Reinventing the Design Process: Teams and Models
The future of space mission designing will be dramatically different from the past. Formerly, performance-driven paradigms emphasized data return with cost and schedule being secondary issues.
Design↗
SEARCH · Engineering Papers
Results for “data driven modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Pyrotechnic modeling for the NSI and pin puller
A discussion concerning the modeling of pyrotechnically driven actuators is presented in viewgraph format. The following topics are discussed: literature search, constitutive data for full-scale model, simple deterministic model, observed phenomena, and results from simple model.
Powers, Joseph M.↗
Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures
Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.
Thermodynamics↗
Advances in Aero-Propulsive Modeling for Fixed-Wing and eVTOL Aircraft Using Experimental Data
Small unmanned aircraft and electric vertical takeoff and landing (eVTOL) aircraft have recently emerged as vehicles able to perform new missions and stimulate future air transportation methods. This dissertation presents several system identification research advancements for these modern aircraft configurations enabling accurate mathematical model development for flight dynamics simulations based on wind-tunnel and flight-test data. The first part of the dissertation focuses on advances in flight-test system identification methods using small, fixed-wing, remotely-piloted, electric, propeller-driven aircraft. A generalized approach for flight dynamics model development for small fixed-wing aircraft from flight data is described and is followed by presentation of novel flight-test system identification applications, including: aero-propulsive model development for propeller aircraft and nonlinear dynamic model identification without mass properties. The second part of the dissertation builds on established fixed-wing and rotary-wing aircraft system identification methods to develop modeling strategies for transitioning, distributed propulsion, eVTOL aircraft. Novel wind-tunnel experiment designs and aero-propulsive modeling approaches are developed using a subscale, tandem tilt-wing, eVTOL aircraft, leveraging design of experiments and response surface methodology techniques. Additionally, a method applying orthogonal phase-optimized multisine input excitations to aircraft control effectors in wind-tunnel testing is developed to improve test efficiency and identified model utility. Finally, the culmination of this dissertation is synthesis of the techniques described throughout the document to form a flight-test system identification approach for eVTOL aircraft that is demonstrated using a high-fidelity flight dynamics simulation. The research findings highlighted throughout the dissertation constitute substantial progress in efficient empirical aircraft modeling strategies that are applicable to many current and future aeronautical vehicles enabling accurate flight simulation development, which can subsequently be used to foster advancement in many other pertinent technology areas.
system identification↗
Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.
Istem Fer↗
Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline
The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.
Paul Horton↗
Petermann Glacier on the Brink: Progress, Challenges and Insights
Petermann Glacier, the largest marine-terminating glacier in northern Greenland based on catchment area and ice discharge, plays a key role in regulating ice discharge from the Greenland Ice Sheet into the Arctic Ocean. With an upstream catchment connected to the ice sheet interior via a deep subglacial canyon, its future stability has major implications for sea level rise. In this review, we synthesize recent advances in understanding Petermann’s dynamics across three critical interfaces: the ice–ocean, ice–atmosphere, and ice–bed boundaries. At the surface, observations show that reanalysis products underestimate air temperatures and melt, while regional climate models diverge significantly in their estimates of surface mass balance, underscoring the need for improved in situ data and models. At the ocean boundary, enhanced basal melting driven by both subglacial runoff and Atlantic water intrusions is identified as the dominant driver of recent mass loss of Petermann Glacier, with continued warming posing a serious threat to the stability of the floating tongue. At the bed, new geophysical synthesis reveals complex geology, likely spatial variability in geothermal heat flux, and the influence of the megacanyon on seasonal hydrology and velocity fluctuations. Petermann’s mass balance has been negative in recent decades without corresponding flow acceleration. However, the glacier has undergone significant calving events, and the current rifting that began in September 2025 highlights its vulnerability. This upcoming calving event underscores the timeliness of this review, as it will put Petermann Glacier’s terminus at its most retreated position since records began in 1923. The anticipated retreat of the ice tongue also reduces buttressing and brings the terminus closer to the grounding zone, and modeling studies suggest that calving within 12 km of the grounding zone could potentially trigger dynamic retreat, accelerating ice discharge, and a doubling of flow speeds. We conclude that improved observations, sustained monitoring of oceanographic and atmospheric properties, and high-resolution modeling are critical to constraining projections of Petermann Glacier’s future and its role in the stability of the Greenland Ice Sheet.
Dominik Fahrner↗
Theoretical overview and modeling of the sodium and potassium atmospheres of mercury
A general theoretical overview for the sources, sinks, gas-surface interactions, and transport dynamics of sodium and potassium in the exospheric atmsophere of Mercury is given. Information for these four factors, which control the spatial distribution of these two alkali-group gases about the planet, is incorporated in numerical models. The spatial nature and relative importance of the initial source atom atmosphere and the ambient (ballistic hopping) atom atmosphere are then examined and are shown to be controlled and coupled to a great extent by the extremely large and variable solar radiation acceleration experienced by sodium and potassium as they resonantly scatter solar photons. The lateral (antisunward) transport rate of thermally accommodated sodium and potassium ambient atoms is shown to be driven by the solar radiation acceleration and, over a significant portion of Mercury's orbit about the Sun, is sufficiently rapid to be competitive with the short photoionization lifetimes for these atoms when they are located on the summit surface near or within about 30 deg of the terminator. The lateral transport rate is characterized by a migration time determined by model calculations for an ensemble of atoms initially starting at a point source on the surface (i.e., a numerical spacetime dependent Green's function). Four animations for the spacetime evolution of the sodium (or potassium) atmosphere produced by a point source on the surface are presented on a videotape format. For extended surface sources for sodium and potassium, the local column density is determined by competition between the photoionization lifetimes and the lateral transport times of atoms originating from different surface source locations. Sodium surface source fluxes (referenced to Mercury at perihelion) that are required on the sunlit hemisphere to reproduce the typically observed several megarayleighs of D2 emission-line brightness and the inferred column densities of 1-2 x 10(exp 11) atoms per sq cm range from approximately 2-5 x 10(exp 7) atoms/sq cm/sec. The sodium model is applied to study observational data that document an anticorrelation in the average sodium column density and solar radiation acceleration. Lateral transport driven by the solar radiation acceleration is shown to produce this behavior for combinations of different sources and surface accomodation coefficients. The best fit model fits to the observational data require a significant degree of thermal accommodation of the ambient sodium atoms to the surface and a source rate that decreases as an inverse power of 1.5 to 2 in heliocentric distance.
Smyth, William H.↗
Interannual Variability in Soil Trace Gas (CO2, N2O, NO) Fluxes and Analysis of Controllers
Interannual variability in flux rates of biogenic trace gases must be quantified in order to understand the differences between short-term trends and actual long-term change in biosphere-atmosphere interactions. We simulated interannual patterns (1983-1988) of global trace gas fluxes from soils using the NASA Ames model version of CASA (Carnegie-Ames-Stanford Approach) in a transient simulation mode. This ecosystem model has been recalibrated for simulations driven by satellite vegetation index data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. The predicted interannual pattern of soil heterotropic CO2 emissions indicates that relatively large increases in global carbon flux from soils occurred about three years following the strong El Nino Southern Oscillation (ENSO) event of 1983. Results for the years 1986 and 1987 showed an annual increment of +1 Pg (1015 g) C-CO2 emitted from soils, which tended to dampen the estimated global increase in net ecosystem production with about a two year lag period relative to plant carbon fixation. Zonal discrimination of model results implies that 80-90 percent of the yearly positive increments in soil CO2 emission during 1986-87 were attributable to soil organic matter decomposition in the low-latitudes (between 30 N and 30 S). Soils of the northern middle-latitude zone (between 30 N and 60 N) accounted for the residual of these annual increments. Total annual emissions of nitrogen trace gases (N2O and NO) from soils were estimated to vary from 2-4 percent over the time period modeled, a level of variability which is consistent with predicted interannual fluctuations in global soil CO2 fluxes. Interannual variability of precipitation in tropical and subtropical zones (30 N to 20 S appeared to drive the dynamic inverse relationship between higher annual emissions of NO versus emissions of N2O. Global mean emission rates from natural (heterotrophic) soil sources over the period modeled (1983-1988) were estimated at 57.1 Pg C-CO2yr-1, 9.8Tg (1012 g) N-NO yr-1, and 9.7 Tg N-N2O yr-1. Chemical fertilizer contributions to global soil N gas fluxes were estimated at between 1.3 to 7.3 Tg N-NO yr-1, and 1.2 to 4.0 Tg N-N2O yr-1.
Potter, C.↗
Clustering Approach To Identifying Low Lunar Frozen Orbits In A High-Fidelity Model
Low lunar frozen orbits are of continued interest in the astrodynamics community for trajectory design and space domain awareness. This paper presents a data-driven approach to analyzing a wide variety of numerically-generated lunar trajectories in a 100 × 100 lunar gravity model with the point mass gravity of the Earth and Sun. First, clustering is used to extract a summary of these trajectories: within each cluster, trajectories possess a geometrically similar evolution of perilune but varying drift and lifetimes. Within some clusters, trajectories with a bounded perilune evolution are also identified to produce candidates for lunar frozen orbits of distinct geometries.
lunar frozen orbit gravity model data clustering↗
Global Modeling of Tropospheric Chemistry with Assimilated Meteorology: Model Description and Evaluation
We present a first description and evaluation of GEOS-CHEM, a global three-dimensional (3-D) model of tropospheric chemistry driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Data Assimilation Office (DAO). The model is applied to a 1-year simulation of tropospheric ozone-NOx-hydrocarbon chemistry for 1994, and is evaluated with observations both for 1994 and for other years. It reproduces usually to within 10 ppb the concentrations of ozone observed from the worldwide ozonesonde data network. It simulates correctly the seasonal phases and amplitudes of ozone concentrations for different regions and altitudes, but tends to underestimate the seasonal amplitude at northern midlatitudes. Observed concentrations of NO and peroxyacetylnitrate (PAN) observed in aircraft campaigns are generally reproduced to within a factor of 2 and often much better. Concentrations of HNO3 in the remote troposphere are overestimated typically by a factor of 2-3, a common problem in global models that may reflect a combination of insufficient precipitation scavenging and gas-aerosol partitioning not resolved by the model. The model yields an atmospheric lifetime of methylchloroform (proxy for global OH) of 5.1 years, as compared to a best estimate from observations of 5.5 plus or minus 0.8 years, and simulates H2O2 concentrations observed from aircraft with significant regional disagreements but no global bias. The OH concentrations are approximately 20% higher than in our previous global 3-D model which included an UV-absorbing aerosol. Concentrations of CO tend to be underestimated by the model, often by 10-30 ppb, which could reflect a combination of excessive OH (a 20% decrease in model OH could be accommodated by the methylchloroform constraint) and an underestimate of CO sources (particularly biogenic). The model underestimates observed acetone concentrations over the South Pacific in fall by a factor of 3; a missing source from the ocean may be implicated.
Bey, Isabelle↗
Modeling Magnetic Disk Wind State Transitions in Black Hole X-Ray Binaries
We analyze three prototypical black hole X-ray binaries, 4U 1630–472, GRO J1655–40, and H1743–322, in an effort to systematically understand the intrinsic state transition of the observed accretion disk winds between wind-on and wind-off states by utilizing state-of-the-art Chandra/HETGS archival data from multi-epoch observations. We apply our magnetically driven wind models in the context of magnetohydrodynamic(MHD)calculations to constrain(1)their global density slope(p),(2)their density (n17) at the foot point of the inner most launching radius, and(3)the abundances of heavier elements(AFe,S,Si). Incorporating the MHD winds into xstar photoionization calculations in a self-consistent manner, we create a library of synthetic absorption spectra given the observed X-ray continua. Our analysis clearly indicates a characteristic bimodal transition of multi-ion X-ray winds; i.e., the wind density gradient is found to steepen(fro mp∼1.2–1.4 to∼1.4–1.5)while its density normalization declines as the source transitions from the wind-onto the wind-off state. The model implies that the ionized wind remains physically present even in the wind-off state, despite its apparent absence in the observed spectra. Super solar abundances for heavier elements are also favored. Our global multi-ion wind models, taking into account soft X-ray ions as well as Fe K absorbers, show that the internal wind condition plays an important role in wind transitions besides photoionization changes. Simulated XRISM/Resolve and Athena/X-IFU spectra are presented to demonstrate a high fidelity of the multi-ion wind model for a better understanding of these powerful ionized winds in the coming decades
Keigo Fukumura↗
Event-driven simulation in SELMON: An overview of EDSE
EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.
Rouquette, Nicolas F.↗
Progress toward an explicit mechanistic model for the light-driven pump, bacteriorhodopsin
Recent crystallographic information about the structure of bacteriorhodopsin and some of its photointermediates, together with a large amount of spectroscopic and mutational data, suggest a mechanistic model for how this protein couples light energy to the translocation of protons across the membrane. Now nearing completion, this detailed molecular model will describe the nature of the steric and electrostatic conflicts at the photoisomerized retinal, as well as the means by which it induces proton transfers in the two half-channels leading to the two membrane surfaces, thereby causing unidirectional, uphill transport.
Review, Tutorial↗
Turbulent Diffusivities for Momentum, Heat, Salt and Passive Scalars
A program began ten years ago to build a turbulence model to describe high Reynolds numbers flows. Specifically, the aim was to devise a model that would satisfy two basic conditions: reproduce available turbulence data (laboratory, numerical simulations, etc.) concerning shear driven flows, buoyancy driven flows, 2D turbulence, freely decaying turbulence etc., and yet be manageable so as to be used, for example, in General Circulation Models (GCM'S). The model was presented in a series of papers that have appeared in Physics of Fluids since 1996. A total of about 80 turbulence statistics were reproduced. The model has no adjustable parameters. The next step was to apply the model to construct the vertical diffusities K for moment K(sub m), temperature K(sub h), salt K(sub s). and passive scalars K(sub c). First, we took K(sub s) = K(sub h) and tested the model using the GFDL ocean model. The results for the profiles of T and S vs. depth are indistinguishable from those derived using the latest model, the KPP model by the NCAR group. Presently, we are running the same Geophysical Fluid Dynamics Laboratory (GFDL) code relaxing the assumptive K(sub s) = K(sub h). Indeed, the turbulence model yields a salt diffusivity that depends on Ri and R rho (= Beta delta S/delta z/alpha delta T/delta z) in such a way that K(sub s) may be quite different from K(sub h). Salt fingers and double diffusivity laboratory data are reproduced. Results from the ocean model will be available shortly. Finally, we are trying to derive the horizontal diffusivities with the goal of providing a physically acceptable representation of mesoscale eddies. The recently suggested GMW parameterization has improved several O-GCM results and the goal here is to try to derive/justify it from a turbulence model and/or propose improvements/modifications. Theoretical work is in progress.
Canuto, V. M.↗
Turbulent Diffusivities for Momentum, Heat, Salt and Passive Scalars
A program began ten years ago to build a turbulence model to describe high Reynolds numbers flows. Specifically, the aim was to devise a model that would satisfy two basic conditions: reproduce available turbulence data (laboratory, numerical simulations, etc.) concerning shear driven flows, buoyancy driven flows, 2D turbulence, freely decaying turbulence etc., and yet be manageable so as to be used, for example, in GCM'S. The model was presented in a series of papers that have appeared in Physics of Fluids since 1996. A total of about 80 turbulence statistics were reproduced. The model has no adjustable parameters. The next step was to apply the model to construct the vertical diffusities K for moment K(sub m), temperature K(sub h), salt K(sub s), and passive scalars K(sub c). First, we took K(sub c) = K(sub h) and tested the model using the GFDL ocean model. The results for the profiles of T and S vs. depth are indistinguishable from those derided using the latest model, the KPP model by the NCAR group. Presently, we are running the same GFDL code relaxing the assumptive K(sub c) = K(sub h). Indeed, the turbulence model yelds a salt diffusivity that depends on R(sub i) and R(sub rho) = ((beta)(delta)S/(alpha)(delta)T/(delta)z) in such a way that K(sub s) may be quite different from K(sub h). Salt fingers and double diffusivity laboratory data are reproduced. Results from the ocean model will be available shortly. Finally, we are trying to derive the horizontal diffusivities with the goal of providing a physically acceptable representation of mesoscale eddies. The recently suggested GMW parameterization has improved several O-GCM results and the goal here is to try to derive/justify it from a turbulence model and/or propose improvements/modifications. Theoretical work is in progress.
Canuto, V. M.↗
Turbulent Diffusivities for Momentum, Heat, Salt and Passive Scalars
A program began ten years ago to build a turbulence model to describe high Reynolds numbers flows. Specifically, the aim was to devise a model that would satisfy two basic conditions: reproduce available turbulence data (laboratory, numerical simulations, etc.) concerning shear driven flows, buoyancy driven flows, 2D turbulence, freely decaying turbulence etc., and yet be manageable so as to be used, for example, in GCM'S. The model was presented in a series of papers that have appeared in Physics of FLuids since 1996. A total of about 80 turbulence statistics were reproduced. The model has no adjustable parameters. The next step was to apply the model to construct the vertical diffusivities K for moment K(sub m), temperature K(sub h), salt K(sub g) and passive scalars K(sub c). First, we took K(sub g) = K(sub h) and tested the model using the Geophysical Fluid Dynamics Laboratory (GFDL) ocean model. The results for the profiles of T and S vs. depth are indistinguishable from those derided using the latest model, the KPP model by the NCAR group. Presently, we are running the same GFDL code relaxing the assumptive K(sub g) = K(sub h). Indeed, the turbulence model yields a salt diffusivity that depends on Ri and R Rho (= Beta Sigma S/Sigma z/Alpha Sigma T/Sigma z) in such a way that K(sub g) may be quite different from K(sub h). Salt fingers and double diffusivity laboratory data are reproduced. Results from the ocean model will be available shortly. Finally, we are trying to derive the horizontal diffusivities with the goal of providing a physically acceptable representation of mesoscale eddies. The recently suggested GMW parameterization has improved several O-GCM results and the goal here is to try to derive/justify it from a turbulence model and/or propose improvements/modifications. Theoretical work is in progress.
Canuto, Vittorio↗
Physics-Guided Deep Learning for Complex System Health Management and Decision Making
The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.
Diagnostics↗