SEARCH · Engineering Papers

Results for “constitutive models”

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.

SourceResearch connectionFrom yearThrough year

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

At least 847 records · Page 47

All sources NASA NTRS DOE OSTI

NASA NTRS2006

Simulation of Delamination Propagation in Composites Under High-Cycle Fatigue by Means of Cohesive-Zone Models

A damage model for the simulation of delamination propagation under high-cycle fatigue loading is proposed. The basis for the formulation is a cohesive law that links fracture and damage mechanics to establish the evolution of the damage variable in terms of the crack growth rate dA/dN. The damage state is obtained as a function of the loading conditions as well as the experimentally-determined coefficients of the Paris Law crack propagation rates for the material. It is shown that by using the constitutive fatigue damage model in a structural analysis, experimental results can be reproduced without the need of additional model-specific curve-fitting parameters.

Turon, Albert↗

DOE OSTI2026

Theoretical and kinetic modeling study of hydrazine oxidation

The present work constitutes the first theoretical and kinetic modeling study of hydrazine oxidation, which may be important for burnout in ammonia-fueled combustion. The kinetics of the oxidation of N 2 H 4 , N 2 H 3 and tHNNH by molecular oxygen were investigated via a quantum chemistry/canonical transition state theory approach. Geometries and anharmonic frequencies were obtained with density functional theory, and energies from coupled cluster calculations (CCSD(T)) extrapolated to the infinite basis set limit, with corrections for core-valence electron correlation, scalar relativistic effects, and higher level correlation up to lambda coupled cluster, CCSDT(Q) Λ . The key reactions occurred on the N 2 H 4 O 2 potential energy surface, where the results indicated a fast reaction of N 2 H 3 with HO 2 via singlet adducts to yield tHNNH + H 2 O 2 and HNN(H)O + H 2 O, while reaction on the triplet surface proceeds via a bound complex followed by a tight, submerged barrier to yield N 2 H 4 + O 2 . The results were incorporated in a detailed reaction mechanism, which was used to interpret the shock tube results from Michel and Wagner (1965) on the effect of O 2 on hydrazine conversion at 1100–1400 K. The kinetic model captured qualitatively the observed behavior, but underestimated the reaction rate under oxidizing conditions. The hydrazine pyrolysis chemistry dominated conversion at reducing conditions and/or high temperature. At oxidizing conditions and intermediate temperatures (≲ 1400 K), reactions of N 2 -amines with HO 2 and O 2 were important for the oxidation rate.

Ab initio calculations↗

NASA NTRS1991

Inelastic Strain Analysis of Solder Joint in NASA Fatigue Specimen

The solder fatigue specimen designed by NASA-GSFC/UNISYS is analyzed in order to obtain the inelastic strain history during two different representative temperature cycles specified by UNISYS. In previous reports (dated July 25, 1990, and November 15, 1990), results were presented of the elastic-plastic and creep analysis for delta T = 31 C cycle, respectively. Subsequent results obtained during the current phase, from viscoplastic finite element analysis of the solder fatigue specimen for delta T = 113 C cycle are summarized. Some common information is repeated for self-completeness. Large-deformation continuum formulations in conjunction with a standard linear solid model is utilized for modeling the solder constitutive creep-plasticity behavior. Relevant material properties are obtained from the literature. Strain amplitudes, mean strains, and residual strains (as well as stresses) accumulated due to a representative complete temperature cycle are obtained as a result of this analysis. The partitioning between elastic strains, time-independent inelastic (plastic) strains, and time-dependent inelastic (creep) strains is also explicitly obtained for two representative cycles. Detailed plots are presented for two representative temperature cycles. This information forms an important input for fatigue damage models, when predicting the fatigue life of solder joints under thermal cycling

Dasgupta, Abhijit↗

DOE OSTI2025

Measurement of Close-in Ground Motion from an Underground Chemical Explosion

Understanding the geophysical response near an underground explosion is crucial for generating insights into the source and emplacement conditions that produce distinct observations in monitoring scenarios occurring at greater distances. Recently, Shot A of the Low Yield Nuclear Monitoring (LYNM) Physics Experiment 1 (PE1) series was conducted at the Nevada National Security Site to provide ground truth for subsurface explosion signal models. This experiment resulted in measuring near-source ground motion at distances ranging from 70 to 1000 m/kt with a 99% success rate, yielding high-fidelity knowledge of the near-field response that can serve as benchmarks for future numerical modeling and experiment planning. However, technical challenges exist in observing near-source phenomena while safeguarding sensitive data acquisition components from the detrimental effects of ground motion in the subsurface. This report outlines tools and techniques to address challenges associated with observing near-source accelerations and within the tunnel drift of the PE1 test bed. Additionally, we describe key systems designed with both modern advancements and legacy guidance to maximize the collection of high-quality ground motion data, which may be applied to constitutive and computational models, leading to new or improved understanding of the near- and far-field signals produced by underground explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Dynamic embedding of effective harmonic normal mode vibrations in all-atomistic energy gap fluctuations: Case study of light harvesting 2 complex

Environmental effects in excitation energy transfer have mostly been modeled by baths of harmonic oscillators, but to what extent such modeling provides a reliable description of actual interactions between molecular systems and environments remains an open issue. Here, we address this issue by investigating fluctuations in the excitation energies of the light harvesting 2 complex using a realistic all-atomistic simulation of the potential energy surface. Our analyses reveal that molecular motions exhibit significant anharmonic features, even for underdamped intramolecular vibrations. In particular, we find that the anharmonicity contributes to the broadening of spectral densities and substantial overlaps between neighboring peaks, which complicates the meaning of mode frequencies constituting a bath model. Thus, we develop a strategy to construct a minimally underdamped harmonic bath that has a clear connection to all-atomistic dynamics by utilizing actual normal modes of molecules but optimizing their frequencies such that the resulting bath model can best reproduce the all-atomistic simulation results. By subtracting the underdamped contribution from the entire fluctuations, we also show that identifying a residual spectral density representing all other contributions with overdamped behavior is possible. We find that this can be fitted well with a well-established analytic form of a spectral density function or, alternatively, modeled as explicit time dependent fluctuations with muti-exponential or power law type correlation functions. We provide an assessment and the implications of these possibilities. The approach presented here can also serve as a general strategy to construct a simplified bath model that can effectively represent the underlying all-atomistic bath dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1994

Modeling of Wall-Bounded Complex Flows and Free Shear Flows

Various wall-bounded flows with complex geometries and free shear flows have been studied with a newly developed realizable Reynolds stress algebraic equation model. The model development is based on the invariant theory in continuum mechanics. This theory enables us to formulate a general constitutive relation for the Reynolds stresses. Pope was the first to introduce this kind of constitutive relation to turbulence modeling. In our study, realizability is imposed on the truncated constitutive relation to determine the coefficients so that, unlike the standard k-E eddy viscosity model, the present model will not produce negative normal stresses in any situations of rapid distortion. The calculations based on the present model have shown an encouraging success in modeling complex turbulent flows.

Shih, Tsan-Hsing↗

DOE OSTI2021

Programmable interactions and emergent geometry in an array of atom clouds

Interactions govern the flow of information and the formation of correlations between constituents of many-body quantum systems, dictating phases of matter found in nature and forms of entanglement generated in the laboratory. Typical interactions decay with distance and thus produce a network of connectivity governed by geometry—such as the crystalline structure of a material or the trapping sites of atoms in a quantum simulator. However, many envisioned applications in quantum simulation and computation require more complex coupling graphs including non-local interactions, which feature in models of information scrambling in black hole, and mappings of hard optimization problems onto frustrated classical magnets. Here we describe the realization of programmable non-local interactions in an array of atomic ensembles within an optical cavity, in which photons carry information between atomic spins. By programming the distance dependence of the interactions, we access effective geometries for which the dimensionality, topology and metric are entirely distinct from the physical geometry of the array. As examples, we engineer an antiferromagnetic triangular ladder, a Möbius strip with sign-changing interactions and a treelike geometry inspired by concepts of quantum gravity. The tree graph constitutes a toy model of holographic duality, in which the quantum system lies on the boundary of a higher-dimensional geometry that emerges from measured correlations. Overall, our work provides broader prospects for simulating frustrated magnets and topological phases, investigating quantum optimization paradigms and engineering entangled resource states for sensing and computation.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

AWSD Reactive Burn Model for the HMX‐Based High Explosive LX‐04

An Arrhenius–Wescott–Stewart–Davis (AWSD) reactive burn model is applied to describe shock initiation and detonation properties of the HMX-based high explosive LX-04. The parameters in the model are calibrated to data from multiple sources. The thermodynamic equations of state used in the model are calibrated to a combination of thermochemical calculations for HMX and LX-04 as well as experimentally-measured cylinder expansion results for LX-04. The kinetic parameters are calibrated to velocity data from gas gun experiments performed using EDC-32—a high explosive with the same chemical composition as LX-04 but different structural properties, and scaled rate stick data for PBX 9012. The AWSD model is shown to accurately describe the shock initiation and propagation of LX-04. Very good agreement is observed between the available experimental data and the AWSD model output. The presented results constitute an accurate LX-04 reactive burn model for use in engineering-scale models and simulations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

Modification of the MTS model for high strain-rate behavior of TI-6AL-4V

The Mechanical Threshold Stress (MTS) model provides excellent predictive capabilities for the material constitutive response for a wide range of temperatures and strain rates. However, the MTS model fails to capture the rapidly increasing yield stress at high strain rate behavior as the deformation controlling mechanism transitions from thermal activation to drag mechanisms, only capturing the linear behavior. Further, the model typically over predicts the flow stress behavior at yield and post yield due to its use of a constant work hardening rate parameter derived from the stress–strain response at constant saturation stress. An alternative approach to fitting portions of the MTS model is investigated and mathematical models are developed to address these issues. The results show that with appropriate experimental data, the mechanical threshold stress and work hardening rate parameters within the MTS model can quite easily and accurately be modified to extend applicability to high strain rate behavior and more accurately model the initial flow stress behavior at early work hardening rates without modification of the functions core to the MTS model itself.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

High-Fidelity CFD Verification Workshop 2024 Summary: Spalart-Allmaras QCR2000-R Turbulence Model

This paper summarizes solutions submitted for the Reynolds-averaged Navier-Stokes (RANS) test suite of the High-Fidelity CFD Verification Workshop. The goal of the workshop is to establish standards for verification of computational fluid dynamics (CFD) approaches to simulations of steady and unsteady turbulent flows. The RANS verification studies focus on a one-equation Spalart-Allmaras model with quadratic constitutive relation and rotation correction, SA-neg-QCR2000-R. The verification test cases are a two-dimensional subsonic flow around a Joukowski airfoil, a three-dimensional subsonic flow around an extruded NACA 0012 wing in a tunnel, and a subsonic flow around a wing-body configuration developed for verification of solvers participating in the 5 𝑡 ℎ High-Lift Prediction Workshop. The turbulencemodel formulation, geometry, flow conditions, grids, and reference solutions are described in detail. Solutions for the test cases are computed by seven established CFD solvers on adaptedand fixed-grid families using different discretization approaches. While some noticeable differences between solutions remain, the results achieved by contributing solvers show that different solutions computed for the same RANS model on different grid families can converge to a common limit with grid refinement. The apparent requirements for grid convergence are a well designed family of grids that provide sufficient resolution in important areas and a strong solver capable of deep iterative convergence on each grid. For each test case in the study, the variation between aerodynamic forces computed by different solvers on the finest grids of different families is less than 2%.

Boris Diskin↗

NASA NTRS2009

Complexity for Survival of Living Systems

A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.

Zak, Michail↗

DOE OSTI2021

Impact Fracture and Fragmentation of Glass via the 3D Combined Finite-Discrete Element Method

A driving technical concern for the automobile industry is their assurance that developed windshield products meet Federal safety standards. Besides conducting innumerable glass breakage experiments, product developers also have the option of utilizing numerical approaches that can provide further insight into glass impact breakage, fracture, and fragmentation. The combined finite-discrete element method (FDEM) is one such tool and was used in this study to investigate 3D impact glass fracture processes. To enable this analysis, a generalized traction-separation model, which defines the constitutive relationship between the traction and separation in FDEM cohesive zone models, was introduced. The mechanical responses of a laminated glass and a glass plate under impact were then analyzed. For laminated glass, an impact fracture process was investigated and results were compared against corresponding experiments. Correspondingly, two glass plate impact fracture patterns, i.e., concentric fractures and radial fractures, were simulated. The results show that for both cases, FDEM simulated fracture processes and fracture patterns are in good agreement with the experimental observations. The work demonstrates that FDEM is an effective tool for modeling of fracture and fragmentation in glass.

42 ENGINEERING↗

NASA NTRSDate not supplied

Development of a Combined Cohesive and Virtual Crack-Closure Technique Approach to Represent R-Curves

Resistance curve (R-curve) effects due to fiber bridging, crack migration, and other blunting mechanisms are common in composite laminates. These mechanisms can dramatically increase the resistance to crack propagation but can be challenging to simulate. Delamination with R-curves can be analyzed using cohesive zone modeling (CZM) or the virtual crack closure technique (VCCT). Large fracture process zones can be simulated with CZM, but they require highly refined meshes. Coarser meshes can be used with VCCT, but this method is only applicable to small fracture process zones with R-curves defined as functions of position. Therefore, a technique with the computational efficiency of VCCT and the natural ability of cohesive elements to represent large fracture process zones is desirable. An approach is proposed that starts as a CZM in which the cohesive traction separation law (TSL) is separated into high-strength (HS) and low-strength (LS) components. The HS part, which is responsible for the mesh requirements of the CZM analysis, is replaced by VCCT. The combined method is evaluated by analyzing the response of double cantilevered beam specimens. Two specimen layup configurations, [0/90/90/0] 3s and [0/90/90/0] 9s , are evaluated. The R-curve response from each configuration is determined and applied to the constitutive properties of models built with CZM and with VCCT. The results and computational efficiency of the CZM, VCCT, and combined approaches are compared. The results are indicative that a combined cohesive/VCCT approach can enable progressive failure analyses to retain the computational efficiency of VCCT with the ability of the cohesive elements to capture R-curve effects.

Virtual Crack-Closure Technique↗

DOE OSTI2025

Baseflow Identification via Explainable AI With Kolmogorov‐Arnold Networks

Abstract Hydrological models often involve constitutive laws that may not be optimal in every application. We propose to replace such laws with the Kolmogorov‐Arnold networks (KANs), a class of neural networks designed to identify symbolic expressions. We demonstrate KAN's potential on the problem of baseflow identification, a notoriously challenging task plagued by significant uncertainty. KAN‐derived functional dependencies of the baseflow components on the aridity index outperform their original counterparts; they demonstrate that water availability, rather than potential evapotranspiration, drives baseflow by constraining actual evapotranspiration under arid conditions. On a test set, they increase the Nash‐Sutcliffe efficiency (NSE) by 65%, decrease the root mean squared error by 29%, and increase the Kling‐Gupta efficiency by 34%. This superior performance is achieved while reducing the number of fitting parameters from three to two. Next, we use data from 378 catchments across the continental United States to refine the water‐balance equation at the mean‐annual scale. The KAN‐derived equations based on the refined water balance outperform both the current aridity index model, with up to a 105% increase in NSE, and the KAN‐derived equations based on the original water balance. While the performance of our model and tree‐based machine learning methods is similar, KANs offer the advantage of simplicity and transparency and require no specific software or computational tools. This case study focuses on the aridity index formulation, but the approach is flexible and transferable to other hydrological processes. Plain Language Summary Equations used in hydrologic model are often suboptimal, resulting in reduced prediction accuracy and efficiency. We implemented Kolmogorov‐Arnold networks (KAN), a machine learning algorithm for deriving symbolic formulations, to estimate groundwater recharge and showed that it outperforms an existing state‐of‐the‐art semi‐empirical formulation. In hydrology, Nash‐Sutcliffe efficiency (NSE), root mean squared error (RMSE), and Kling‐Gupta efficiency (KGE) are commonly used to evaluate model performance. Higher NSE and KGE values indicate better performance, while lower RMSE values are preferable. Our results show that NSE increased by 71%, RMSE decreased by 32%, and KGE improved by 25%. In addition, KAN identifies an optimal functional form and can be used to derive new analytical formulas using the prior knowledge. The KAN‐inspired equation outperformed the original formulation and reduced the fitting parameters. Furthermore, we refined the water‐balance equation at the mean‐annual scale and showed that, based on the new water‐balance equation, KAN can derive new formulations that are superior to the original aridity index formulations (up to 105% increase in NSE) and KAN‐derived equations based on the original water balance. These findings highlight the significant potential of KAN to advance the scientific understanding of a wide range of hydrologic processes. Key Points Kolmogorov‐Arnold networks (KANs) enhance interpretability of machine‐learned hydrological models KAN‐derived symbolic formulations outperform state‐of‐the‐art semi‐empirical aridity indices KAN‐identified functional form yields an analytical index with fewer fitting parameters and improved performance

baseflow↗

NASA NTRS1997

Lymphocyte trafficking and HIV infection of human lymphoid tissue in a rotating wall vessel bioreactor

The pathogenesis of HIV infection involves a complex interplay between both the infected and noninfected cells of human lymphoid tissue, the release of free viral particles, the de novo infection of cells, and the recirculatory trafficking of peripheral blood lymphocytes. To develop an in vitro model for studying these various aspects of HIV pathogenesis we have utilized blocks of surgically excised human tonsils and a rotating wall vessel (RWV) cell culture system. Here we show that (1) fragments of the surgically excised human lymphoid tissue remain viable and retain their gross cytoarchitecture for at least 3 weeks when cultured in the RWV system; (2) such lymphoid tissue gradually shows a loss of both T and B cells to the surrounding growth medium; however, this cellular migration is reversible as demonstrated by repopulation of the tissue by labeled cells from the growth medium; (3) this cellular migration may be partially or completely inhibited by embedding the blocks of lymphoid tissue in either a collagen or agarose gel matrix; these embedded tissue blocks retain most of the basic elements of a normal lymphoid cytoarchitecture; and (4) both embedded and nonembedded RWV-cultured blocks of human lymphoid tissue are capable of productive infection by HIV-1 of at least three various strains of different tropism and phenotype, as shown by an increase in both p24 antigen levels and free virus in the culture medium, and by the demonstration of HIV-1 RNA-positive cells inside the tissue identified by in situ hybridization. It is therefore reasonable to suggest that gel-embedded and nonembedded blocks of human lymphoid tissue, cocultured with a suspension of tonsillar lymphocytes in an RWV culture system, constitute a useful model for simulating normal lymphocyte recirculatory traffic and provide a new tool for testing the various aspects of HIV pathogenesis.

Tissue Culture/instrumentation/methods↗

DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗

DOE OSTI2024

On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them

Abstract Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the inputs and outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding “the right quantities” to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple observation processes of the system. We determine (i) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (ii) what information is unrelated to these common observables, therefore particular to each observation process, and not contributing to the desired function. Any data-driven technique can subsequently be used to learn the input–output relation—from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular “twists” of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations from one process to entire level sets of measurements of the second process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures “now,” while the second observation process measures “in the future,” the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution.

Sroczynski, David W.↗

NASA NTRS1976

Oso 7 measurements of atomic oxygen and molecular nitrogen densities in the earth's upper atmosphere

Densities of molecular nitrogen and monatomic oxygen are presented as a function of altitude for selected intervals of March-May 1972 Oso 7 satellite data. Results on atmospheric attenuation of solar extreme UV radiation at 200-400 A, at heights from 250 to 500 km, are of particular interest. Satellite sunrise measurements above 330 km are not consistent with the Jacchia (1971) atmospheric model, and may constitute evidence for dynamic phenomena occurring in the heated upper levels. Density and temperature variations are considered mainly responsible for the sunrise anomaly observed. Checks were made for possible errors in the satellite pointing system and other instrumental effects.

Modali, S. B.↗