Formulation of Explicit Nonlinear Thermoelastic Terms Using Additive Thermal Strains

A new method of including geometrically nonlinear thermoelastic effects in a total Lagrangian finite element formulation is developed and demonstrated. This method provides explicit formulas for the thermoelastic terms, allows greater insight into the relationship between linear and nonlinear thermoelasticity and provides alternative nonlinear solution methods. The explicit formulation is accomplished through defining a new thermal strain term, called the additive thermal strain. The relative difference between the exact thermoelastic load and commonly used approximations is investigated. It is shown that the geometrically nonlinear thermoelastic formulation reduces to the standard linear thermoelastic formulation with appropriate assumptions

Atomistic Simulations of Short-range Ordering with Light Interstitials in Inconel Superalloys

This study employed hybrid Monte Carlo Molecular Dynamics simulations to investigate the short-range ordering behavior of Ni-based superalloys doped with boron or carbon. The simulations revealed that both boron and carbon dissociated from their host Ti atoms to achieve energetically favored ordering with Cr, Mo, and Nb. Boron clusters formed as B2, surrounded by Mo, Nb, and Cr, while carbon preferentially clustered with Cr to form a Cr23C6 local motif and with Nb to form Nb2C. Distinct preferences for interstitial sites were observed, with boron favoring tetrahedral sites and carbon occupying octahedral sites. In the presence of a vacancy, B2 shifted from the tetrahedral site to the vacancy, where it remained coordinated with Mo, Nb, and Cr. Similarly, carbon utilized vacancies to form Nb2C clusters. Excess energy calculations showed that B and C exhibited strong thermodynamic stability within their short-range ordered configurations. However, under Ti-rich conditions, C was more likely to segregate into TiC, despite preexisting ordering with Cr. This shift in stability suggests that increased Ti availability would alter carbide formation pathways, drawing C away from Cr-rich networks and promoting the development of TiC. Such redistribution may disrupt the continuity of Cr-based carbide networks, which play a critical role in stabilizing grain boundaries and impeding crack propagation. These effects further underscore the impact of interstitial-induced ordering on phase stability and microstructural evolution. This work provides an atomistic perspective on how boron- and carbon-induced ordering influences microstructure and mechanical properties. These findings highlight the critical role of interstitial-induced short-range ordering and demonstrate that this mechanism can be leveraged as a design principle to fine-tune alloy microstructures for specific engineering applications. Click here for a graphical abstract

Biot Number Error in Low-Temperature Inconel Overall Effectiveness Experiments

To predict the performance of turbine materials at engine conditions, experiments are often performed at low-temperature laboratory conditions. In order to ensure the low-temperature, laboratory results accurately predict the nondimensionalized surface temperature at engine conditions, several nondimensional parameters must be matched in the experiment, including the Biot number. Matching the Biot number requires that the ratio of the thermal conductivity of the material to the thermal conductivity of the air must be matched between laboratory experiments and engine conditions. With traditional nickel alloys such as Inconel, it is sometimes assumed that the Biot number is matched since Inconel\u27s thermal conductivity variation with temperature scales relatively closely with that of air. However, the thermal conductivity ratio does not scale perfectly and therefore some Biot number error does indeed exist, with the problem exacerbated at lower testing temperatures. To date, there has been no experimentally verified quantification of the error in the overall effectiveness, ϕ, that might be caused by this Biot number error. Ti-6Al-4V is predicted to allow for a better Biot number match, thereby better simulating Inconel at engine conditions in typical low-temperature experiments. In this research, we utilized geometrically identical models constructed of Ti-6Al-4V and Inconel 718 to evaluate the error in overall effectiveness that might occur through simply using an actual engine nickel alloy part at experimental conditions. While the Ti-6Al-4V model has a nearly perfectly matched Biot number, the Inconel model\u27s Biot number was 73% higher than appropriate. The results demonstrate that ϕ measured in low-temperature tests performed on an Inconel turbine component do not suffer markedly from Biot number error. The theoretically more Biot number appropriate Ti-6Al-4V model produced area-averaged overall effectiveness values that differed by only 0.01 from its Inconel counterpart. These results suggest that typical nickel superalloys used in turbine components may be tested at low temperature without the use of a surrogate material to better match Biot number

Hypersonic boundary layer flow at an axisymmetric stagnation point on a blunt body

A boundary layer analysis is presented for hypersonic flow at an axisymmetric stagnation point region of a blunt body under isothermal and adiabatic boundary conditions. Consideration is given to variable properties of air. It has been shown that surface drag and heat transfer rates may be controlled by applying magnetic field and vectored surface mass transfer. The range of Mach numbers considered is 1–10. As the magnetic field strength M increases, friction factor and heat transfer rate (in the case of isothermal surface) or surface temperature (in the case of adiabatic surface) increase. Friction factor and surface temperature (in the case of adiabatic surface) can be reduced by applying vectored surface mass transfer

Multi-class Classification of Satellite Orbits for Database Quality Control

The Joint Spectrum Center (JSC) Equipment, Tactical, and Space (JETS) database contains 9,539 satellite records. When new data is ingested the satellite orbit type needs to be identified, which is currently a manual process. To save time, this work explores automating the process using machine learning. Several statistical machine learning and neural network models were developed and compared using the weighted averages of precision, recall, and F1 score metrics. The number of records used in training and testing was 1,024 with a 60/20/20 train, validation, and test split. Six orbital parameters were initially used to fit the models, but three parameters (the mean motion, eccentricity, and inclination) were most important in determining orbit type. A decision tree model with the three most important orbital parameters as inputs best identified the seven target orbit types. The weighted averages of the precision, recall, and F1 score on the test data were 0.991, 0.990, and 0.990 respectively. This compared favorably to the F1 metrics for a random classifier (0.106) and a model that always predicted the majority class (0.103)

Occulation Observations with Event-based Vision Sensors [ Poster ]

Event-based vision sensors (EVS) offer exceptional change detection capabilities due to their asynchronous and independent pixels which record binary events with changes in their photocurrent. The resulting address event representation data is a sparse timeseries list

Preliminary investigation of planar Sun-Ceres trajectories in variable restricted dynamical models

Excerpt: The dwarf planet Ceres is the largest body in the Asteroid Belt and represents a key destination for planetary science missions seeking to understand the evolution of small bodies. Due to its location on the boundary between the inner and outer planets of the Solar System, Ceres could become a lucrative base camp location for asteroid mining and surveying operations, or a way-station for outer planet missions. This paper, for the first time in literature, presents 93 unique planar periodic and quasi-periodic orbits discovered in the Sun-Ceres system that may be employed for Ceres-focused missions. The Circular Restricted Three-Body Problem (CR3BP) and Circular Restricted N-Body Problem (CRNBP) are used as the primary dynamical models for orbit generation

Statistical Reliability Estimation of Deep Space Satellites and Launch Vehicles: 1958–2022

On-orbit flight data for deep space satellites and space launch vehicles (SLVs) servicing deep space missions operated between 1958 and 2022 is analyzed due to a lack of reliability research combining both satellites and SLVs for this class of mission. Satellite reliability is first estimated by the Kaplan–Meier estimator, and then parameterized through the Weibull distribution. This process is applied to the general deep space satellite data set as well as the delimitation of the data according to decade. SLV reliability is computed using standard maximum likelihood estimation and an evolving first-level Bayesian estimation scheme. Results from the satellite analysis demonstrated the differences between the parameterization methods used. The bulk satellite data set indicated that deep space satellites suffer from infant mortality, while certain subsets were found to have a primary wear-out failure mode. In general, U.S. deep space missions demonstrated the highest reliability. Deep space satellite reliability was also found to have increased as a function of time. Results from the SLV data set revealed that deep space launches are primarily performed by four main launch vehicle families. Additionally, most deep space SLV failures occurred in the space age (1959–1975), with the USA and USSR/Russia being the only two countries to experience deep space launch failures. With both deep space SLV and satellite reliability increasing over time, reliability is not expected to be a major hindrance to future deep space missions