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NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

Accurate modeling of boundary-layer transition is an important aspect of developing greener air transport technologies. In that regard, transition models based on auxiliary transport equations offer a robust approach that is easily integrated into the Reynolds-averaged Navier-Stokes (RANS) solvers. Recent workshops under NATO and AIAA have identified the verification of transport-equations-based transition modeling as a critical aspect of reducing the scatter between the predictions of different CFD codes. Follow-on work has highlighted the need for highly dense grids to achieve an asymptotic convergence of transition related flow metrics. The present work examines the role of automatic near-body mesh adaptation capability in the NASA OVERFLOW CFD solver to enable verification studies in an efficient manner, and for establishing best practices for designing grids for the RANS-based transition models. A sensor function relevant to the Langtry-Menter \gamma-Re_{\theta t}\ transition model has been identified and used for error-based mesh adaptation for canonical configurations comprising a flat plate, and the S809 and NLR-7301 airfoils. The efficacy of the mesh adaptation approach is assessed for flow conditions involving multiple transition scenarios such as natural transition, separation-induced transition, and shock-induced transition. The results from this exploratory study indicate that the meshes adapted using the proposed sensor provide solutions that approach the references solutions obtained with uniformly refined hand-crafted meshes, in terms of the chosen metrics, and yield modest yet significant savings in grid count. We also highlight areas for improvement in the grid adaptation methodology within OVERFLOW.

CFD↗
NASA NTRS2005

Journal of Gravitational Physiology, Volume 12, Number 1

The following topics were covered: System Specificity in Responsiveness to Intermittent -Gx Gravitation during Simulated Microgravity in Rats; A Brief Overview of Animal Hypergravity Studies; Neurovestibular Adaptation to Short Radius Centrifugation; Effect of Artificial Gravity with Exercise Load by Using Short-Arm Centrifuge with Bicycle Ergometer as a Countermeasure Against Disused Osteoporosis; Perception of Body Vertical in Microgravity during Parabolic Flight; Virtual Environment a Behavioral and Countermeasure Tool for Assisted Gesture in Weightlessness: Experiments during Parabolic Flight; Artificial Gravity: Physiological Perspectives for Long-Term Space Exploration; Comparison of the Effects of DL-threo-Beta-Benzyloxyaspartate on the Glutamate Release from Synaptosomes before and after Exposure of Rats to Artificial Gravity; Do Perception and Postrotatory Vestibulo-Ocular Reflex Share the Same Gravity Reference?; Vestibular Adaptation to Changing Gravity Levels and the Orientation of Listing's Plane; Compound Mechanism Hypothesis on +Gz - Induced Brain Injury and Dysfunction of Learning and Memory; Environmental Challenge Impairs Prefrontal Brain Functions; Effect of 6-Days of Support Withdrawal on Characteristics of Balance Function; Hypergravity-Induced Changes of Neuronal Activities in CA1 Region of Rat Hippocampus; Audiological Findings in Antiorthostatic Position Modelling Microgravitation; Investigating Human Cognitive Performance during Spaceflight; The Relevance of the Minimization of Torque Exchange with the Environment in Weightlessness is Confirmed by Asimulation Study; Characteristics of the Eyes Pursuit Function during Readaptation to Terrestrial Gravity after Prolonged Flights Aboard the International Space Station; Comparison of Cognitive Performance Tests for Promethazine Pharmacodynamics in Human Subjects; Structural Reappraisal of Dendritic Tree of Cerebellar Purkinje Cell for Novel Functional Modeling of Elementary Sensorimotor Adaptive Processes; Orpheus 0 G or Ear in Microgravity to Establish Symptoms Concomittant of Inner and Middle Ear and Osteoporosis in Microgravity; Understanding Visual Perception in the Perspective of Gravity; Cortical Regions Associated with Orthostatic Stress in Conscious Humans; Restoration of Central Blood Volume: Application of a Simple Concept and Simple Device to Counteract Cardiovascular Instability in Syncope and Hemorrhage; WISE-2005: Integrative Cardiovascular Responses with LBNP during 60-Day Bed Rest in Women; Intracranial Pressure Increases during Weightlessness. A Parabolic Flights Study; Lower Limb & Portal Veins Echography for Predicting Risk of Thrombosis during a 90-D Bed Rest; Calf Tissue Liquid Stowage and Muscular and Deep Vein Distension in Orthostatic Tests after a 90-Day Head Down Bed Rest; Morphology of Brain Vessels in the Tail Suspended Rats Exposed to Intermittent 2 G; Alterations in Vasoreactivity of Femoral Artery Induced by Hindlimb Unweighting are Related to the Changes of Contractile Protein in Rats; and Respiratory Sinus Arrhythmia: A Marker of Decreased Parasympathetic Modulation after Short Duration.

Fuller, Charles A.↗
NASA NTRS1978

Dual-loop model of the human controller

A dual-loop model of the human controller in single-axis compensatory tracking tasks is introduced. This model possesses an inner-loop closure that involves feeding back that portion of controlled element output rate that is due to control activity. A novel feature of the model is the explicit appearance of the human's internal representation of the manipulator-controlled element dynamics in the inner loop. The sensor inputs to the human controller are assumed to be system error and control force. The former can be sensed via visual, aural, or tactile displays, whereas the latter is assumed to be sensed in kinesthetic fashion. A set of general adaptive characteristics for the model is hypothesized, including a method for selecting simplified internal models of the manipulator-controlled element dynamics. It is demonstrated that the model can produce controller describing functions that closely approximate those measured in four laboratory tracking tasks in which the controlled element dynamics vary considerably in terms of ease of control. An empirically derived expression for the normalized injected error remnant spectrum is introduced.

Hess, R. A.↗
NASA NTRS1997

Vertical Transport Processes for Inert and Scavenged Species: TRACE-A Measurements

The TRACE-A mission of the NASA DC-8 aircraft made a large-scale survey of the tropical and subtropical atmosphere in September and October of 1992. Both In-situ measurements of CO (G. Sachsen NASA Langley) and aerosol size (J. Browell group, NASA Langley) provide excellent data sets with which to constrain vertical transport by planetary boundary layer mixing and deep-cloud cumulus convection. Lidar profiles of aerosol-induced scattering and ozone (also by Bremen) are somewhat require more subtle interpretation as tracers, but the vertical information on layering largely compensates for these complexities. The reason this DC-8 dataset is so useful is that very large areas of biomass burning over Africa and South America provide surface sources of appropriate sizes with which to characterize vertical and horizontal motions; the major limitation of our source description is that biomass burning patterns move considerably every few days, and daily burning inventories are a matter of concurrent, intensive research. We use the Penn State / NCAR MM5 model in an assimilation mode on the synoptic and intercontinental scale, and assess the success it shows in vertical transport descriptions. We find that the general level of emissions suggested by the climatological approach (Will. Has, U. of Montana) appears to be approximately correct, possibly a bit low, for this October, 1992, time period. Vertical transport in planetary boundary layer mixing to 5.5 kin was observed and reproduced in our simulations. Furthermore we find evidence that Blackader "transilient" or matrix-transport scheme is needed, but may require some adaptation in our tracer model: CO seems to exhibit very high values at the top of the planetary boundary layer, a process that stretches the eddy-diffusion parameterization. We will report on progress in improving the deep convective transport of carbon monoxide: the Grail scheme as we used it at 100 kin resolution did not transport enough material to the upper troposphere. We expect to be able to attribute this to either parameterization reasons (inadequacy of this parameterization at the large 100km scale) or other reasons. Nevertheless, the qualitative nature of deep transport by clouds shows up well in the simulations. As for scavengable species, the simulations predict tens of micrograms per standard cubic meter of smoke aerosol in the boundary layer. In a straightforward illustration of our simple bulk-mass scavenging parameterization, to one or two micrograms per standard cubic meter of smoke aerosol in the free troposphere just above the source regions: very high concentrations for the free troposphere. We expect to report on comparisons of these predictions to a variety of observations.

Chatfield, Robert B.↗
NASA NTRS1994

Anticipatory Neurofuzzy Control

Technique of feedback control, called "anticipatory neurofuzzy control," developed for use in controlling flexible structures and other dynamic systems for which mathematical models of dynamics poorly known or unknown. Superior ability to act during operation to compensate for, and adapt to, errors in mathematical model of dynamics, changes in dynamics, and noise. Also offers advantage of reduced computing time. Hybrid of two older fuzzy-logic control techniques: standard fuzzy control and predictive fuzzy control.

Mccullough, Claire L.↗
NASA NTRS2003

Fast-Running Aeroelastic Code Based on Unsteady Linearized Aerodynamic Solver Developed

The NASA Glenn Research Center has been developing aeroelastic analyses for turbomachines for use by NASA and industry. An aeroelastic analysis consists of a structural dynamic model, an unsteady aerodynamic model, and a procedure to couple the two models. The structural models are well developed. Hence, most of the development for the aeroelastic analysis of turbomachines has involved adapting and using unsteady aerodynamic models. Two methods are used in developing unsteady aerodynamic analysis procedures for the flutter and forced response of turbomachines: (1) the time domain method and (2) the frequency domain method. Codes based on time domain methods require considerable computational time and, hence, cannot be used during the design process. Frequency domain methods eliminate the time dependence by assuming harmonic motion and, hence, require less computational time. Early frequency domain analyses methods neglected the important physics of steady loading on the analyses for simplicity. A fast-running unsteady aerodynamic code, LINFLUX, which includes steady loading and is based on the frequency domain method, has been modified for flutter and response calculations. LINFLUX, solves unsteady linearized Euler equations for calculating the unsteady aerodynamic forces on the blades, starting from a steady nonlinear aerodynamic solution. First, we obtained a steady aerodynamic solution for a given flow condition using the nonlinear unsteady aerodynamic code TURBO. A blade vibration analysis was done to determine the frequencies and mode shapes of the vibrating blades, and an interface code was used to convert the steady aerodynamic solution to a form required by LINFLUX. A preprocessor was used to interpolate the mode shapes from the structural dynamic mesh onto the computational dynamics mesh. Then, we used LINFLUX to calculate the unsteady aerodynamic forces for a given mode, frequency, and phase angle. A postprocessor read these unsteady pressures and calculated the generalized aerodynamic forces, eigenvalues, and response amplitudes. The eigenvalues determine the flutter frequency and damping. As a test case, the flutter of a helical fan was calculated with LINFLUX and compared with calculations from TURBO-AE, a nonlinear time domain code, and from ASTROP2, a code based on linear unsteady aerodynamics.

Reddy, T. S. R.↗
NASA NTRS2003

Active Control of High Frequency Combustion Instability in Aircraft Gas-Turbine Engines

Active control of high-frequency (greater than 500 Hz) combustion instability has been demonstrated in the NASA single-nozzle combustor rig at United Technologies Research Center. The combustor rig emulates an actual engine instability and has many of the complexities of a real engine combustor (i.e. actual fuel nozzle and swirler, dilution cooling, etc.) In order to demonstrate control, a high-frequency fuel valve capable of modulating the fuel flow at up to 1kHz was developed. Characterization of the fuel delivery system was accomplished in a custom dynamic flow rig developed for that purpose. Two instability control methods, one model-based and one based on adaptive phase-shifting, were developed and evaluated against reduced order models and a Sectored-1-dimensional model of the combustor rig. Open-loop fuel modulation testing in the rig demonstrated sufficient fuel modulation authority to proceed with closed-loop testing. During closed-loop testing, both control methods were able to identify the instability from the background noise and were shown to reduce the pressure oscillations at the instability frequency by 30%. This is the first known successful demonstration of high-frequency combustion instability suppression in a realistic aero-engine environment. Future plans are to carry these technologies forward to demonstration on an advanced low-emission combustor.

Corrigan, Bob↗
NASA NTRS2018

A Conservative, Scalable, Space-Time Blade Element Rotor Model for Multi-Rotor Vehicles

The development of a parallel blade-element rotor model and its implementation into an adaptive Cartesian method is described. The unsteady version of the rotor model applies a body force to all cells contained in the swept space-time volume at each timestep and special care is taken to maintain axisymmetry on the Cartesian grid. Mesh convergence of rotor thrust and torque is obtained with around 10000 cells in the disk for the steady model. Parallelization is accomplished using OpenMP and the rotor force computation is distributed across all available nodes. Simulations of an isolated XV-15 rotor in hover show good correlation with experimental data and predictions of multi-rotor thrust variation closely match previous high fidelity simulations. The final paper will also include results from the unsteady rotor model and parallel scaling tests.

Conservative↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
NASA NTRS1987

Adaptive control and noise suppression by a variable-gain gradient algorithm

An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.

Merhav, S. J.↗
NASA NTRS2010

Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

Land surface (or "skin") temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. Here we assimilate LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) into the Noah and Catchment (CLSM) land surface models using an ensemble-based, off-line land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LST typically exhibit different mean values and variability. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation ("open loop") are comparable to each other and superior to that of ISCCP retrievals. For LST, RMSE values are 4.9 K (CLSM), 5.6 K (Noah), and 7.6 K (ISCCP), and anomaly correlation coefficients (R) are 0.62 (CLSM), 0.61 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over open loop) of up to 0.7 K in RMSE and 0.05 in anomaly R. The skill of surface turbulent flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Reichle, Rolf H.↗
NASA NTRS1990

Rule-based mechanisms of learning for intelligent adaptive flight control

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Handelman, David A.↗
NASA NTRSDate not supplied

Near-Body Mesh Adaption for Transitional Flows Using OVERFLOW

Accurate modeling of boundary-layer transition is an important aspect of developing greener air transport technologies. In that regard, transition models based on auxiliary transport equations offer a robust approach that is easily integrated into the Reynolds-averaged Navier-Stokes (RANS) solvers. Recent workshops under NATO and AIAA have identified the verification of transport-equations-based transition modeling as a critical aspect of reducing the scatter between the predictions of different CFD codes. Follow-on work has highlighted the need for highly dense grids to achieve an asymptotic convergence of transition related flow metrics. The present work examines the role of automatic near-body mesh adaptation capability in the NASA OVERFLOW CFD solver to enable verification studies in an efficient manner, and for establishing best practices for designing grids for the RANS-based transition models. A sensor function relevant to the Langtry-Menter \gamma-Re_{\theta t}\ transition model has been identified and used for error-based mesh adaptation for canonical configurations comprising a flat plate, and the S809 and NLR-7301 airfoils. The efficacy of the mesh adaptation approach is assessed for flow conditions involving multiple transition scenarios such as natural transition, separation-induced transition, and shock-induced transition. The results from this exploratory study indicate that the meshes adapted using the proposed sensor provide solutions that approach the references solutions obtained with uniformly refined hand-crafted meshes, in terms of the chosen metrics, and yield modest yet significant savings in grid count. We also highlight areas for improvement in the grid adaptation methodology within OVERFLOW.

CFD↗
NASA NTRS2020

Integrated assessment of climate change impacts on crop productivity and income of commercial maize farms in northeast South Africa

Agriculture in South Africa sustains about 70% of the region’s population for food, income and employment, playing an important role for food security and the local economy. The focus of the study was the commercial maize farms of the Free State Province given their importance in the National economy. The Regional Integrated Assessment (phase I) was implemented to assess climate change and adaptation that links climate, crops, economic data and tools developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP). In this context, the“system”is defined as a whole of agronomic and socio-economic factors. Within that framework three core questions were being evaluated: (i) Impacts of climate change under current system; (ii) Impacts of climate change under future system; (iii) The role of adaptation under climate change and the future system. Maize production will decrease between 10% to 16% as a result of projected climate impacts. Also, current agricultural production systems are negatively affected by climate change with an increase in poverty rates between 2% to 3%. The projected adoption of the adapted technology would result in positive increased net returns and a decrease in poverty rate of between 12%and 22%. The results of this study show that implementing adaptation measures and other strategies as indicated by the local stakeholders will have positive impacts on the agricultural production systems and can contribute to support and inform climate change policy decision making such as the development of National Adaptation Plans.

Integrated assessment↗
NASA NTRS1992

An ice-ocean coupled model for the Northern Hemisphere

The Hibler ice model has been modified and adapted to a domain that includes most of the sea ice-covered areas in the Northern Hemisphere. This model, joined with the Cox ocean model, is developed as an enhancement to the U.S. Navy's sea ice forecasting, PIPS, and is termed PIPS2.0. Generally, the modeled ice edge is consistent with the Navy-NOAA Joint Ice Center weekly analysis, and the modeled ice thickness distribution agrees with submarine sonar data in the central Arctic basin.

Cheng, Abe↗
NASA NTRS2002

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Kegerise, M. A.↗