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DOE OSTI2025

Increasing Mosquito Abundance Under Global Warming

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence‐based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short‐Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5‐8.5, respectively. We expect that this model can serve as a valuable tool and inform science‐based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Ground truth for oceanic rainfall

Communications systems operating at frequencies in excess of 10 GHz are degraded significantly by rainfall. To provide the information needed for design of these millimeter wave systems, rain attentuation models were developed and data bases of propagation related information were accumulated. These data bases were developed based on the signal level measurements of geostationary satellite beacons at selected frequencies. Groundbased radar reflection measurements were able to develop data bases for system design. The rain attenuation models allow accurate correlation between the rain rate and the attenuation.

Dorman, C. E.↗

NASA NTRS2017

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

There are many flare forecasting models. For an excellent review and comparison of some of them see Barnes et al. (2016). All these models are successful to some degree, but there is a need for better models. We claim the most successful models explicitly or implicitly base their forecasts on various estimates of components of the photospheric current density J, based on observations of the photospheric magnetic field B. However, none of the models we are aware of compute the complete J. We seek to develop a better model based on computing the complete photospheric J. Initial results from this model are presented in this talk. We present a data driven, near photospheric, 3 D, non-force free magnetohydrodynamic (MHD) model that computes time series of the total J, and associated resistive heating rate in each pixel at the photosphere in the neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of B measured by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series of B in every AR pixel. Errors in B due to these periods can be significant.

Goodman, Michael L.↗

NASA NTRS1988

A practical approach to object based requirements analysis

Presented here is an approach developed at the Unisys Houston Operation Division, which supports the early identification of objects. This domain oriented analysis and development concept is based on entity relationship modeling and object data flow diagrams. These modeling techniques, based on the GOOD methodology developed at the Goddard Space Flight Center, support the translation of requirements into objects which represent the real-world problem domain. The goal is to establish a solid foundation of understanding before design begins, thereby giving greater assurance that the system will do what is desired by the customer. The transition from requirements to object oriented design is also promoted by having requirements described in terms of objects. Presented is a five step process by which objects are identified from the requirements to create a problem definition model. This process involves establishing a base line requirements list from which an object data flow diagram can be created. Entity-relationship modeling is used to facilitate the identification of objects from the requirements. An example is given of how semantic modeling may be used to improve the entity-relationship model and a brief discussion on how this approach might be used in a large scale development effort.

Drew, Daniel W.↗

NASA NTRS1993

ERS-1 and Seasat scatterometer measurements of ocean winds: Model functions and the directional distribution of short waves

Calculation of accurate vector winds from scatterometers requires knowledge of the relationship between backscatter cross-section and the geophysical variable of interest. As the detailed dynamics of wind generation of centimetric waves and radar-sea surface scattering at moderate incidence angles are not well known, empirical scatterometer model functions relating backscatter to winds must be developed. Less well appreciated is the fact that, given an accurate model function and some knowledge of the dominant scattering mechanisms, significant information on the amplitudes and directional distributions of centimetric roughness elements on the sea surface can be inferred. accurate scatterometer model functions can thus be used to investigate wind generation of short waves under realistic conditions. The present investigation involves developing an empirical model function for the C-band (5.3 GHz) ERS-1 scatterometer and comparing Ku-band model functions with the C-band model to infer information on the two-dimensional spectrum of centimetric roughness elements in the ocean. The C-band model function development is based on collocations of global backscatter measurements with operational surface analyses produced by meteorological agencies. Strengths and limitations of the method are discussed, and the resulting model function is validated in part through comparison with the actual distributions of backscatter cross-section triplets. Details of the directional modulation as well as the wind speed sensitivity at C-band are investigated. Analysis of persistent outliers in the data is used to infer the magnitudes of non-wind effects (such as atmospheric stratification, swell, etc.). The ERS-1 C-band instrument and the Seasat Ku-band (14.6 GHz) scatterometer both imaged waves of approximately 3.4 cm wavelength assuming that Bragg scattering is the dominant mechanism. Comparisons of the C-band and Ku-band model functions are used both to test the validity of the postulated Bragg mechanism and to investigate the directional distribution of the imaged waves under a variety of conditions where Bragg scatter is dominant.

Freilich, Michael H.↗

NASA NTRS2017

Model-Based Thermal System Design Optimization for the James Webb Space Telescope

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Cataldo, Giuseppe↗

NASA NTRS2012

Motion Imagery and Robotics Application (MIRA): Standards-Based Robotics

This technology development originated from the need to assess the debris threat resulting from soil material erosion induced by landing spacecraft rocket plume impingement on extraterrestrial planetary surfaces. The impact of soil debris was observed to be highly detrimental during NASA s Apollo lunar missions and will pose a threat for any future landings on the Moon, Mars, and other exploration targets. The innovation developed under this program provides a simulation tool that combines modeling of the diverse disciplines of rocket plume impingement gas dynamics, granular soil material liberation, and soil debris particle kinetics into one unified simulation system. The Unified Flow Solver (UFS) developed by CFDRC enabled the efficient, seamless simulation of mixed continuum and rarefied rocket plume flow utilizing a novel direct numerical simulation technique of the Boltzmann gas dynamics equation. The characteristics of the soil granular material response and modeling of the erosion and liberation processes were enabled through novel first principle-based granular mechanics models developed by the University of Florida specifically for the highly irregularly shaped and cohesive lunar regolith material. These tools were integrated into a unique simulation system that accounts for all relevant physics aspects: (1) Modeling of spacecraft rocket plume impingement flow under lunar vacuum environment resulting in a mixed continuum and rarefied flow; (2) Modeling of lunar soil characteristics to capture soil-specific effects of particle size and shape composition, soil layer cohesion and granular flow physics; and (3) Accurate tracking of soil-borne debris particles beginning with aerodynamically driven motion inside the plume to purely ballistic motion in lunar far field conditions.

Martinez, Lindolfo↗

NASA NTRS1989

Expert systems for automated maintenance of a Mars oxygen production system

A prototype expert system was developed for maintaining autonomous operation of a Mars oxygen production system. Normal operation conditions and failure modes according to certain desired criteria are tested and identified. Several schemes for failure detection and isolation using forward chaining, backward chaining, knowledge-based and rule-based are devised to perform several housekeeping functions. These functions include self-health checkout, an emergency shut down program, fault detection and conventional control activities. An effort was made to derive the dynamic model of the system using Bond-Graph technique in order to develop the model-based failure detection and isolation scheme by estimation method. Finally, computer simulations and experimental results demonstrated the feasibility of the expert system and a preliminary reliability analysis for the oxygen production system is also provided.

Ash, Robert L.↗

NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗

NASA NTRSDate not supplied

The Anatomy of Software Changes and Bugs in Autonomous Operating System

Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.

Katerina Goseva-Popstojanova↗

NASA NTRS1994

Enhancements to the KATE model-based reasoning system

KATE (Knowledge-based Autonomous Test Engineer) is a model-based software system developed in the Artificial Intelligence Laboratory at the Kennedy Space Center for monitoring, fault detection, and control of launch vehicles and ground support systems. This report describes two software efforts which enhance the functionality and usability of KATE. The first addition, a flow solver, adds to KATE a tool for modeling the flow of liquid in a pipe system. The second addition adds support for editing KATE knowledge base files to the Emacs editor. The body of this report discusses design and implementation issues having to do with these two tools. It will be useful to anyone maintaining or extending either the flow solver or the editor enhancements.

Thomas, Stan J.↗

DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Development & Experimental Validation of a Generalized Resistance-Capacitance Model for Numerical Simulation of Phase-Change Material Embedded Heat Exchangers

Latent heat thermal energy storage (LHTES) using phase change material (PCM) has attracted increased attention as a viable solution for overcoming the mismatch between energy supply and demand for renewable energy-based systems. PCM-embedded heat exchangers (PCM-HX) have the potential to significantly improve thermal performance due to high storage capacity and low temperature variation during the phase change process. Most models for simulating LHTES heat transfer use Computational Fluid Dynamics (CFD) simulations, which have high computational costs resulting from considering the complex and time-dependent physics relevant to PCM-HXs. In this paper, a Generalized Resistance Capacitance-based Model (GRCM) was developed to predict the thermal performance of arbitrary PCM-HXs in a computationally efficient manner without compromising modeling accuracy. The GRCM is exercised for three case studies: (i) verification for a single-slabbed finned PCM-HX, (ii) verification and validation for a copper foam/paraffin composite PCM-HX, and (iii) validation for a straight tube annular finned PCM-HX. The copper foam PCM-HX uses an electric heater at the top of HX, while the other two configurations utilize water as heat transfer fluid. For the single-slabbed finned PCM-HX melting case, the mean deviation in average PCM temperature predicted by the GRCM compared to the CFD model was between 0.56 – 0.73 K, with maximum temperature deviation of 2.68 K. For the HTF outlet temperature, the validation results showed that GRCM prediction matches very well with experimental data, with mean temperature deviation of 0.24 K during melting case, while for solidification case was 0.34 K. These results showcase the GRCM’s capability for accurately reproducing the thermal characteristics of PCM-HXs with considerably lower computational effort.

42 ENGINEERING↗

NASA NTRS2004

Advanced Diagnostic System on Earth Observing One

In this infusion experiment, the Livingstone 2 (L2) model-based diagnosis engine, developed by the Computational Sciences division at NASA Ames Research Center, has been uploaded to the Earth Observing One (EO-1) satellite. L2 is integrated with the Autonomous Sciencecraft Experiment (ASE) which provides an on-board planning capability and a software bridge to the spacecraft's 1773 data bus. Using a model of the spacecraft subsystems, L2 predicts nominal state transitions initiated by control commands, monitors the spacecraft sensors, and, in the case of failure, isolates the fault based on the discrepant observations. Fault detection and isolation is done by determining a set of component modes, including most likely failures, which satisfy the current observations. All mode transitions and diagnoses are telemetered to the ground for analysis. The initial L2 model is scoped to EO-1's imaging instruments and solid state recorder. Diagnostic scenarios for EO-1's nominal imaging timeline are demonstrated by injecting simulated faults on-board the spacecraft. The solid state recorder stores the science images and also hosts: the experiment software. The main objective of the experiment is to mature the L2 technology to Technology Readiness Level (TRL) 7. Experiment results are presented, as well as a discussion of the challenging technical issues encountered. Future extensions may explore coordination with the planner, and model-based ground operations.

Hayden, Sandra C.↗

NASA NTRS1988

Fatigue life prediction modeling for turbine hot section materials

A major objective of the fatigue and fracture efforts under the Hot Section Technology (HOST) program was to significantly improve the analytic life prediction tools used by the aeronautical gas turbine engine industry. This was achieved in the areas of high-temperature thermal and mechanical fatigue of bare and coated high-temperature superalloys. The cyclic crack initiation and propagation resistance of nominally isotropic polycrystalline and highly anisotropic single crystal alloys were addressed. Life prediction modeling efforts were devoted to creep-fatigue interaction, oxidation, coatings interactions, multiaxiality of stress-strain states, mean stress effects, cumulative damage, and thermomechanical fatigue. The fatigue crack initiation life models developed to date include the Cyclic Damage Accumulation (CDA) and the Total Strain Version of Strainrange Partitioning (TS-SRP) for nominally isotropic materials, and the Tensile Hysteretic Energy Model for anisotropic superalloys. A fatigue model is being developed based upon the concepts of Path-Independent Integrals (PII) for describing cyclic crack growth under complex nonlinear response at the crack tip due to thermomechanical loading conditions. A micromechanistic oxidation crack extension model was derived. The models are described and discussed.

Halford, G. R.↗

NASA NTRS1989

Fatigue life prediction modeling for turbine hot section materials

A major objective of the fatigue and fracture efforts under the NASA Hot Section Technology (HOST) program was to significantly improve the analytic life prediction tools used by the aeronautical gas turbine engine industry. This was achieved in the areas of high-temperature thermal and mechanical fatigue of bare and coated high-temperature superalloys. The cyclic crack initiation and propagation resistance of nominally isotropic polycrystalline and highly anisotropic single crystal alloys were addressed. Life prediction modeling efforts were devoted to creep-fatigue interaction, oxidation, coatings interactions, multiaxiality of stress-strain states, mean stress effects, cumulative damage, and thermomechanical fatigue. The fatigue crack initiation life models developed to date include the Cyclic Damage Accumulation (CDA) and the Total Strain Version of Strainrange Partitioning (TS-SRP) for nominally isotropic materials, and the Tensile Hysteretic Energy Model for anisotropic superalloys. A fatigue model is being developed based upon the concepts of Path-Independent Integrals (PII) for describing cyclic crack growth under complex nonlinear response at the crack tip due to thermomechanical loading conditions. A micromechanistic oxidation crack extension model was derived. The models are described and discussed.

G R Halford↗

NASA NTRS2017

Using A Model-Based Systems Engineering Approach For Exploration Medical System Development

NASA's Human Research Program's Exploration Medical Capabilities (ExMC) element is defining the medical system needs for exploration class missions. ExMC's Systems Engineering (SE) team will play a critical role in successful design and implementation of the medical system into exploration vehicles. The team's mission is to "Define, develop, validate, and manage the technical system design needed to implement exploration medical capabilities for Mars and test the design in a progression of proving grounds." Development of the medical system is being conducted in parallel with exploration mission architecture and vehicle design development. Successful implementation of the medical system in this environment will require a robust systems engineering approach to enable technical communication across communities to create a common mental model of the emergent engineering and medical systems. Model-Based Systems Engineering (MBSE) improves shared understanding of system needs and constraints between stakeholders and offers a common language for analysis. The ExMC SE team is using MBSE techniques to define operational needs, decompose requirements and architecture, and identify medical capabilities needed to support human exploration. Systems Modeling Language (SysML) is the specific language the SE team is utilizing, within an MBSE approach, to model the medical system functional needs, requirements, and architecture. Modeling methods are being developed through the practice of MBSE within the team, and tools are being selected to support meta-data exchange as integration points to other system models are identified. Use of MBSE is supporting the development of relationships across disciplines and NASA Centers to build trust and enable teamwork, enhance visibility of team goals, foster a culture of unbiased learning and serving, and be responsive to customer needs. The MBSE approach to medical system design offers a paradigm shift toward greater integration between vehicle and the medical system and directly supports the transition of Earth-reliant ISS operations to the Earth-independent operations envisioned for Mars. Here, we describe the methods and approach to building this integrated model.

Hanson, A.↗

DOE OSTI2026

Experimental and simulation study of target biasing effects on plasma transport in linear plasma device MPS-LD

Linear plasma devices (LPDs) are important experimental platforms for investigating plasma–material interactions (PMI). In PMI experiments, it has been found that applying a target bias not only effectively modifies the incident ion energy, but also induces significant changes in the electron density and electron temperature, whereby the evolution of these plasma parameters is primarily governed by plasma transport processes. However, at present, the physical process and mechanism underlying such bias-induced variations remain unclear. In this work, biasing experiments under argon plasma discharge conditions were first carried out on the MPS-LD device. For the corresponding experiments, an electric potential model was newly developed based on the BOUT++ LPD module, enabling self-consistent simulations of plasma transport under biased conditions. Numerical simulations were then performed to reproduce the experimental results and to validate the accuracy of the proposed model. Finally, by combining experimental measurements with numerical simulations, a bias-voltage scan was performed to investigate how the electron density and electron temperature vary with the bias voltage (U bias ). The results show that applying negative bias decreases the target electron density (n e,T ) while increasing the target electron temperature (T e,T ). In contrast, positive bias increases both n e,T and T e,T ; however, at high positive bias, n e,T first reaches a maximum and subsequently decreases with further increases in U bias . The underlying physical mechanisms are analyzed using particle flux, momentum, and energy conservation. It indicates that the applied bias regulates the parallel electric field, thereby changing ion and electron velocities, and consequently affecting the electron density. At high positive bias, the ion velocity is further influenced by ion viscosity, leading to the reversal in n e,T . Meanwhile, the enhanced parallel electric field drives stronger currents, significantly increasing ion–electron frictional work and converting the input bias power into electron energy, which raises the electron temperature. In conclusion, these results contribute to a deeper understanding of the effects and mechanisms of biasing on plasma transport in the MPS-LD device.

BOUT++ simulation↗