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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.
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JISEA Research Highlight: Integrated Model of Natural Gas Pipelines and the Electricity Grid
This study developed a market-based coordination framework that allows for exchanging information on gas off-takes as well as constraints between a power sector model. This includes unit commitment, economic dispatch, and a dynamic gas network simulation model. The framework was then tested using real power- and gas-system data from Colorado.
Investigating SOFC Electrode Microstructures and Performance Degradation: A High Throughput Integrated Model
Presented by NETL at MS&T 2019, 09/19
Integrated Modeling of U.S. Agricultural Soil Emissions of Reactive Nitrogen and Associated Impacts on Air Pollution, Health, and Climate
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AIIM: Advanced Infrastructure Integrity Modeling
TechConnect World Innovation Conference & Expo, Washington, DC, June 13-16, 2022
Integrated Modeling to Support Prescribed Fire
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Smart Infrastructure Integrity Models to Support Remediation and Inform Safe Reuse Strategies
Resource Sustainability Project Review Meeting, Pittsburgh, PA, October 25-28, 2022
Advancing the HERO WEC Through Integrated Modeling, Testing, and Field Deployments: Preprint
The Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) was developed by the National Laboratory of the Rockies as a modular platform for advancing wave-powered desalination technologies. Designed to operate in either a hydraulic or electric configuration, the system enables direct comparison of mechanical-to-water and electrical-to-water conversion pathways using a single hardware architecture. Deployments at the Jennette's Pier test site in 2022 and 2024 demonstrated freshwater production between 60 and 300 gallons per day, including successful operation in wave heights as low as 0.25 m. A structured evaluation approach combining numerical modeling of hydrodynamic and PTO response, controlled laboratory testing, and in-ocean field deployments has been used to characterize and refine system performance. Insights from these efforts are now informing the second-generation HERO WEC (V2), which incorporates improved drivetrain components, refined geometry, enhanced control systems, and design updates aimed at increasing robustness and long-duration survivability. The HERO WEC platform continues to serve as an open-access research asset supporting broader marine energy and desalination development.
Medical Updates Number 5 to the International Space Station Probability Risk Assessment (PRA) Model Using the Integrated Medical Model
The Integrated Medical Model (IMM) Project has been developing a probabilistic risk assessment tool, the IMM, to help evaluate in-flight crew health needs and impacts to the mission due to medical events. This package is a follow-up to a data package provided in June 2009. The IMM currently represents 83 medical conditions and associated ISS resources required to mitigate medical events. IMM end state forecasts relevant to the ISS PRA model include evacuation (EVAC) and loss of crew life (LOCL). The current version of the IMM provides the basis for the operational version of IMM expected in the January 2011 timeframe. The objectives of this data package are: 1. To provide a preliminary understanding of medical risk data used to update the ISS PRA Model. The IMM has had limited validation and an initial characterization of maturity has been completed using NASA STD 7009 Standard for Models and Simulation. The IMM has been internally validated by IMM personnel but has not been validated by an independent body external to the IMM Project. 2. To support a continued dialogue between the ISS PRA and IMM teams. To ensure accurate data interpretation, and that IMM output format and content meets the needs of the ISS Risk Management Office and ISS PRA Model, periodic discussions are anticipated between the risk teams. 3. To help assess the differences between the current ISS PRA and IMM medical risk forecasts of EVAC and LOCL. Follow-on activities are anticipated based on the differences between the current ISS PRA medical risk data and the latest medical risk data produced by IMM.
Mixed models and reduced/selective integration displacement models for nonlinear shell analysis
One of the objectives of the present investigation is to identify classes of equivalent mixed models and reduced/selective integration displacement models for curved shell structures. A second objective is concerned with the identification of the spurious modes exhibited by various mixed models and their equivalent reduced integration displacement models. The merits of using mixed models are also evaluated. The mixed elements developed in the current investigation differ from those presented by Noor and Hartley (1977) by the fact that the stress resultants are discontinuous at interelement boundaries and, therefore, are eliminated on the element level. The high accuracy and effectiveness of the elements developed is demonstrated by means of numerical examples, taking into account three geometrically nonlinear problems of shallow shells.
Development of a Validation Approach for an Integrated Waste Glass Melter Model
Integrated models are being developed to represent the physics occurring within the high-level and low-activity waste melters that will be used to vitrify legacy tank waste at the Hanford site. These models couple the melt pool, cold cap, and plenum region within a single computational domain. Validation of the models is essential to ensure the reliability of the numerical predictions of the operational melters. Experimental data from laboratory- and pilot-scale tests are thus being used to inform and validate various aspects of the melter model. This study presents a tiered approach to model validation consisting of a series of progressively more complex test cases designed to model the physics occurring in the full-scale system. A hierarchical methodology has been developed to segregate and simplify the physical phenomena affecting the multiphase flow and heat transfer within a waste glass melter. Finally, four hierarchical levels are defined in a validation pyramid and built up in levels of increasing complexity from unit problems to subsystem cases, to pilot-scale systems, and then to the full-scale system.
Modeling jets in cross flow
Various approaches to the modeling of jets in cross flow are reviewed. These are grouped into four classes, namely: empirical models, integral models, perturbation models, and numerical models. Empirical models depend largely on the correlation of experimental data and are mostly useful for first-order estimates of global properties such as jet trajectory and velocity and temperature decay rates. Integral models are based on some ordinary-differential form of the conservation laws, but require substantial empirical calibration. They allow more details of the flow field to be obtained; simpler versions have to assume similarity of velocity and temperature profiles, but more sophisticated ones can actually calculate these profiles. Perturbation models require little empirical input, but the need for small parameters to ensure convergent expansions limits their application to either the near-field or the far-field. Therefore, they are mostly useful for the study of flow physics. Numerical models are based on conservation laws in partial-differential form. They require little empirical input and have the widest range of applicability. They also require the most computational resources. Although many qualitative and quantitative features of jets in cross flow have been predicted with numerical models, many issues affecting accuracy such as grid resolution and turbulence model are not completely resolved.
Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model
Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.
Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models
Integrated assessment models are extensively used in the analysis of climate change mitigation and are informing national decision makers as well as contribute to international assessments such as those of the Intergovernmental Panel on Climate Change. This paper conducts a comprehensive review of techno-economic assumptions in the electricity sector among sixteen different global and national integrated assessment models. Particular focus is given to six major economies in the world: Brazil, China, the EU, India, Japan and the US. The comparison reveals that techno-economic characteristics are quite different across integrated assessment models, both for the base year and future years. It is, however, important to recognize that techno-economic assessments from the literature exhibit an equally large range of parameters as the integrated assessment models reviewed. Beyond numerical differences, the representation of technologies also differs among models which needs to be taken into account when comparing numerical parameters. While desirable from a first principles perspective and often called for, it seems difficult to fully harmonize techno-economic parameters across a broader range of models due to structural differences in the representation of technology. Therefore, in the future making techno-economic parameters available together with of the technology representation as well as the exact definitions of the parameters should become the standard approach as it allows an open discussion of appropriate assumptions.
Mixed models and reduced/selective integration displacement models for nonlinear analysis of curved beams
Simple mixed models are developed for use in the geometrically nonlinear analysis of deep arches. A total Lagrangian description of the arch deformation is used, the analytical formulation being based on a form of the nonlinear deep arch theory with the effects of transverse shear deformation included. The fundamental unknowns comprise the six internal forces and generalized displacements of the arch, and the element characteristic arrays are obtained by using Hellinger-Reissner mixed variational principle. The polynomial interpolation functions employed in approximating the forces are one degree lower than those used in approximating the displacements, and the forces are discontinuous at the interelement boundaries. Attention is given to the equivalence between the mixed models developed herein and displacement models based on reduced integration of both the transverse shear and extensional energy terms. The advantages of mixed models over equivalent displacement models are summarized. Numerical results are presented to demonstrate the high accuracy and effectiveness of the mixed models developed and to permit a comparison of their performance with that of other mixed models reported in the literature.
IAM-FIRE: a Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models
Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the coming years. We introduce IAM-FIRE (Integrated Assessment Model – Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), we report projections BA and CE derived from IAM-FIRE for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for total BA, including the observed global decline since the early 2000s, and for forest BA. Projected fire trajectories differ strongly among scenarios: total BA range from declines under SSP1-2.6 (-3.36 Mha yr-1) to increases under SSP3-6.6 (+1.6 Mha yr-1). Corresponding total CE show a similar divergence ranging from -15 to +10.6 TgC yr-1. Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO2-driven increases in vegetation productivity amplify fire risk, particularly under high-emissions pathways. Compared with CMIP6 fire-enabled Earth System Models, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting substantial structural uncertainties in future fire projections. By providing a computationally efficient and internally consistent approach to represent wildfire impacts within IAMs, IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.
Model Evaluation and Hindcasting: An Experiment with an Integrated Assessment Model
Integrated assessment models have been extensively used for analyzing long term energy and greenhouse emissions trajectories and have influenced key policies on this subject. Though admittedly these models are focused on the long term trajectories, how well these models are able to capture historical dynamics is an open question. In a first experiment of its kind, we present a framework for evaluation of such integrated assessment models. We use Global Change Assessment Model for this zero order experiment, and focus on the building sector results for USA. We calibrate the model for 1990 and run it forward up to 2095 in five year time steps. This gives us results for 1995, 2000, 2005 and 2010 which we compare to observed historical data at both fuel level and service level. We focus on bringing out the key insights for the wider process of model evaluation through our experiment with GCAM. We begin with highlighting that creation of an evaluation dataset and identification of key evaluation metric is the foremost challenge in the evaluation process. Our analysis highlights that estimation of functional form of the relationship between energy service demand, which is an unobserved variable, and its drivers is a significant challenge in the absence of adequate historical data for both the dependent and driver variables. Historical data availability for key metrics is a serious limiting factor in the process of evaluation. Interestingly, service level data against which such models need to be evaluated are itself a result of models. Thus for energy services, the best we can do is compare our model results with other model results rather than observed and measured data. We show that long term models, by the nature of their construction, will most likely underestimate the rapid growth in some services observed in a short time span. Also, we learn that modeling saturated energy services like space heating is easier than modeling unsaturated services like space cooling and understanding that how far a service is from its saturation level is a key question which we probably don’t have an answer to. Finally and most importantly, even if long term models partially miss the short term dynamics, the long term insights provides by these models is fairly robust. We conclude by highlighting that our work is the first step in the much wider process of integrated assessment model evaluation and will hence have its own limitations. Future evaluation research work should build upon this zero order experiment for improving our modeling of human and coupled earth systems.
Optimization Routine for Generating Medical Kits for Spaceflight Using the Integrated Medical Model
The Integrated Medical Model (IMM) is a MATLAB model that provides probabilistic assessment of the medical risk associated with human spaceflight missions.Different simulations or profiles can be run in which input conditions regarding both mission characteristics and crew characteristics may vary. For each simulation, the IMM records the total medical events that occur and “treats” each event with resources drawn from import scripts. IMM outputs include Total Medical Events (TME), Crew Health Index (CHI), probability of Evacuation (pEVAC), and probability of Loss of Crew Life (pLOCL).The Crew Health Index is determined by the amount of quality time lost (QTL). Previously, an optimization code was implemented in order to efficiently generate medical kits. The kits were optimized to have the greatest benefit possible, given amass and/or volume constraint. A 6-crew, 14-day lunar mission was chosen for the simulation and run through the IMM for 100,000 trials. A built-in MATLAB solver, mixed-integer linear programming, was used for the optimization routine. Kits were generated in 10% increments ranging from 10%-100% of the benefit constraints. Conditions wheremass alone was minimized, volume alone was minimized, and where mass and volume were minimizedjointly were tested.