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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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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Comparison of soil dielectric mixing models for Soil Moisture Retrieval using SMAP Brightness Temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R^(2)) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 cu. m/cu. m), followed by Wang & Schmugge model (RMSE = 0.08 cu. m3/cu. m), Hallikainen model (RMSE = 0.09 cu. m/cu. m), Dobson model (RMSE = 0.10 cu. m/cu. m) and original SMAP radiometer SM (RMSE = 0.12 cu. m/cu. m). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

Swati Suman↗
DOE OSTI2021

Selecting and Implementing Resilience Metrics in Existing Energy Sector Models [Slides]

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result of the growing interest in energy sector resilience, research communities have proposed a plethora of candidate resilience indicators and metrics, most of which remain immature at different scales and segments within the energy system. A necessary focus of the research community lies in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing these challenges that need to be addressed, we explore how to effectively integrate resilience considerations into energy sector models and tools. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes - including consequences and/or changes in investment or operational decisions due to avoided consequences - based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. To demonstrate the importance of perspective, we selected models that represent different segments of the energy sector, geographic scales, and modeling approaches. A second important aspect of our effort was the development of generalized power interruption scenarios. These scenarios were intended to help establish a framework for simulating the effects of real-world threats in terms of their impacts on system components and, in turn, power interruption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Direct Study of Changes in Catalyst Structure-Kinetic Properties During Redox Transitions

The Temporal Analysis of Products (TAP) pulse response methodology is a transient technique that provides the time resolution needed to deconvolve reaction steps from the complex networks typical in industrial catalytic processes. Traditionally, TAP measurements observe gas phase dynamics at the reactor exit but lack direct measurements of changes in the catalyst itself. Recently, a new operando technique was developed that couples gas phase transients to dynamic changes in metal centers with the precise TAP methodology for nanomole titration. Using an industrial CrOx/Al2O3 catalyst used for propane dehydrogenation, we demonstrate the capabilities of this unique device to reveal key catalytic processes: 1) total propane oxidation not associated with chromia centers, 2) reduction of Cr6+ to Cr3+ correlated with selective product formation, and 3) subsequent carbon accumulation. By utilizing incremental pulsing in a diffusion-only transport regime, the spectrokinetic device allows us to resolve detailed changes in catalyst structure, composition, and kinetic function that are otherwise indistinguishable in conventional operando devices. The unification of the TAP methodology with time-resolved spectroscopic measurements offers new and unique insights into the complex kinetic phenomena regulated by solid catalyst surfaces.

03 - NATURAL GAS↗
NASA NTRS2004

Progressive Aerodynamic Model Identification From Dynamic Water Tunnel Test of the F-16XL Aircraft

Development of a general aerodynamic model that is adequate for predicting the forces and moments in the nonlinear and unsteady portions of the flight envelope has not been accomplished to a satisfactory degree. Predicting aerodynamic response during arbitrary motion of an aircraft over the complete flight envelope requires further development of the mathematical model and the associated methods for ground-based testing in order to allow identification of the model. In this study, a general nonlinear unsteady aerodynamic model is presented, followed by a summary of a linear modeling methodology that includes test and identification methods, and then a progressive series of steps suggesting a roadmap to develop a general nonlinear methodology that defines modeling, testing, and identification methods. Initial steps of the general methodology were applied to static and oscillatory test data to identify rolling-moment coefficient. Static measurements uncovered complicated dependencies of the aerodynamic coefficient on angle of attack and sideslip in the stall region making it difficult to find a simple analytical expression for the measurement data. In order to assess the effect of sideslip on the damping and unsteady terms, oscillatory tests in roll were conducted at different values of an initial offset in sideslip. Candidate runs for analyses were selected where higher order harmonics were required for the model and where in-phase and out-of-phase components varied with frequency. From these results it was found that only data in the angle-of-attack range of 35 degrees to 37.5 degrees met these requirements. From the limited results it was observed that the identified models fit the data well and both the damping-in-roll and the unsteady term gain are decreasing with increasing sideslip and motion amplitude. Limited similarity between parameter values in the nonlinear model and the linear model suggest that identifiability of parameters in both terms may be a problem. However, the proposed methodology can still be used with careful experiment design and carefully selected values of angle of attack, sideslip, amplitude, and frequency of the oscillatory data.

Murphy, Patrick C.↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2025

“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs

Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Photobiocatalysis for Abiological Transformations

Harnessing biocatalysts for novel abiological transformations is a longstanding goal of synthetic chemistry. Combining the merits of biocatalysis and photocatalysis allows for selective transformations fueled by visible light and offers many advantages including new reactivity, high enantioselectivity, greener syntheses, and high yields. Photoinduced electron or energy transfer enables synthetic methodologies that complement conventional two electron processes or offer orthogonal pathways for developing new reactions. Enzymes are well suited and can be tuned by directed evolution to exert control over radical intermediates thereby suppressing undesirable reactions and delivering high chemo- and stereoselectivities. Within the past decade, the combination of biocatalysis and photocatalysis was mainly focused on exploiting light-regenerated cofactors to increase native enzymatic activity. However, recent developments have demonstrated that the combination can unlock new-to-nature chemistry. Particularly, the discovery and application of new strategies are well poised to expand the applications of photobiocatalysis. In the past three years, our lab has been studying the combinations of photocatalysis and biocatalysis which can be applied to create new synthetic methodologies and solve challenges in synthetic organic chemistry. Our efforts have expanded the strategies for combining external photocatalysts with enzymes through the construction of a synergistic cooperative stereoconvergent reduction system consisting of photosensitized energy transfer and ene-reductase catalyzed alkene reduction. Additionally, our efforts have also extended the capability of cofactor-dependent photoenzymatic systems to include enantioselective bimolecular radical hydroalkylations of alkenes by irradiating electron donor-acceptor complexes comprised of enzymatic redox active cofactors and unnatural substrates. In this Account, we highlight strategies developed by our group and others for combining biocatalysis and photocatalysis with the aim of introducing nonnatural reactivity to enzymes. Presently, strategies applied to achieve this goal include the repurposing of newly discovered photoactive enzymes, the elucidation of new photoreactivity within cofactor-dependent enzymes, the combination of external photocatalysts with enzymes, and the construction of artificial photoenzymes. By demonstrating the successful application of these strategies for achieving selective new-to-nature transformations, we hope to spur interest in the expanding the scope of photobiocatalytic systems through the use and extension of these strategies and creation of new strategies. In addition, we hope to elucidate the intuition in synergizing the unique capabilities of biocatalysis and photocatalysis so that photobiocatalysis can be recognized as a potential solution to difficult challenges in synthetic organic chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-informed machine learning for building performance simulation-A review of a nascent field

Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing the performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on accurate building energy models, which are primarily physics-based and heavily dependent on detailed building information, expert knowledge, and case-by-case model calibrations, significantly limiting their scalability. With the development of sensing technology and the increased availability of data, there is growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have begun integrating physics priors into data-driven models, a methodology known as physics-informed machine learning (PIML). PIML is an emerging field where its definitions, methodologies, evaluation criteria, application scenarios, and future directions remain open. To bridge those gaps, this study systematically reviews the state-of-the-art PIML for BPS, offering a comprehensive definition of PIML and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance, and computational cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages, and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.

Jiang, Zixin↗
DOE OSTI2023

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Advancing environmental assessment of the circular economy: Challenges and opportunities

The lifecycle assessment (LCA) framework is widely applied to comprehensively evaluate and improve the environmental performance of a circular economy (CE). The advances and application of LCA has been primarily restricted to evaluate the environmental performance of the CE at a micro-level, wherein the CE is implemented for a single product system. However, the CE can be operationalized at two broader levels: the meso–level (for eco-industrial parks) and the macro-level (for a city, state, or nation). Six methodological challenges emerge when applying LCA to a meso- or macro-level CE and remain unaddressed in the existing literature. This includes: selecting a relevant system boundary and functional unit, addressing data paucity and uncertainty, accounting for stakeholder behavior, assessing the trade-offs from renewable energy (RE) use, accounting for manufacturing and technology evolution, and quantifying displacement and rebound. This article proposes potential solutions and research priorities to address the above challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Fluorescence Signatures of Rare Earth Metals during Precipitation in Various Conditions

Fluorescence spectroscopy is a widely used sensor methodology that analyzes light emitted from a compound or element as it decays from an excited state. This technique is very sensitive and selective, which is ideal to characterize analytes at lower limits of detection. Key example targets of significant industry and research interest include rare earth elements (REEs) such as dysprosium (Dy) and europium (Eu). These are widely used in advanced technologies including semiconductors, electric vehicle motors, lasers, and permanent magnets. Identifying new sources and responsible reutilization of REEs is essential, and new approaches to extract and recycle REEs could be notably enhanced through the integration of on-line sensors. The sensors can support faster process design, informed scale-up, and cost-effective deployment. This study covers the initial exploration of applying fluorescence-based on-line monitoring to REEs within a precipitation process. This study demonstrates the successful scale-up of a fluorescence -based sensing approach, from stationary cuvettes and small-volume microfluidic devices to continuous flow systems operating at the bench scale (10-25mL). This work also provides initial insight into the challenges of signal’s effects and utility within a turbid environment. Using a modular design for monitoring flowing solutions in a flow tube, fluorescence can be characterized for a variety of analytical targets. In this study, detection performance parameters between the cuvette and flow tube system were compared. Additionally, the response of Dy during precipitation by sodium bicarbonate in the two measurement designs was explored. This letter represents a starting point to bridge the gap between traditional fluorescence sensor measurements in a cuvette to future developments that explore the ability to integrate fluorescence sensors into extraction and separation processes at industrially relevant scales.

fluorescence↗
DOE OSTI2018

Encapsulating perovskite solar cells to withstand damp heat and thermal cycling

Perovskite solar cells (PSCs) are highly promising, but they are mechanically fragile, composed of layers with mismatches in thermal expansion coefficients, and known to decompose in the presence of heat and moisture. Here we show the development of a glass–glass encapsulation methodology for PSCs that enables them to pass the industry standard IEC 61646 damp heat and thermal cycling tests. It is important to select a thermally stable perovskite composition to withstand the encapsulation process at 150 °C and design a cell that minimizes metal diffusion. Moreover, the package needs an edge seal to effectively prevent moisture ingress and an inert encapsulant with an appropriate elastic modulus to hold the package together while allowing for compliance during temperature fluctuations. This work demonstrates that industrially relevant encapsulation techniques have the potential to enable the commercial viability of PSCs.

14 SOLAR ENERGY↗
DOE OSTI2014

Electronic structure, molecular orientation, charge transfer dynamics and solar cells performance in donor/acceptor copolymers and fullerene: Experimental and theoretical approaches

By combining experimental and theoretical approaches, the electronic structure, molecular orientation, charge transfer dynamics and solar cell performance in donor/acceptor copolymer poly[2,7-(9,9-bis(2-ethylhexyl)-dibenzosilole)-alt-4,7-bis(thiophen-2-yl) benzo-2,1,3-thiadiazole] (PSiF-DBT) films and blended with 6,6.-phenyl-C 61-butyric acid methyl ester (PSiF-DBT:PCBM) were investigated. Good agreement between experimental and theoretical PSiF-DBT UV-Vis absorption spectrum is observed and the main molecular orbitals contributing to the spectrum were determined using DFT single point calculations. Non-coplanar configuration was determined by geometric optimization calculation in isolated PSiF-DBT pentamer and corroborated by angular variation of the sulphur 1s near-edge X-ray absorption fine structure (NEXAFS) spectra. Edge-on and plane-on molecular orientations were obtained for thiophene and benzothiadiazole units, respectively. A power conversion efficiency up to 1.58%, open circuit voltage of 0.51 V, short circuit current of 8.71 mA/cm{sup 2} and a fill factor of 35% was obtained using blended PSiF-DBT:PCBM as active layer in a bulk heterojunction solar cell. Ultrafast electron dynamics in the low-femtosecond regime was evaluated by resonant Auger spectroscopy using the core-hole clock methodology around sulphur 1s absorption edge. Electron delocalization times for PSiF-DBT and PSiF-DBT:PCBM polymeric films were derived for selected excitation energies corresponding to the main transitions in the sulphur 1s NEXAFS spectra. The mixture of PSiF-DBT with PCBM improves the charge transfer process involving the π* molecular orbital of the thiophene units.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Complex Oxide Nanoparticle Synthesis: Where to Begin to Do It Right?

Synthesis of advanced ceramics requires a high degree of control over the particle size and stoichiometry of the material. When choosing a synthesis method for complex oxides it is important to begin with the correct precursors and solvents to achieve high purity nanoparticles. Here, we detail the selection process for precursors and solvents for liquid-phase precipitation synthesis. Data for metal nitrate, chloride, acetate, and oxalate precursors has been compiled to assist future synthesis. The role of hydration within the precursors is discussed as it affects the final stoichiometry of the material. Melting temperatures are also compiled for these compounds to assist in material selection. The solubility of the precursors in different solvents is examined to determine the correct solvent during synthesis. As an example, using the methodology presented here, two different materials are synthesized based on commonly available precursors. A catalyst based on a quaternary perovskite and an advanced ionic conductor based on a high entropy fluorite oxide are synthesized using precipitation methods and their characterization is detailed.

Gager, Elizabeth (ORCID:0000000306046571)↗
DOE OSTI2017

Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ET i ) to daily evapotranspiration (ET d ) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation ( R S ) to be the most robust reference variable explaining the ratio between ET d and ET i . This study aims to contribute in ET i upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation ( R Sd / R Si ) as a factor for converting ET i to ET d . This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict R Sd from R Si followed by using the R Sd / R Si ratio to convert ET i to ET d across different terrestrial ecosystems. Using R Si and R Sd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing R Sd and further used it to upscale ET i to ET d . The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. R S -based upscaled ET d produced a significant linear relation ( R 2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m -2 d -1 ; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m -2 d -1 ; approx. 10 %) with the observed ET d , although a systematic overestimation of ET d was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven R S -based ET i upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ET d prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. As a result, the overall methodology appears to be promising and has substantial potential for upscaling ET i to ET d for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Study of space shuttle orbiter system management computer function. Volume 2: Automated performance verification concepts

The findings are presented of investigations on concepts and techniques in automated performance verification. The investigations were conducted to provide additional insight into the design methodology and to develop a consolidated technology base from which to analyze performance verification design approaches. Other topics discussed include data smoothing, function selection, flow diagrams, data storage, and shuttle hydraulic systems.

Source record↗
NASA NTRS1986

Implementation of and Ada real-time executive: A case study

Current Ada language implementations and runtime environments are immature, unproven and are a key risk area for real-time embedded computer system (ECS). A test-case environment is provided in which the concerns of the real-time, ECS community are addressed. A priority driven executive is selected to be implemented in the Ada programming language. The model selected is representative of real-time executives tailored for embedded systems used missile, spacecraft, and avionics applications. An Ada-based design methodology is utilized, and two designs are considered. The first of these designs requires the use of vendor supplied runtime and tasking support. An alternative high-level design is also considered for an implementation requiring no vendor supplied runtime or tasking support. The former approach is carried through to implementation.

Laird, James D.↗