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NASA NTRS2003

Neural Flight Control System

The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

Gundy-Burlet, Karen↗

NASA NTRS2016

Thermodynamic Spectrum of Solar Flares Based on SDO/EVE Observations: Techniques and First Results

The Solar Dynamics Observatory (SDO)/EUV Variability Experiment (EVE) provides rich information on the thermodynamic processes of solar activities, particularly on solar flares. Here, we develop a method to construct thermodynamic spectrum (TDS) charts based on the EVE spectral lines. This tool could potentially be useful for extreme ultraviolet (EUV) astronomy to learn about the eruptive activities on distant astronomical objects. Through several cases, we illustrate what we can learn from the TDS charts. Furthermore, we apply the TDS method to 74 flares equal to or greater than the M5.0 class, and reach the following statistical results. First, EUV peaks are always behind the soft X-ray (SXR) peaks and stronger flares tend to have faster cooling rates. There is a power-law correlation between the peak delay times and the cooling rates, suggesting a coherent cooling process of flares from SXR to EUV emissions. Second, there are two distinct temperature drift patterns, called Type I and Type II. For Type I flares, the enhanced emission drifts from high to low temperature like a quadrilateral, whereas for Type II flares the drift pattern looks like a triangle. Statistical analysis suggests that Type II flares are more impulsive than Type I flares. Third, for late-phase flares, the peak intensity ratio of the late phase to the main phase is roughly correlated with the flare class, and the flares with a strong late phase are all confined. We believe that the re-deposition of the energy carried by a flux rope, which unsuccessfully erupts out, into thermal emissions is responsible for the strong late phase found in a confined flare. Furthermore, we show the signatures of the flare thermodynamic process in the chromosphere and transition region in the TDS charts. These results provide new clues to advance our understanding of the thermodynamic processes of solar flares and associated solar eruptions, e.g., coronal mass ejections.

Wang, Yuming↗

NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗

NASA NTRS2016

Flat H Frangible Joint Evolution

Space vehicle staging and separation events require pyrotechnic devices. They are single-use mechanisms that cannot be tested, nor can failure-tolerant performance be demonstrated in actual flight articles prior to flight use. This necessitates the implementation of a robust design and test approach coupled with a fully redundant, failure-tolerant explosive mechanism to ensure that the system functions even in the event of a single failure. Historically, NASA has followed the single failure-tolerant (SFT) design philosophy for all human-rated spacecraft, including the Space Shuttle Program. Following the end of this program, aerospace companies proposed building the next generation human-rated vehicles with off-the-shelf, non-redundant, zero-failure-tolerant (ZFT) separation systems. Currently, spacecraft and launch vehicle providers for both the Orion and Commercial Crew Programs (CCPs) plan to deviate from the heritage safety approach and NASA's SFT human rating requirements. Both programs' partners have base-lined ZFT frangible joints for vehicle staging and fairing separation. These joints are commercially available from pyrotechnic vendors. Non-human-rated missions have flown them numerous times. The joints are relatively easy to integrate structurally within the spacecraft. In addition, the separation event is debris free, and the resultant pyro shock is lower than that of other design solutions. It is, however, a serious deficiency to lack failure tolerance. When used for critical applications on human-rated vehicles, a single failure could potentially lead to loss of crew (LOC) or loss of mission (LOM)). The Engineering and Safety & Mission Assurance directorates within the NASA Johnson Space Center took action to address this safety issue by initiating a project to develop a fully redundant, SFT frangible joint design, known as the Flat H. Critical to the ability to retrofit on launch vehicles being developed, the SFT mechanisms must fit within the same three-dimensional envelope as current designs as well as meet structural loads requirements. There is increased mass associated with the redundant design, and the goal is to minimize the weight impact as much as possible. These requirements presented significant challenges, both technically and financially; these challenges will be explored in this paper. Perhaps greater than the technical issues confronted during this design process, were the financial considerations. These were a significant part of the story of this design and development plan. Insufficient financial and labor resources were formidable barriers to completing this project. Nevertheless, JSC personnel successfully conducted several test series at JSC with very useful results. The many lessons learned drove design improvements, performance efficiency, and increased functional reliability. This paper examines the significant technical and financial challenges that these requirements posed to the project team. It discusses the evolution of the SFT frangible joint design, including optimization, testing, and successful partnering of the Johnson Space Center (JSC) engineering and JSC safety organizations, to enhance the flight safety margin for America's next generation of human-rated space vehicles.

Diegelman, Thomas E.↗

NASA NTRS2003

A Gold Standards Approach to Training Instructors to Evaluate Crew Performance

The Advanced Qualification Program requires that airlines evaluate crew performance in Line Oriented Simulation. For this evaluation to be meaningful, instructors must observe relevant crew behaviors and evaluate those behaviors consistently and accurately against standards established by the airline. The airline industry has largely settled on an approach in which instructors evaluate crew performance on a series of event sets, using standardized grade sheets on which behaviors specific to event set are listed. Typically, new instructors are given a class in which they learn to use the grade sheets and practice evaluating crew performance observed on videotapes. These classes emphasize reliability, providing detailed instruction and practice in scoring so that all instructors within a given class will give similar scores to similar performance. This approach has value but also has important limitations; (1) ratings within one class of new instructors may differ from those of other classes; (2) ratings may not be driven primarily by the specific behaviors on which the company wanted the crews to be scored; and (3) ratings may not be calibrated to company standards for level of performance skill required. In this paper we provide a method to extend the existing method of training instructors to address these three limitations. We call this method the "gold standards" approach because it uses ratings from the company's most experienced instructors as the basis for training rater accuracy. This approach ties the training to the specific behaviors on which the experienced instructors based their ratings.

Baker, David P.↗

NASA NTRS2009

Testing and Performance Verification of a High Bypass Ratio Turbofan Rotor in an Internal Flow Component Test Facility

A 1/5 scale model rotor representative of a current technology, high bypass ratio, turbofan engine was installed and tested in the W8 single-stage, high-speed, compressor test facility at NASA Glenn Research Center (GRC). The same fan rotor was tested previously in the GRC 9x15 Low Speed Wind Tunnel as a fan module consisting of the rotor and outlet guide vanes mounted in a flight-like nacelle. The W8 test verified that the aerodynamic performance and detailed flow field of the rotor as installed in W8 were representative of the wind tunnel fan module installation. Modifications to W8 were necessary to ensure that this internal flow facility would have a flow field at the test package that is representative of flow conditions in the wind tunnel installation. Inlet flow conditioning was designed and installed in W8 to lower the fan face turbulence intensity to less than 1.0 percent in order to better match the wind tunnel operating environment. Also, inlet bleed was added to thin the casing boundary layer to be more representative of a flight nacelle boundary layer. On the 100 percent speed operating line the fan pressure rise and mass flow rate agreed with the wind tunnel data to within 1 percent. Detailed hot film surveys of the inlet flow, inlet boundary layer and fan exit flow were compared to results from the wind tunnel. The effect of inlet casing boundary layer thickness on fan performance was quantified. Challenges and lessons learned from testing this high flow, low static pressure rise fan in an internal flow facility are discussed.

VanZante, Dale E.↗

NASA NTRS2010

Ares I-X: First Flight of a New Generation

The Ares I-X suborbital development flight test demonstrated NASA s ability to design, develop, launch and control a new human-rated launch vehicle (Figure 14). This hands-on missions experience will provide the agency with necessary skills and insights regardless of the future direction of space exploration. The Ares I-X team, having executed a successful launch, will now focus on analyzing the flight data and extracting lessons learned that will be used to support the development of future vehicles.

Davis, Stephan R.↗

NASA NTRSDate not supplied

A Preliminary Assessment of Physical Demand during Simulated Lunar Surface Extravehicular Activities

Returning to the moon requires many advances in current space technology. One major aspect of this development is a new exploration spacesuit (xEMU). Taking lessons learned from Apollo era suitsand the Extravehicular Mobility Unit (EMU) used on the International Space Station (ISS), xEMU will have increased mobility, dust mitigation, headspace, glove fit, and life support capabilities. Artemis astronauts in xEMU will complete a far more rigorous Extravehicular Activity (EVA) schedule than Apolloand ISS. Notably, metabolic rates during Apollo lunar EVA tasks were observed to be up to 50% lower than similar tasks performed in a ground analog environment under simulated partial gravity with newer suits. Therefore, understanding the physical demands of lunar surface exploration operations is criticalto ensuring best outcomes operating within the constraints of xEMU and planning for exploration EVA activities. This study utilized the Active Response Gravity Offload System (ARGOS) to simulate the lunar environment and continuously offload subjects to lunar gravity. Two male subjects completed two days of EVAs wearing the pressurized Mark III spacesuit, completing suit fit and mobility checks, as well as simulated lander operations, cable routing, crew rescue, geology, payload relocation, and traverse tasks in an end-to-end EVA (E2E) task block and standalone (SA) task blocks. We recorded continuous values of metabolic rate (MR) and heart rate (HR) to assess physical demand. During the E2E task block, subjects did not rest between tasks to simulate continuous effort from task to task, as in real EVAs. In comparison, subjects had a 5-minute break after each SA task block to allow for the metabolic rate and heart rate to return to baseline.MR values were categorized as low (≤ 700 BTU/hr), medium (700-1000 BTU/HR), and high (≥ 1000 BTU/hr), while HR values were categorized as low (≤150) and high (>150). During the 16 tasks in the E2E block, subjects averaged low MR in 6% of tasks, medium MR in 47% of tasks, and high MR in 47% of tasks. While MR was consistent between subjects, Subject 1 averaged low HR for 100% of these tasks, while Subject 2 averaged low HR in 44% of tasks. During the 23 tasks in the SA task blocks, subjects averaged low MR in 26% of tasks, medium MR in 52% of tasks, and high MR in 22% of tasks. Again, HR was different between subjects, with subject 1 averaging low HR in 100% of these tasks while subject 2 averaged low HR in 70%. Across all tasks in this study, subjects reached maximum MR and HR values during a 500m traverse at 30% grade in the E2E block (subject 1: 1747 BTU/hr, 150 BPM; subject 2: 1656 BTU/hr, 177 BPM).Understanding the physical demand to complete exploration EVA tasks will be instrumental to the future success of exploration spacesuit designs and missions. Further work in this study will be needed to characterize MR during exploration EVA tasks, including expanding the subject pool and testing new suit designs.

Taylor E Schlotman↗

NASA NTRS2023

Invasion in the Niger Delta: Remote Sensing of Mangrove Conversion to Invasive Nypa fruticans from 2015-2020

Invasive species are a leading threat to biodiversity worldwide. Nypa palm ( Nypa fruticans ) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines, and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbates mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyze multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015-2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel-1 SAR, Sentinel-2 MSI, and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel-1 were best suited for mapping Nypa extent with similar accuracies (78% and 75% respectively). Based on data availability and accuracy, we focused our change analysis on Sentinel-1 SAR. Our results show ~28,000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from GEDI (Global Ecosystem Dynamics Investigation) LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top-of-canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves.

GEE↗

NASA NTRS1995

Cascaded VLSI neural network architecture for on-line learning

High-speed, analog, fully-parallel and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware-compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A comparison-intensive feature classification application has been demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as application-specific-coprocessors for solving real-world problems at extremely high data rates.

Duong, Tuan A.↗

NASA NTRS1996

Lava Flow Dynamics

This grant originally had four major tasks, all of which were addressed to varying extents during the course of the research: (1) Measure the fractal dimensions of lava flows as a function of topography, substrate, and rheology; (2) The nature of lava tube systems and their relation to flow fields; (3) A quantitative assessment of lava flow dynamics in light of the fractal nature of lava flow margins; and (4) Development and application of a new remote sensing tool based on fractal properties. During the course of the research, the project expanded to include the following projects: (1) A comparison of what we can-learn from remote sensing studies of lava flow morphology and from studies of samples of lava flows; (2) Study of a terrestrial analog of the nakhlites, one of the groups of meteorites from Mars; and (3) Study of the textures of Hawaiian basalts as an aid in understanding the dynamics (flow rates, inflation rates, thermal history) of flow interiors. In addition, during the first year an educational task (development and writing of a teacher's guide and activity set to accompany the lunar sample disk when it is sent to schools) was included.

Taylor, G. Jeffrey↗

NASA NTRS2020

Development and Evaluation of the Active Response Gravity Offload System as a Lunar and Martian EVA Simulation Environment

In preparation for future exploration missions, NASA seeks the ability to simulate partial-gravity operations for use in ground-based research, crew training, and engineering design evaluations. The Active Response Gravity Offload System (ARGOS) at the Johnson Space Center (JSC) is designed to simulate reduced gravity environments, such as lunar, Martian, or microgravity, using a robotic system similar to an overhead bridge crane. ARGOS continuously offloads a portion of a suited human’s weight during all dynamic motions within the test facility, which can include basic functional movements such as walking, running, and jumping, as well as a wide range of planetary surface activities. This system will be used as part of a metabolic-rate task characterization study to determine the workload associated with partial-gravity extravehicular activity (EVA). Pilot testing was conducted using the MKIII prototype planetary space suit and two gimbal designs to determine the ability of the ARGOS test environment to simulate planetary EVA operations. This paper will describe the lessons learned from the feasibility testing, simulation-environment mockup design, and the results from the pilot tests and their influence into the final study design. Being able to effectively simulate partial-gravity environments and characterize the performance of crewmembers will have an impact on multiple domains including suit design, task design, thermal models, and life-support-system capacity verification plans, among others.

Omar S Bekdash↗

NASA NTRSDate not supplied

Advanced Diagnostics for Megahertz Imaging of Mixing, Fuel Spray, and Combustion Processes for Rotating Detonation Combustors

Recent advancements in megahertz rate, high-power, burst-mode laser technology are leveraged to perform and explore imaging measurements that spatially and temporally resolve the mixing, combustion, and detonation flow field in two laboratory-scale rotating detonation combustors (RDCs). In a non-premixed annular RDC, multiple imaging diagnostics are explored to investigate gaseous and liquid injector behavior, the detonation wave structure, and the propellant refill. In one instance, OH planar laser-induced fluorescence (OH-PLIF) imaging is performed up to a 2 MHz repetition rate to track the combustion products and reaction zone locations. In the same annular RDC, a single liquid fuel jet is injected, and laser-based 355-nm imaging of the fuel spray is performed up to a 1 MHz repetition rate. For this configuration, the annular RDC is used as a detonation driver to impose periodic detonation waves to interact with the fuel spray. Moreover, in this annular RDC, a range of tracer-based laser imaging measurements are explored to time-resolve the unsteady oxidizer air recovery and refill process. In a non-premixed linear RDC, planar imaging measurements of the fuel mixing are performed up to a 200 kHz repetition rate using PLIF of a tracer in the fuel supply. The fuel mixing imaging helps explain the origin of the observed pre and post wave burning, detonation structure, and enables quantifying injector recovery timescales. This paper will provide a high-level broad survey of diagnostics applied in these RDCs, lessons learned, and interesting observations.

Combustion↗

NASA NTRSDate not supplied

Advanced Diagnostics For Megahertz Imaging Of Mixing, Fuel Spray, And Combustion Processes For Rotating Detonation Combustors

Combustion↗

NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗

NASA NTRS2015

Software Defined Radio Architecture Contributions to Next Generation Space Communications

Space communications architecture concepts, comprising the elements of the system, the interactions among them, and the principles that govern their development, are essential factors in developing National Aeronautics and Space Administration (NASA) future exploration and science missions. Accordingly, vital architectural attributes encompass flexibility, the extensibility to insert future capabilities, and to enable evolution to provide interoperability with other current and future systems. Space communications architectures and technologies for this century must satisfy a growing set of requirements, including those for Earth sensing, collaborative observation missions, robotic scientific missions, human missions for exploration of the Moon and Mars where surface activities require supporting communications, and in-space observatories for observing the earth, as well as other star systems and the universe. An advanced, integrated, communications infrastructure will enable the reliable, multipoint, high-data-rate capabilities needed on demand to provide continuous, maximum coverage for areas of concentrated activity. Importantly, the cost/value proposition of the future architecture must be an integral part of its design; an affordable and sustainable architecture is indispensable within anticipated future budget environments. Effective architecture design informs decision makers with insight into the capabilities needed to efficiently satisfy the demanding space-communication requirements of future missions and formulate appropriate requirements. A driving requirement for the architecture is the extensibility to address new requirements and provide low-cost on-ramps for new capabilities insertion, ensuring graceful growth as new functionality and new technologies are infused into the network infrastructure. In addition to extensibility, another key architectural attribute of the space communication equipment's interoperability with other NASA communications systems, as well as those communications and navigation systems operated by international space agencies and civilian and government agencies. In this paper, we review the philosophies, technologies, architectural attributes, mission services, and communications capabilities that form the structure of candidate next-generation integrated communication architectures for space communications and navigation. A key area that this paper explores is from the development and operation of the software defined radio for the NASA Space Communications and Navigation (SCaN) Testbed currently on the International Space Station (ISS). Evaluating the lessons learned from development and operation feed back into the communications architecture. Leveraging the reconfigurability provides a change in the way that operations are done and must be considered. Quantifying the impact on the NASA Space Telecommunications Radio System (STRS) software defined radio architecture provides feedback to keep the standard useful and up to date. NASA is not the only customer of these radios. Software defined radios are developed for other applications, and taking advantage of these developments promotes an architecture that is cost effective and sustainable. Developments in the following areas such as an updated operating environment, higher data rates, networking and security can be leveraged. The ability to sustain an architecture that uses radios for multiple markets can lower costs and keep new technology infused.

Lessons Learned↗

NASA NTRS1990

General purpose optimization software for engineering design

The author has developed several general purpose optimization programs over the past twenty years. The earlier programs were developed as research codes and served that purpose reasonably well. However, in taking the formal step from research to industrial application programs, several important lessons have been learned. Among these are the importance of clear documentation, immediate user support, and consistent maintenance. Most important has been the issue of providing software that gives a good, or at least acceptable, design at minimum computational cost. Here, the basic issues developing optimization software for industrial applications are outlined and issues of convergence rate, reliability, and relative minima are discussed. Considerable feedback has been received from users, and new software is being developed to respond to identified needs. The basic capabilities of this software are outlined. A major motivation for the development of commercial grade software is ease of use and flexibility, and these issues are discussed with reference to general multidisciplinary applications. It is concluded that design productivity can be significantly enhanced by the more widespread use of optimization as an everyday design tool.

Vanderplaats, G. N.↗

NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗