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NASA NTRSDate not supplied

Enabling Nuclear and Solar Thermal Propulsion with the Computational Laboratory: Faster and Cheaper Materials Characterization

Nuclear and solar thermal propulsion offer enhanced efficiencies for in-space travel which may open up the outer Solar System to both crewed and automated craft. However, these advanced propulsion systems require cutting-edge materials which are often not easily tested in the laboratory. Ab initio thermodynamics and other computational methods offer a way to test materials at high pressures and temperatures more quickly, and with reduced cost. Here, several case studies are presented which highlight the utility of modern simulation techniques. First, an extensive thermodynamic study was performed at the ab initio level to elucidate the role of hot hydrogen flow on the erosion of four refractory carbides to examine their appropriateness for use as channel coatings. A detailed analysis of reaction products informs estimates of erosion which are validated in comparison with heritage NERVA data. Specific material suggestions are made for both nuclear and solar designs. Secondly, we examined the mechanical behavior and chemical stability of various components of an idealized nuclear cermet core design using techniques spanning from the atomic to microscale. Next, the mechanical and chemical behavior of tungsten (a potential matrix material) and the stability of uranium mononitride (a potential ceramic fuel element), are characterized in the presence of hydrogen and at elevated temperatures with ab initio thermodynamics. Finally, the mechanical response and failure of tungsten under tensile strain is simulated at the micron scale with a combined finite element and dislocation dynamics approach. The resultant stress-strain data agrees well with experiments and leads to a workflow which can incorporate ab initio thermodynamic data into micron-scale simulations and thus provide real-world relevant estimates of erosion and stress-strain behavior. The power of this framework and our plans to use it in the future will be discussed.

William C Tucker↗

NASA NTRSDate not supplied

Boundary Representation Tolerance Impacts on Mesh Generation and Adaptation

The control of discretization error is critical to obtaining reliable simulation results. Re-cent progress has matured anisotropic mesh adaptation, which automates discretization error control for complex geometries. However, the meshing process can fail when geometric model Boundary REPresentation (BREP) tolerances are larger than boundary layer surface nor-mal and tangential spacing requirements. Geometry sources are often created in Mechanical Computer-Aided Design (MCAD) systems, which are designed to produce models for manufacturing and not the stricter requirements of viscous flow analysis. BREP tolerances are strained by three factors: the inherent complexity of the model (e.g., large range of scales and complex topology), the numerical difficulties arising from surface/surface intersections, and the omission of crucial data during export and translation. Manual preparation of geometry is commonly employed to enable expert-guided mesh generation, which severely inhibits work-flow automation. Accommodation of loose BREP tolerances is required for automated mesh processes, because barrier issues may not be detected until well into the solution process. To raise awareness of this class of geometry issues and their impact on simulation, a survey of these barrier issues is presented with example mitigation techniques. This awareness may also impact geometry creation workflows to prevent the introduction of these artifacts.

CAD↗

NASA NTRSDate not supplied

Expanding Biological Repository Data Available for Sharing and Knowledge Discovery

Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.

Ryan T Scott↗

NASA NTRS2021

Perceive: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures

National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns. This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated model scan match or outperform existing manual practices.

ASRS↗

NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

Natural Language Processing↗

NASA NTRSDate not supplied

Analysis of MSL/MEDLI Entry Data with Coupled CFD and Material Response

The Mars Science Laboratory (MSL) was protected during its atmospheric entry by an instrumented heatshield using NASA's Phenolic Impregnated Carbon Ablator (PICA) material [1]. PICA is a lightweight carbon fiber/polymeric resin material that offers outstanding performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of material response and atmospheric entry. MEDLI recorded, among other things, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP) [2]. The objective of this work is to showcase and analyze the coupling between the material response and the aerothermal environment. As shown in Figure 1, the workflow is divided into the following steps. First, the aerothermal properties are computed in the Data Parallel Line Relaxation (DPLR) code [3] and used with the Nonequilibrium air radiation (NEQAIR) program [8] to compute radiative heating. Second, the thermal response inside the material is computed in the Porous material Analysis Toolbox based on OpenFOAM (PATO) [4,5,6] using a fixed blowing correction parameter. Third, the pyrolysis gases computed in PATO are used as inputs to a blowing boundary condition within DPLR. Fourth, the new environment properties from DPLR are used in NEQAIR to provide an updated solution, then both the updated aerothermal environment and radiative heating are used in PATO without blowing correction. The third and fourth steps are then repeated until convergence in surface temperature is obtained. Convergence in the radiative heating is generally achieved before surface temperature, at which point the radiative heating is no longer updated. Char mass loss rates are forced to zero to produce a non-receding surface condition. For early time points in the trajectory, where flow around the MSL aeroshell is rarefied, the Direct Simulation Monte Carlo (DSMC) code, SPARTA [7], is used to compute the aerothermal environment. Iteration between PATO and SPARTA is not performed due to the computational cost of DSMC simulations. Preliminary results of the coupling between PATO and DPLR for the MSL heatshield atmospheric entry model are presented in Figures 2-4 at 65 seconds after entry interface. Figure 2 shows the surface temperature results from an uncoupled simulation in PATO with the blowing correction parameter applied (left) along with the coupled surface temperature after iteration (right). Figure 3 shows the surface temperature along the centerline from windward to leeward for easier comparison. Figure 4 shows the coupled and uncoupled pyrolysis gas blowing rate. Mars 2020 used a similar heatshield consisting of PICA for thermal protection during entry, descent, and landing. In preparation for Mars 2020 post-flight analysis, the predictive material response capability is benchmarked against flight data from MEDLI. This work represents an important milestone toward the development of validated predictive capabilities for designing thermal protection systems for planetary probes.

Thermal Protection Systems↗

NASA NTRS2021

Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications

The exceptional progress in the field of machine learning (ML) in recent years has attracted a lot of interest in using this technology in aviation. Possible airborne applications of ML include safety-critical functions, which must be developed in compliance with rigorous certification standards of the aviation industry. Current certification standards for the aviation industry were developed prior to the ML renaissance without taking specifics of ML technology into account. There are some fundamental incompatibilities between traditional design assurance approaches and certain aspects of ML-based systems. In this paper, we analyze the current airborne certification standards and show that all objectives of the standards can be achieved for a low-criticality ML-based system if certain assumptions about ML development workflow are applied.

Avionics↗

NASA NTRSDate not supplied

Certification Considerations for Adaptive Stress Testing of Airborne Software

eduAdaptive Stress Testing (AST) has shown promise in identifying errant corner cases in complex software used in aerospace applications including Flight Management Systems (FMS). The strength of AST is performing test-based verification of complex aerospace software intensive systems at scale in simulated operational environments.Simulating and capturing the realistic operational complexities in integrated verification environments may exposeflaws in the softwareprior to field deployment, whereas the software may perform just fine to traditional requirements-basedunit and component level testing.AST can be used to test the whole system.Individual components may behave safely, but together can result in complex interactions and emergent failures, so it is important to test at the integrated system level.Motivated by the observed benefitsat the prototype proof of concept scale, this paper considers how AST may be integrated into a production workflow and used to generate objective evidence in a processthat delivers certified aerospace software.The research includes evaluation of alignment with both DO-178C and Overarching Properties(OP). The paper addresses questions such as “where should AST fit in the Plan for Software Aspects of Certification (PSAC) and Software Verification Plan (SVP), what aspects of AST do not fit, and what objectives does it satisfy?” The paper concludes that AST is in fact useful at locating errors in complex airborne application software and in doing so provides benefits to suppliers and end users. Furthermore, AST appears appropriate to add value in both DO-178Cbased and Overarching Properties based certification approaches.

certification↗

NASA NTRSDate not supplied

Visualizing UPSP Data with Python

The Unsteady Pressure-Sensitive Paint (uPSP) projects uses Pressure-Sensitive paint applied over aerospace models during wind tunnel testing to collect pressure data with high spatial and temporal resolution in order to inform unsteady aerodynamics studies. For each of the 800+ experimental runs, four cameras generate up to 50 GB of video data, which must then be processed, analyzed, and visualized on the NASA Advanced Supercomputing system (NAS) to assess the result. One of the final data analysis products is the dynamic modal decomposition (DMD) results, which decomposes the pressure reading signals by their frequency component. The goal of this project is to visualize the DMD results over a 3D rendering of the model, using efficient and parallelized python routines. The software uses the pytecplot library, a high-level API that connects python scripting to a Tecplot 360 engine. Tecplot is an industry standard high-performance visualization tool that can handle large datasets and workflow. Various animation, rendering, and image-combination techniques were investigated to generate the final videos using OpenCV on the NAS. The final result is a software tool that takes in data products from the uPSP processing chain and generates high resolution visualization videos in parallel for every data file, allowing researchers to view their results efficiently and at an unprecedentedly detailed level.

Emma Dolores McMillian↗

NASA NTRS2021

Southwest Water Resources: Monitoring Surface Water Extents of Remote Stock Ponds in the Southwestern United States Using Earth Observing Systems for Enhanced Water Resources Management

Due to increasingly frequent and severe drought conditions in the southwestern US, land managers and livestock producers need to monitor stock ponds with increasing regularity. The ability to assess stock pond water levels with Earth observing satellite systems would enhance monitoring efforts of partners at the US Forest Service, Arizona Department of Game and Fish, and the Diablo Trust. This study employed Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to monitor surface water extent for hundreds of critical stock ponds in Arizona. Using methods adapted from previously developed image processing workflows, this project conducted a time-series analysis to capture seasonal and interannual variations in surface water area between 2013 to 2021. In addition, end users can monitor the surface water extent of stock ponds through the developed Google Earth Engine software tool called Surface Water Identification and Forecasting Tool (SWIFT). SWIFT incorporates the Automated Water Extraction Index, Modified Normalized Difference Water Index, and Tasseled Cap-Wetness Index for optical imagery and the incidence angle, VV and VH polarization bands for Sentinel-1 imagery to detect small water bodies in the study area with an overall accuracy range of 88-93%. These tools will empower our partners to monitor the extents of water in their stock ponds remotely, enabling them to develop data-informed and sustainable management solutions for decades to come.

Rainey Aberle↗

NASA NTRSDate not supplied

A Review of Extra-Terrestrial Regolith Excavation Concepts and Prototypes

Regolith is present on many extra-terrestrial bodies, and the crushed rock material it is made of contains many of the resources that are enabling for In-Situ Resource Utilization (ISRU). When extracted these resources can be used to provide consumables such as rocket propellant, human life support, working fluids and gases for industrial processes and feedstocks for manufacturing. In addition, the regolith can also be very beneficial for construction purposes as an aggregate which can be used for construction materials and shielding for radiation protection and micrometeorite impact. Binders for regolith concrete may also be made from geopolymers that may be in the regolith. The regolith can be melted and drawn out into glass fibers and used as reinforcements in a metal, polymer, or concrete matrix. In addition, there is tremendous scientific and geological knowledge that can only be obtained by studying samples of the regolith. However, none of these valuable activities can proceed without first acquiring the regolith granular material with some type of excavation device and method. Excavation is in the critical path of many workflows that will make up the capabilities required to establish a human and robotic presence in our solar system. While scientific in-situ sampling of regolith in small quantities has been achieved since the dawn of the space age in the 1960’s, large scale excavation for mining and construction on extra-terrestrial bodies has only been contemplated for many decades in works of scientific fact and also in fictional stories, but serious development and prototyping of excavation technologies for use in reduced gravity space environments was only started in the late 1990’s.This paper will review and document the evolution of extra-terrestrial excavation concepts and prototypes based on the available literature and the personal experience of the author who has been working on regolith excavation technology development since 1998.

ISRU↗

NASA NTRSDate not supplied

Application of Agile for Systems Engineering, Project Management and Modeling and Lessons Learned

The Systems Engineering team within the Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has been transforming its development processes to be more efficient, robust,and responsive to change and to its stakeholders. To these ends, the Systems Engineering team trialed the integration of agile development techniques into existing and new processes. Agile development methods are well understood within the software development community. Outside ofsoftware development, however, how non-software project management (PM) and systems engineering (SE) teams implement agile development techniques is less well understood. In its transformation efforts, the ExMC SE team focused on three main areas: Improving the project communications among subsystem teams and stakeholders by adopting a scrum-like process, Changing the status and reporting mechanisms to improve schedule coordination between the subsystem team, SE leadership, and ExMC Element leadership, and Unifying the model-based SE workflow to improve understanding of Concepts of Operations across projects.This presentation highlights several of these transformations and what the SE team learned while undergoing the transformation.

S Lumpkins↗

NASA NTRSDate not supplied

Modeling and Simulation Efforts to Support Improved Comfort in ARGOS

BACKGROUND: The Active Response Gravity Offload System (ARGOS) provides an analog environment for extravehicular activity (EVA) testing and training. Discomfort has been observed during longer suited test sessions. While the subject’s core is offloaded during surface EVA evaluations, his/her arms experience full Earth gravity and can become overly fatigued, especially during suited tests which involve reaching and prolonged arm extensions. A device (ARGOS Negation of Gravitational Effects on the Limbs: ANGEL) to offload the weight of the arms and suit sleeves is being developed by JSC’s Flight Systems Branch, and here we present preliminary modeling of that device using the open-source biomechanical tool OpenSim [1,2] with an in-house developed plugin. We analyze a series of motions performed by a single shirt-sleeved subject with goals of characterizing the device, validating the model, and predicting whether reduced gravity conditions (i.e., lunar gravity (Lg) or Martian gravity (Mg)) can be accurately simulated with the device, as well as providing comfort to the ARGOS user. METHODS AND RESULTS: To model the offload device, we augment the OpenSim human model topology with the offload mechanism components and joints, using CAD models to represent the mechanism graphically. The joint angles of the device are either obtained from (1) inverse kinematics (IK) using motion capture markers on the various components of the device or (2) calculated in the OpenSim plugin by modeling how the components configure themselves under the offloading spring tension given a particular IK-derived arm position. Given the joint angles of the device, the resulting force on the arm is computed by the plugin and applied as an external load in inverse dynamics (ID) in order to enable study of overall shoulder joint torques as well as offload achieved. We verify the calculated joint angles by using the inverse kinematic data and the forces from manual measurements of the spring both independently and integrated within the device. We found that calculated joint angles generally represent the angles measured and computed with IK, supporting a possible analysis workflow inputting human motion data and observing system behavior under varied design parameters. In two different device configurations in which the maximum applied force was 131 N, our current model accurately captured force with a difference of 2-3 N from measured loads. Though our initial test was performed with a shirt-sleeve subject, arm weights were added to emulate the weight of the suit sleeve and the subject was positioned in a test stand with a Mark-III Hard Upper Torso (HUT) and Portable Life Support System (PLSS) mockup. Arm range of motion tasks were performed outside of the HUT, inside the HUT, and inside the HUT while using the device. A variety of other upper body tasks were completed as well. In summary, we have developed a model to investigate and verify an upper limb offload device currently in development. We believe this model will be a valuable tool not only for device characterization but also to predict proper configurations to simulate Lg or Mg conditions, investigate range of motion concerns, predict limitations such as internal collisions and contacts, and inform future design improvements.

L B Nilsson↗

NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing and Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

Biology↗

NASA NTRSDate not supplied

Component-Based Development of CFD Software FUN3D

FUN3D, a suite of Computational Fluid Dynamics simulation and design tools developed at the NASA Langley Research Center, has undergone continuous development since the late1980s. It contains a large portion of legacy code. Extending it with new capabilities becomes increasingly difficult. To improve the extensibility and reusability, FUN3D is moving toward component-based development. New features, such as Stabilized Finite Elements, Yoga, and Sparse Linear Algebra Toolkit, are integrated into the system as components. Some existing features such as the Node-Centered Finite Volume Solver, are also being refactored to components. The integration of these components poses new requirements on the development workflow. In this paper, we describe the Continuous Integration of FUN3D to support component-based development, and discuss the tools used, the practices followed, and lessons learned during the transition from the traditional approach.

computational fluid dynamics software↗

NASA NTRSDate not supplied

Anisotropic Goal-Based Mesh Adaptation Metric Clarification and Development

Adaptive unstructured mesh techniques have a limited, but growing impact on production analysis workflows to control discretization error for reliable simulation results. Multiple independent implementations of flow solvers, anisotropic metric construction methods, and anisotropic mesh adaptation mechanics have matured. Goal-based metrics target estimated error in output functions, such as lift and drag, through the guidance of an adjoint solution. A unification of goal-based anisotropic metrics is presented for steady viscous flows, which is an active area of research. These goal-based metrics drive robust and efficient anisotropic mesh adaptation for the calculation of output functions. The super-convergent functional output error behavior of stabilized finite-element methods is exploited without a formal proof, and evidence of super-convergence is shown in numerical experiments. Mesh adapted drag and lift outputs for two simple bodies in compressible viscous flow show convergence of error to less than a single drag count. Asymptotic behavior established for relatively coarse meshes shows the efficiency of this goal-based metric when compared to solution interpolation error control and expert-guided meshing. Anisotropic mesh adaptation techniques are applied to a transport aircraft in a high-lift configuration where variation between approaches decreases with mesh refinement, but asymptotic behavior is not observed with available resources.

goal-based↗

NASA NTRSDate not supplied

Enabling Metric-Based Mesh Adaptation for Advanced Compressible Flow Simulations Using US3D

This work presents a metric-based mesh adaptation capability added to the flow solver US3D that is used to improve the accuracy of atmospheric entry simulations. Flow past an entry vehicle in free-flight is usually unsteady and characterized by anisotropic flow features like a strong bow shock and small-scale isotropic flow features like turbulent eddies in the wake. The results presented in this paper demonstrate that we are currently able to iteratively apply metric-based mesh adaptation to improve the solution accuracy by simultaneously targeting isotropic and anisotropic flow features. A simplified workflow of running CFD simulations for entry vehicles is presented where we start off with a coarse initial mesh to compute a first approximation of the flow. This coarse mesh is then iteratively refined using the newly developed metric to improve the resolution of the CFD computation. Furthermore, we demonstrate that we are able to adapt large mesh sizes in a timely fashion which allows us to start thinking about applying this adaptive framework to unsteady atmospheric entry CFD simulations.

Mesh adaptation↗