this effort, SRNL's scope included developing a Safety Analysis Report in Packaging (SARP) Letter Amendment to add the CRMs as authorized contents to the 9978. Recognizing competency, ANL prodigiously tasked SRNL to perform the actual loading of the CRMs into the 9978s, while physically on the ANL site. This paper details five different aspects of this effort. They are: 1. Characterization/validation of the CRM contents. 2. Development of the 9978 SARP Letter Amendment for the content change. 3. Development of the nitrogen inerting method to address SARP identified potential flammability concerns. 4. Renewal of the SRNL 9978 loading procedure. 5. Performance of the actual work execution activities. In 2017, the first nine packages were successfully loaded and shipped from NBL, while in 2019, the last ten were successfully loaded and planned for shipment. Without ANL leveraging SRNL's strengths, the de-inventorying of NBL of its plutonium CRMs would have likely taken many years longer. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Revisiting the first Sandia Fracture Challenge with transient deformation heating and strain localization considerations

In this study, we present a systematic experimental study and accompanying theoretical analysis of the dependence of ductile fracture on strain localization, strain hardening rates, deformation-induced thermal softening, and transient heat conduction under spatially uniform as well as spatially heterogeneous deformation. Spatially uniform cases are studied via standard dogbone shaped specimens subject to uniaxial tension. Spatially heterogeneous deformation is studied via the so-called Sandia Fracture Challenge (SFC) specimen, which is a standard compact tension specimen modified with three machined holes in front of a blunt notch. We utilize the same precipitation hardened martensitic stainless steel used in the first SFC experiments, i.e. 15-5 PH with an H1075 heat treatment. We also study 15-5 PH in Condition A and with the H900 heat treatment, each of which has a different hardening behavior and ductility. We find that the ductility does not correlate with the deformation to first crack initiation in the SFC specimen. Instead, hardening rates are better correlated. Moreover, a re-examination finds that an accurate calibration of the hardening rates is strongly correlated with accurate blind predictions of ductile fracture in the SFC specimens. Given that thermal softening can greatly affect hardening rates, we provided a thermographic analysis of deformation-induced heating and transient heat transfer in both the dogbone shaped samples and the SFC specimens. Transient heating in stainless steels is found to have first-order effects on the strain at the onset of necking, strain to fracture, as well as strain localization and ductile fracture in complex geometries even under so-called quasi-static loading rates.

42 ENGINEERING↗
DOE OSTI2022

A continuum damage approach to spallation and the role of microinertia

Spall failure is of interest due to its prevalence in high strain rate problems in which the spallation is driven by the interaction of release waves. In this article, a porosity-based damage model that includes microinertial effects is used to examine spall failure. The model is successfully calibrated to plate impact-driven experiments and then used to evaluate experimental conditions producing more extreme strain rate conditions, such as those in laser-driven experiments. The incorporation of microinertia allows us to better understand the increase in apparent macroscopic spall strength seen at high strain rates. Correspondingly, we conclude that the incorporation of microinertial effects improves the model’s predictive capabilities. Microinertial effects result in more severe local tensile stresses that affect the damage evolution, and microinertia can play a significant role in the approach to the material’s ideal strength at extreme loading rates. Additionally, a preliminary parametric study is also carried out to investigate the role of microstructural aspects such as nucleation volume fraction and initial pore radius. One counter-intuitive result from the microinertial effects is that, for a given nucleation site volume fraction, having larger initial pore nucleation sites can lead to an improved macroscopic spall strength.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗