Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Predictive Control”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Agent-based Disease Surveillance and Transmission Model

The SARS-CoV-2 pandemic has driven a need for models that can predict transmission among populations and test the effectiveness of proposed interventions to control it such as testing, quarantining, vaccinations, and nonpharmaceutical interventions. Some questions of interest include:1. How rapidly will the virus spread if no action is taken? 2. How frequently should people be tested and quarantined if necessary? 3. Should unvaccinated persons be tested more frequently than the vaccinated? 4. If test results are delayed, how will that affect the spread? 5. How do test false negative rates affect the overall efficacy of testing programs? 6. How do constraints on testing resources impact the overall efficacy of testing programs? 7. How does the patient’s failure to recognize COVID symptoms and quarantine affect the spread? 8. What level of vaccination coverage is needed to suppress the epidemic? 9. What is the risk of a large outbreak (probability exceeding x infections)? 10. What is the cost effectiveness of interventions (dollars per avoided infection)? This report describes the structure of a discrete event simulation model that provides actionable information to address these questions. Parameter inputs and results of analyses have been reported in briefings provided to LLNL management and are not discussed here.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
DOE OSTI2013

Removal of water and iodine by solid sorbents: adsorption isotherms and kinetics

Tritium and iodine-129 are two major radioactive elements that are present in off-gases from spent fuel reprocessing plants. Adsorption by solid sorbents is the state-of-the-art technique for removal of these species from off-gases. Modeling and simulating adsorption processes require accurate adsorption equilibrium and kinetic data to permit reasonable estimates of process parameters. We have developed a continuous flow single-pellet adsorption system to gather accurate adsorption equilibrium and kinetic data for adsorption of water by molecular sieve 3A and for adsorption of iodine by silver exchanged mordenite. In this paper, the design of the water and iodine adsorption experimental systems are briefly described and results of water adsorption experiments are presented and discussed. Water uptake curves are fitted with the linear-driving force (LDF) model and the shrinking-core model to determine kinetic parameters. It is shown that the kinetics of water adsorption on zeolite 3A under current experimental conditions is controlled by both the external film resistance and the macro-pore diffusion and can be predicted by both the LDF model and the shrinking-core model with the former one performing slightly better. Preliminary results from iodine adsorption experiments will be presented in the conference.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Model-based analysis of environmental controls over ecosystem primary production in an alpine tundra dry meadow

We investigated several key limiting factors that control alpine tundra productivity by developing an ecosystem biogeochemistry model. The model simulates the coupled cycling of carbon (C), nitrogen (N), and phosphorus (P) and their interactions with gross primary production (GPP). It was parameterized with field observations from an alpine dry meadow ecosystem using a global optimization strategy to estimate the unknown parameters. The model, along with the estimated parameters, was first validated against independent data and then used to examine the environmental controls over plant productivity. Our results show that air temperature is the strongest limiting factor to GPP in the early growing season, N availability becomes important during the middle portion of the growing season, and soil moisture is the strongest limiting factors by late in the growing season. Overall, the controls over GPP during the growing season, from strongest to weakest, are soil moisture content, air temperature, N availability, and P availability. This simulation provides testable predictions of the shifting nature of physical and nutrient limitations on plant growth. The model also indicates that changing environmental conditions in the alpine will likely lead to changes in productivity. For example, warming eliminates the control of P availability on GPP and makes N availability surpass air temperature to become the second strongest limiting factor. In contrast, an increase in atmospheric nutrient deposition eliminates the control of N availability and enhances the importance of P availability. Furthermore, these analyses provide a quantitative and conceptual framework that can be used to test predictions and refine ecological analyses at this long-term ecological research site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advancing quantitative models of soil microbiology, ecology, and biochemistry

Rapid advances in soil biology are increasingly reflected in mathematical models. From molecular to global scales, models contribute to fundamental understanding and prediction of critical soil processes, such as carbon sequestration. Recent models explicitly incorporate microbial control over soil carbon and nutrient cycling, an approach that offers greater biological realism but raises new challenges in model parameterization and stability. Although they have proliferated at community and ecosystem scales, microbial-explicit models have yet to be incorporated into most Earth system models. Applying soil models across scales requires integration with data to support parameterization and validation using approaches such as Bayesian data assimilation. In conclusion, to catalyze future progress, we recommend breaking down barriers between modeling and empirical disciplines while broadening access to the computational infrastructure that supports soil biological modeling.

Xie, Wally↗
DOE OSTI2018

On the importance of the heat and mass transfer resistances in internally-cooled liquid desiccant dehumidifiers and regenerators

Liquid desiccant heat and mass exchangers are a promising technology for efficient humidity control in buildings. Many researchers have investigated these exchangers, often using numerical models to predict their performance. However, there is a lack of information in the literature on the magnitude of the heat and mass transfer resistances, both for the dehumidifier (which absorbs moisture from the air) and the regenerator (which heats the liquid desiccant to re-concentrate it). This article focuses on internally-cooled, 3-fluid exchangers in a parallel plate geometry. Water heats or cools a desiccant across a plate, and the desiccant absorbs or releases water into an airstream through a membrane. A sensitivity analysis was used to estimate the importance of each of the heat and mass transfer resistances (air, membrane, desiccant, plate, water), and how it changes with different design geometries. The results show that, for most designs, the latent and sensible heat transfer of the dehumidifier is dominated by the air mass transfer resistance and air heat transfer resistance, respectively. The air mass transfer resistance is also important for the regenerator, but much less so; the change in the desiccant equilibrium humidity ratio due to a change in either temperature or desiccant mass fraction is much higher at the regenerator's higher temperatures. This increases the importance of (1) getting heat from the water to the desiccant/membrane interface, and (2) diffusing salt ions quickly away from the desiccant/membrane interface. The membrane heat transfer and water heat transfer resistances were found to be the least important. These results can help inform decisions about what simplifying assumptions to make in numerical models, and can also help in designing these exchangers by understanding which resistances are most important.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Functional wettability in carbonate reservoirs

Oil adsorbs to carbonate reservoirs indirectly through a relatively thick separating water layer, and directly to the surface through a relatively thin intervening water layer. Whereas directly sorbed oil desorbs slowly and incompletely in response to changes in reservoir conditions, indirectly sorbed oil can be rapidly desorbed by changing the chemistry of the separating water layer. The additional recovery might be as much as 30% original oil in place (OOIP) above the ~30% OOIP recovered from carbonates through reservoir depressurization (primary production) and viscous displacement (waterflooding). Electrostatic adhesive forces are the dominant control over carbonate reservoir wettability. A surface complexation model that quantifies electrostatic adhesion accurately predicts oil recovery trends for carbonates. Furthermore, the approach should therefore be useful for estimating initial wettability and designing fluids that improve oil recovery.

02 PETROLEUM↗
DOE OSTI2025

High-throughput and data-driven search for stable optoelectronic AMSe 3 materials

The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Diurnal Variation of the Planetary Boundary Layer Height Observed from GNSS Radio Occultation and Radiosonde Soundings over the Southern Great Plains

>The planetary boundary layer (PBL) height (PBLH) is a key physical parameter of the PBL affected by numerous physical processes within the boundary layer. Specifically, the PBLH over land exhibits large spatial and temporal variation across different geographical regions. In this study, the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) profiles and high-resolution radiosonde profiles from 2007 to 2013 were analyzed to estimate the diurnal cycle of the PBLH over the Southern Great Plains (SGP) in the United States. Large variations in PBLH derived from radiosonde temperature, moisture, and refractivity are observed on seasonal scales. COSMIC RO is capable of observing diurnal and seasonal variations in the terrestrial PBLH over the SGP region. Annual mean diurnal amplitude of approximately 250 m in the terrestrial PBLH was observed, with maxima occurring at around 1500 local solar time (LST) in both the collocated radiosondes and COSMIC RO profiles. Seasonal changes in the PBLH diurnal cycles ranging from approximately 100 to 400 m were also observed. Such PBL diurnal and seasonal changes can be further incorporated into PBL parameterizations to help improve weather and climate model prediction. Significance Statement. The atmospheric planetary boundary layer (PBL) and its height (PBLH) control many atmospheric processes that affect our everyday lives. Observations of the PBL are usually limited to radiosondes at limited time intervals. GNSS radio occultation (RO) provide high-vertical-resolution atmospheric observations that are ideal for PBL study. This study demonstrates that the GNSS RO is capable of capturing the diurnal and seasonal variations of the PBLH over the southern Great Plains (SGP) well as compared to the collocated radiosonde observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Being Scientific: Saryu Fensin: Sparking innovations in both materials science and shock physics

As an enterprising materials scientist, Saryu Fensin leverages both experiments and simulations to improve understanding of how materials behave when subjected to extreme forces. Her aim is to use the complementary techniques to design and develop innovative materials for use in extreme conditions such as stockpile-focused applications. “To certify our stockpile with confidence, we need to understand the role a material’s microstructure plays in determining its dynamic properties,” Saryu says. “I’m hoping to solve this puzzle and improve our predictive capability to model material response in extreme conditions. Achieving this kind of specific controlled functionality of materials could have a big impact on the Laboratory’s national security science mission.” Her tools include molecular dynamics, Monte Carlo methods, gas gun experiments, metallography and various microscopy techniques. She looks forward to the prospect of new capabilities that could let her "see" inside materials.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Radiogenic Risk in Normal Tissues of Cancer Patients Receiving Radiotherapy - Paper 64

Because of its effectiveness in causing lethal damage to cancer cells and yielding local tumor control, radiotherapy is an important component of most curative cancer treatments. In radiotherapy, very high doses of radiation are delivered deliberately to patients, with the goal of conforming a prescribed radiation dose to the clinical target and nearby tissues. In this process, radiation absorbed dose damages organs and tissues both from therapeutic radiation within the treatment fields and stray radiation outside the treatment fields. Stray radiation originates either in the treatment unit or in the patient and has no known therapeutic benefit. Over the past decade, our understanding of these exposures and secondary effects has grown immensely. In this presentation, we will discuss the radiogenic risks in normal tissues of cancer patients receiving radiotherapy, with a particular focus on late effects and what is being done to reduce the risks. Long-term epidemiological studies of patients or others who have been exposed to radiation are essential for understanding the relationship between effective dose and the risk of late effects. However, in the case of epidemiological studies for patients exposed to radiation, many of the techniques used to deliver the radiation (and often the chemotherapy used) are obsolete. The results of these studies are further complicated by the direct or synergistic effects of chemotherapy and variations in genetic susceptibility, which are very difficult to take into account or control statistically. This underscores the value of predictive dosimetric studies that rely on risk models and can be done immediately without the need for long-term follow up, for example in silico clinical trials. In this presentation, we will discuss more comprehensively the risks of radiogenic late effects, specifically with regard to cancer patients receiving radiotherapy, including recent epidemiological and predictive dosimetric studies. Methods of reducing these risks will also be presented. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Flavor as an Incomplete Structure: Conceptual Questions and the Role of DUNE

Flavor remains one of the most successful yet least understood structures of the Standard Model. The discovery of the Higgs boson completed the electroweak account of mass generation, but did not explain the origin of fermion families, mass hierarchies, or mixing patterns. In this sense, flavor can be regarded as an empirically successful but conceptually incomplete structure. Neutrinos occupy a particularly sensitive place within this problem: their masses are tiny, their mixing is large, and their mass-generation mechanism may differ from that of charged fermions. In this article, we discuss flavor as an open conceptual problem and argue that DUNE, as a phased program spanning precision oscillation measurements and sensitivity to BSM and dark-sector phenomena, provides a powerful framework for testing the self-consistency and possible limits of the present three-flavor description. In particular, the complementarity between the long-baseline program and the Phase I near-detector complex, together with the DUNE-PRISM strategy for controlling interaction-model systematics and enabling data-driven near-to-far predictions, makes DUNE especially well-suited to search for small, correlated departures from the minimal flavor framework.

Montanari, Claudio S. [Fermilab; INFN, Pavia] (ORC↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Automated calculation and convergence of defect transport tensors

Defect diffusion is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant end-user intervention. These two bottlenecks prevent high-throughput implementations essential to propagate model-form uncertainty from interatomic interactions to predictive simulations. In order to address these issues, we extend a massively parallel accelerated sampling scheme, autonomously controlled by Bayesian estimators of statewide sampling completeness, to build atomistic kinetic Monte Carlo models on a state-space irreducible under exchange and space group symmetries. Focusing on isolated defects, we derive analytic expressions for drift and diffusion coefficients, providing a convergence metric by calculating the Kullback–Leibler divergence across the ensemble of diffusion processes consistent with the sampling uncertainty. The autonomy and efficacy of the method is demonstrated on surface trimers in tungsten and Hexa-interstitials in magnesium oxide, both of which exhibit complex, correlated migration mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Radioimmunotherapy with radioactive nanoparticles: Biological doses and treatment efficiency for vascularized tumors with or without a central hypoxic area

Purpose: Radioactive atoms attached to monoclonal antibodies are used in radioimmunotherapy to treat cancer while limiting radiation to healthy tissues. One limitation of this method is that only one radioactive atom is linked to each antibody and the deposited dose is often insufficient to eradicate solid and radioresistant tumors. In a previous study, simulations with the Monte Carlo N-Particle eXtended code showed that physical doses up to 50 Gy can be delivered inside tumors by replacing the single radionuclide by a radioactive nanoparticle of 5 nm diameter containing hundreds of radioactive atoms. However, tumoral and normal tissues are not equally sensitive to radiation, and previous works did not take account the biological effects such as cellular repair processes or the presence of less radiosensitive cells such as hypoxic cells. Methods: The idea is to adapt the linear-quadratic expression to the tumor model and to determine biological effective doses (BEDs) delivered through and around a tumor. This BED is then incorporated into a Poisson formula to determine the shell control probability (SCP) which predicts the cell cluster-killing efficiency at different distances ''r'' from the center of the tumor. BED and SCP models are used to analyze the advantages of injecting radioactive nanoparticles instead of a single radionuclide per vector in radioimmunotherapy. Results: Calculations of BED and SCP for different distances r from the center of a solid tumor, using the non-small-cell lung cancer as an example, were investigated for {sup 90}Y{sub 2} O{sub 3} nanoparticles. With a total activity of about 3.5 and 20 MBq for tumor radii of 0.5 and 1.0 cm, respectively, results show that a very high BED is deposited in the well oxygenated part of the spherical carcinoma. Conclusions: For either small or large solid tumors, BED and SCP calculations highlight the important benefit in replacing the single {beta}-emitter {sup 90}Y attached to each antibody by a {sup 90}Y{sub 2} O{sub 3} nanoparticle.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Abstract Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Prognostics and Health Management Framework for Wind

Operation and maintenance costs are a major driver for levelized cost of energy of wind power plants and can be reduced through optimized operation and maintenance practices accomplishable by various prognostics and health management (PHM) technologies. In recent years, the wind industry has become more open to adopting PHM solutions, especially those focusing on diagnostics. However, prognostics activities are, in general, still at the research and development stage. On the other hand, the industry has a request to estimate a component's remaining useful life (RUL) when it has faulted, and this is a key output of prognostics. Systematically presenting PHM technologies to the wind industry by highlighting the RUL prediction need potentially helps speed up its acceptance and provides more benefits from PHM to the industry. In this paper, we introduce a PHM for wind framework. It highlights specifics unique to wind turbines and features integration of data and physics domain information and models. The output of the framework focuses on RUL prediction. To demonstrate its application, a data domain method for wind turbine gearbox fault diagnostics is presented. It uses supervisory control and data acquisition system time series data, normalizes gearbox temperature measurements with reference to environmental temperature and turbine power, and leverages big data analytics and machine-learning techniques to make the model scalable and the diagnostics process automatic. Another physics-domain modeling method for RUL prediction of wind turbine gearbox high-speed-stage bearings failed by axial cracks is also discussed. Bearing axial cracking has been shown to be the prevalent wind turbine gearbox failure mode experienced in the field and is different from rolling contact fatigue, which is targeted during the bearing design stage. The method uses probability of failure as a component reliability assessment and RUL prediction metric, which can be expanded to other drivetrain components or failure modes. The presented PHM for wind framework is generic and applicable to both land-based and offshore wind turbines.

17 WIND ENERGY↗
DOE OSTI2010

EMTA’s Evaluation of the Elastic Properties for Fiber Polymer Composites Potentially Used in Hydropower Systems

Fiber-reinforced polymer composites can offer important advantages over metals where lightweight, cost-effective manufacturing and high mechanical performance can be achieved. To date, these materials have not been used in hydropower systems. In view of the possibility to tailor their mechanical properties to specific applications, they now have become a subject of research for potential use in hydropower systems. The first step in any structural design that uses composite materials consists of evaluating the basic composite mechanical properties as a function of the as-formed composite microstructure. These basic properties are the elastic stiffness, stress-strain response, and strength. This report describes the evaluation of the elastic stiffness for a series of common discontinuous fiber polymer composites processed by injection molding and compression molding in order to preliminarily estimate whether these composites could be used in hydropower systems for load-carrying components such as turbine blades. To this end, the EMTA (Copyright © Battelle 2010) predictive modeling tool developed at the Pacific Northwest National Laboratory (PNNL) has been applied to predict the elastic properties of these composites as a function of three key microstructural parameters: fiber volume fraction, fiber orientation distribution, and fiber length distribution. These parameters strongly control the composite mechanical performance and can be tailored to achieve property enhancement. EMTA uses the standard and enhanced Mori-Tanaka type models combined with the Eshelby equivalent inclusion method to predict the thermoelastic properties of the composite based on its microstructure.

36 MATERIALS SCIENCE↗