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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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USM3D-ME Contributions to the 5th AIAA High Lift Prediction Workshop

This paper presents the results of Reynolds-averaged Navier-Stokes (RANS) simulations conducted by the NASA flow solver, mixed-element USM3D (USM3D-ME), for the 5th AIAA High-Lift Prediction Workshop. As part of the Fixed-Grid RANS Technology Focus Group (TFG), these simulations were performed to assess the accuracy and efficiency of the USM3D-ME solutions in predicting high-lift flows. The High-Lift Common Research Model (CRM-HL) served as the primary geometry. Several CRM-HL configurations were used for three case studies: a verification study (Case 1), a configuration buildup study (Case 2), and a Reynolds-number variation study (Case 3). Overall, USM3D-ME RANS results aligned with the solutions selected by the Fixed-Grid RANS TFG and available wind tunnel data. Simulations for Case 1 and Configuration 2.1 achieved machine-zero residual convergence, with aerodynamic coefficients converging to steady-state values. However, Configurations 2.2-2.4 and Case 3 encountered iterative- and grid-convergence challenges, particularly at high angles of attack. Compared with the experimental data available for Configurations 2.2-2.4, close agreement was demonstrated at low angles of attack. However, for angles of attack approaching the maximum lift conditions, the predicted lift coefficient and pitching moment deviated from experimental values. The drag-coefficient predictions were in relatively good agreement, however, slight overpredictions were observed at the highest angle of attack corresponding to the maximum-lift condition. Although iterative convergence for Configurations 2.2-2.4 at high angles of attack remains a persistent challenge, averaging aerodynamic coefficients over the last 5000 iterations yielded satisfactory agreement with the available wind tunnel experimental data. During the workshop, the lack of iterative convergence was attributed to the vortex structures emanating from the slat brackets. To investigate this issue further, post-workshop simulations were conducted on Configuration 2.2. In one study, RANS simulations were performed on a simplified geometry with the slat brackets removed. The second study focused on performing unsteady RANS (URANS) simulations on the original Configuration 2.2 geometry. Preliminary results from both studies are presented and compared with wind tunnel data for Configuration 2.2. Consistent with the findings of other participants in the Fixed-Grid RANS TFG, this study emphasizes the necessity for further exploration and advancement in RANS technology for predicting high-lift flows.

Aerodynamics↗
DOE OSTI2020

Integrating Energy Efficiency Strategies with Industrialized Construction for Our Clean Energy Future: Preprint

NREL’s Industrialized Construction Innovation Team has developed an ambitious plan to accelerate the integration of energy efficiency (EE) strategies with Industrialized Construction (IC). The United States (U.S.) construction industry is beginning to use IC methods to build multifamily apartment buildings to address affordability and labor shortages. Apart from reducing cost of construction and delivery times, the IC method of permanent modular construction has the potential to facilitate the integration of a wide range of EE strategies and advanced controls into such buildings. While there may be unintended EE benefits to IC such as a tighter envelope due to higher construction quality, the process has not been leveraged specifically to enhance EE. NREL aims to claim this missed opportunity and integrate IC benefits with EE as well as advanced controls, distributed energy resources, and grid-friendly design strategies. The paper proposes an ‘IC Assessment Framework’ to achieve affordable zero-energy modular multifamily buildings. Through the selection criteria of Design for Manufacturing and Assembly, the framework aims to distill a broad range of proven EE strategies for site-built into a set of strategies that qualify as easy to integrate for off-site. The output is a Factory Information Model (FIM) that represents a process-based digital twin to enable advanced time-and-motion study, plugs into open source building energy modeling platform (EnergyPlus), and serves as a vital tool facilitating wider adoption of EE integration. Conclusively, the paper delineates next steps for upcoming pilots with NREL’s IC partners towards developing a transformational pathway for our Clean Energy Future.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Nucleophilic Thiols Reductively Cleave Ether Linkages in Lignin Model Polymers and Lignin

Lignin may serve as a renewable feedstock for the production of chemicals and fuels if mild, scalable processes for its depolymerization can be devised. The use of small organic thiols represents a bioinspired strategy to cleave the β–O–4 bond, the most common linkage in lignin. In the present study, synthetic β–O–4 linked polymers were treated with organic thiols, yielding up to 90 % cleaved monomer products. Lignin extracted from poplar was also treated with organic thiols resulting in molecular weight reductions as high as 65 % (Mn) in oxidized lignin. Thiol–based cleavage of other lignin linkages was also explored in small–molecule model systems to uncover additional potential pathways by which thiols might depolymerize lignin. The success of thiol–mediated cleavage on model dimers, polymers, and biomass–derived lignin illustrates the potential utility of small redox–active molecules to penetrate complex polymer matrices for depolymerization and subsequent valorization of lignin into fuels and chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Parabolized Navier-Stokes analysis of ducted turbulent mixing problems with finite-rate chemistry

A hybrid implicit/explicit approach for analyzing 2D ducted turbulent mixing problems with finite-rate chemistry is presented. The approach combines a fully-implicit parabolic mixing algorithm, an explicit viscous-characteristic based shock-capturing algorithm, and linearized implicit chemical kinetic algorithm. The resultant model provides spatial marching solutions of the parabolized Navier-Stokes (PNS) equations for supersonic combustion and viscous nozzle flowfields. Specialized procedures are incorporated to deal with the near wall sublayer and with small central pockets of subsonic flow. A parabolic option is available which can be utilized in the direct or inverse mode for design applications. The ability of the model to treat shock waves and wave/mixing layer interactions is assessed via comparisons of predictions with those of well established explicit shock-capturing Euler (SCIPPY) and PNS (SCIPVIS) models. Applications to a variety of supersonic combustion flowfield problems involving tangential or moderately inclined fuel injection, and, to a viscous nozzle flow problem, are presented. This paper serves to exhibit overall capabilities of the model developed and no comparisons with supersonic combustion data are presented. Such comparisons serve mainly to validate the modeling of the turbulence and turbulence/chemical interactions and will be the subject of a future paper.

Sinha, N.↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Constraints on the Parameterization of Convective Cloud Systems from Analyses of ARM Observations and Models

Anthony D. Del Genio has served as principal investigator since the inception of ASR in 2009 as co-chair of the Cloud Lifecycle Working Group (CLWG) and in connection with that as a member of the ASR Science and Infrastructure Steering Committee. In this role he has been intimately involved with oversight of existing and proposed ASR Focus Groups (Vertical Velocity, QUICR, Ice Properties) and thematic interest groups (Mesoscale Organized Convection, Warm Low Clouds, Mixed-Phase Clouds, MJO, Entrainment) whose activities fall under the purview of CLWG. The PI actively participates himself in the Mesoscale Organized Convection and MJO groups, is a Co-Investigator on two successfully implemented IOPs (AMIE, MC3E), and was an unfunded Collaborator on the recently completed FASTER project at BNL under the DOE Modeling Program. The PI has also spent the past three years as a member of the ARM Science Board, which reviews proposals to conduct IOPs at the permanent ARM sites and to deploy the ARM Mobile Facilities. Finally, the PI was a participant in the first ARM-European workshop in 2012 that eventually led to the creation of the SGP supersite concept. This report includes an annotated list of published research results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combined Thermal-Water Microgrid Model

SAND2022-3874 O The Combined Thermal-Water Microgrid Model is a set of three models combined with controls algorithms for a Water Thermal Storage (WTS), Ground Source Heat Pump (GSHP), and Thermal Bridge Loop (TBL) that serves a residential neighborhood. The model allows a parametric investigation of volume of water storage versus number of ground heat exchanger bore-holes versus seasonal energy efficiency measures. The code is being released as open source so that the entire study can be replicated per an open access journal publication. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Villa, Daniel↗
DOE OSTI2020

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1974

Operations analysis (study 2.1): Payload designs for space servicing

Potential modes of operating in space in the space shuttle era are documented. The October 1973 NASA Mission Model provides a definition of various NASA and non-DOD automated payload configurations when employed in an expendable mode. The model also specifies a launch schedule for initial deployment of payloads as well as for subsequent replacements at periodic cycles. This model and its associated payload definitions serve as a foundation for the data presented in this report. The reference model has been revised to reflect automated space servicing of payloads as an operational concept instead of the existing expendable approach. The indication is that the bulk of a payload's subsystems and mission equipment require no support over the lifetime of the program. However, failure of a single unit could result in loss of the mission objectives. When space servicing is employed, the approach is to replace only that unit causing the anomaly. This concept affords an opportunity to standardize space replacable units, as well as to reduce the expense of logistics support, by allowing multiple servicing on any single upper stage/shuttle flight.

Wolfe, R. R.↗
NASA NTRS2007

CFD Analysis of Tile-Repair Augers for the Shuttle Orbiter Re-Entry Aeroheating

A three-dimensional aerothermodynamic model of the shuttle orbiter's tile overlay repair (TOR) sub-assembly is presented. This sub-assembly, which is an overlay that covers the damaged tiles, is modeled as a protuberance with a constant thickness. The washers and augers that serve as the overlay fasteners are modeled as cylindrical protuberances with constant thicknesses. Entry aerothermodynamic cases are studied to provide necessary inputs for future thermal analyses and to support the space-shuttle return-to-flight effort. The NASA Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) is used to calculate heat transfer rate on the surfaces of the tile overlay repair and augers. Gas flow is modeled as non-equilibrium, five species air in thermal equilibrium. Heat transfer rate and surface temperatures are analyzed and studied for a shuttle orbiter trajectory point at Mach 17.85. Computational results show that the average heat transfer rate normalized with respect to its value at body point 1800 is about BF=1.9 for the auger head. It is also shown that the average BF for the auger and washer heads is about BF=2.0.

Mazaheri, Ali R.↗
DOE OSTI2026

Heat Transfer Experiments of a 1st Stage Blade Cascade for Supercritical CO2 Oxy-Combustion Turbine Application

The results of internally cooled 1st stage blade (S1B) cascade testing in a supercritical CO2 environment is presented. The turbine blade design has been previously established for the end application of an oxy-combustion turbine operating in the Allam-Fetvedt cycle with turbine inlet conditions of 305 bar and 1150°C. The internally cooled blade features leading edge (LE) region impingement cooling, mid-section ribbed serpentine passages, and a pin-finned trailing edge (TE) region before cooling ejection holes. The geometry for the tested blade cascade has a cooled central blade with un-cooled blades on either side to match flowpath areas of the actual turbine. The flowpath reuses internal components previously employed for mid-section region ribbed serpentine passage experiments that established Nusselt number enhancement ratios over a range of Reynolds numbers from 100,000-400,000. New components include flow conditioning plates upstream and downstream of the blade cascade to adequately represent the flow field and blade external heat transfer coefficient profiles for the actual turbine. The cooled central blade utilizes uniform crystal temperature sensors (UCTS) with six sensors each on the blade pressure and suction surfaces distributed radially and from LE to TE. The post-processed UCTS quantified the maximum wall temperature seen at each installed sensor location. The test procedure consisted of establishing supercritical CO2 cooling flow temperature and flow rate and maintaining it throughout the test. The flow rate aims to match that for the actual in-service turbine blade design and is maintained through an orifice restriction to keep the pressure differential between internal cooling flow and external hot flow nearly constant. For the sCO2 flow path external to the blade, temperatures were ramped throughout the test via control of the test loop’s natural gas burner heater. The maximum temperature seen was 468°C and held constant for a duration of 10 minutes at which the blade metal temperature was predicted to be at its maximum before ramping down. For the turbine blade design for service inlet conditions, external flow path computational fluid dynamics (CFD) results and an internal cooling 1-D thermal and hydraulic flow network model using experimentally validated correlations served as thermal finite element (FE) boundary conditions to predict blade metal temperatures. These predicted temperatures were subsequently utilized in a structural FE model to predict blade life ratings dictated by Haynes 282 creep strength data, having a strong dependence on temperature. The boundary conditions experienced during testing are used in the same workflow and compared to the experimental results, with the goal of validating the analysis methodology and providing insight on the uncertainty in local metal temperature predictions.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2012

Crossing the Virtual World Barrier with OpenAvatar

There are multiple standards and formats for 3D models in virtual environments. The problem is that there is no open source platform for generating models out of discrete parts; this results in the process of having to "reinvent the wheel" when new games, virtual worlds and simulations want to enable their users to create their own avatars or easily customize in-world objects. OpenAvatar is designed to provide a framework to allow artists and programmers to create reusable assets which can be used by end users to generate vast numbers of complete models that are unique and functional. OpenAvatar serves as a framework which facilitates the modularization of 3D models allowing parts to be interchanged within a set of logical constraints.

Joy, Bruce↗
DOE OSTI2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of 108 Ru, 110 Ru, and 116 Pd were measured to a relative mass precision $\delta$⁢$m/m$ ≈ 10 -8 via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. Further, these mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Shifting mineral and redox controls on carbon cycling in seasonally flooded mineral soils

Although wetland soils represent a relatively small portion of the terrestrial landscape, they account for an estimated 20%–30% of the global soil carbon (C) reservoir. C stored in wetland soils that experience seasonal flooding is likely the most vulnerable to increased severity and duration of droughts in response to climate change. Redox conditions, plant root dynamics, and the abundance of protective mineral phases are well-established controls on soil C persistence, but their relative influence in seasonally flooded mineral soils is largely unknown. To address this knowledge gap, we assessed the relative importance of environmental (temperature, soil moisture, and redox potential) and biogeochemical (mineral composition and root biomass) factors in controlling CO 2 efflux, C quantity, and organic matter composition along replicated upland–lowland transitions in seasonally flooded mineral soils. Specifically, we contrasted mineral soils under temperature deciduous forests in lowland positions that undergo seasonal flooding with adjacent upland soils that do not, considering both surface (A) and subsurface (B and C) horizons. We found the lowland soils had lower total annual CO 2 efflux than the upland soils, with monthly CO 2 efflux in lowlands most strongly correlated with redox potential (E h ). Lower CO 2 efflux as compared to the uplands corresponded to greater C content and abundance of lignin-rich, higher-molecular-weight, chemically reduced organic compounds in the lowland surface soils (A horizons). In contrast, subsurface soils in the lowland position (C g horizons) showed lower C content than the upland positions (C horizons), coinciding with lower abundance of root biomass and oxalate-extractable Fe (Fe o , a proxy for protective Fe phases). Our linear mixed-effects model showed that Fe o served as the strongest measured predictor of C content in upland soils, yet Fe o had no predictive power in lowland soils. Instead, our model showed that E h and oxalate-extractable Al (Al o , a proxy of protective Al phases) became significantly stronger predictors in the lowland soils. Combined, our results suggest that low redox potentials are the primary cause for C accumulation in seasonally flooded surface soils, likely due to selective preservation of organic compounds under anaerobic conditions. In seasonally flooded subsurface soils, however, C accumulation is limited due to lower C inputs through root biomass and the removal of reactive Fe phases under reducing conditions. Our findings demonstrate that C accrual in seasonally flooded mineral soil is primarily due to low redox potential in the surface soil and that the lack of protective metal phases leaves these C stocks highly vulnerable to climate change.

58 GEOSCIENCES↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗
DOE OSTI2022

The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake

In this paper, a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at a low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics. This is achieved by creating so-called observation points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computationally lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to Simulator for Offshore Wind Farm Applications (SOWFA) simulations in three- and nine-turbine cases under static and changing environmental conditions. The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three-turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model-based dynamic wind farm control.

17 WIND ENERGY↗
NASA NTRS2005

Crater Lakes on Mars: Development of Quantitative Thermal and Geomorphic Models

Impact craters on Mars have served as catchments for channel-eroding surface fluids, and hundreds of examples of candidate paleolakes are documented [1,2] (see Figure 1). Because these features show similarity to terrestrial shorelines, wave action has been hypothesized as the geomorphic agent responsible for the generation of these features [3]. Recent efforts have examined the potential for shoreline formation by wind-driven waves, in order to turn an important but controversial idea into a quantitative, falsifiable hypothesis. These studies have concluded that significant wave-action shorelines are unlikely to have formed commonly within craters on Mars, barring Earth-like weather for approx.1000 years [4,5,6].

Barnhart, C. J.↗