SEARCH · Engineering Papers

Results for “heterogeneous networks”

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 451 records · Page 25

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Control of Energy Transport and Transduction in Photosynthetic Down-Conversion (Final Technical Report)

The research conducted under this contract focused on three areas related to control of energy transport and transduction in natural and synthetic light harvesting systems: (1) experimental and theoretical studies exploring the use of ultrafast laser pulse shaping to manipulate the initially excited states of multi-chromophore biological light harvesting networks and influence the early time energy transfer and electronic and vibrational relaxation pathways, (2) fundamental theoretical developments aimed at computing and analyzing the dynamics of excitations of complex heterogeneous environments and nano structures, and (3) joint experimental and theoretical design studies of artificial light harvesting materials exploring the influence of incorporating different types of organic layers in low dimensional lead halide perovskite materials on their hot carrier relaxation dynamics, and theoretical studies of the design of nano structured light harvesting antenna systems. These research projects supported the training of three theoretical and computational graduate students, one experimental graduate student, and one experimental post-doctoral researcher. The research has been published in seven peer reviewed papers and presented at several international meetings and workshops.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Interpretation of Ion Irradiation and Neutron Irradiation Damage in Additively Manufactured 316 Stainless Steel using Multiscale Modeling

The accelerated adoption of nuclear energy necessitates advanced manufacturing technologies, such as additive manufacturing, to meet heightened supply chain requirements and support innovative reactor technologies. Due to the unique microstructural characteristics of additively manufactured materials under distinct solidification conditions, comprehensive evaluation of their performance in reactor environments is essential. The Advanced Materials and Manufacturing Technologies program under the Department of Energy's Office of Nuclear Energy focuses on understanding the irradiation performance and damage evolution of laser powder bed fusion 316 stainless steel, with an emphasis on integrating ion and neutron irradiation data to accelerate the development and qualification of materials for advanced nuclear reactor applications. While ion irradiation is a cost- and time-effective method, modeling and simulation are required to interpret the data for the broader range of irradiation conditions encountered in advanced reactors. In fiscal year 2025, integrated multiscale modeling and simulations were conducted to assess irradiation damage in additively manufactured 316 stainless steel. Key outcomes include predictions of chromium enrichment at grain boundaries, nickel enrichment at dislocation cell walls and void surfaces, and heterogeneous void evolution under ion and neutron irradiation conditions. Cluster dynamics simulations revealed the coarsening of voids at high irradiation temperatures and the suppression of void growth by high network dislocation density, while also demonstrating significant growth and coarsening of voids and self-interstitial atom loops at low dose rates. Machine learning-accelerated atomistic simulations highlighted the impact of the local environment and chromium concentration on vacancy diffusivity, providing key insights on the influence of composition on void swelling and radiation-induced segregation. Additionally, molecular dynamics simulations demonstrated the presence of defect production bias and a significant effect of carbon content on defect cluster behavior. These combined efforts aim to predict the performance of additively manufactured materials under various reactor conditions, supporting their qualification for nuclear reactor applications by interpreting ion irradiation data. This report underscores the potential of integrated multiscale modeling to analyze ion irradiation data in the effort to accelerate the qualification of additively manufactured materials for nuclear reactor components.

316 stainless steel↗

NASA NTRS2009

Accelerating Climate Simulations Through Hybrid Computing

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

Zhou, Shujia↗

DOE OSTI2024

Resilience of the slow component in timescale separated synchronized oscillators

Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Resilience of the slow component in timescale-separated synchronized oscillators

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1991

Hypercube technology

The JPL designed MARKIII hypercube supercomputer has been in application service since June 1988 and has had successful application to a broad problem set including electromagnetic scattering, discrete event simulation, plasma transport, matrix algorithms, neural network simulation, image processing, and graphics. Currently, problems that are not homogeneous are being attempted, and, through this involvement with real world applications, the software is evolving to handle the heterogeneous class problems efficiently.

Parker, Jay W.↗

DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗

DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗

DOE OSTI2025

VUV Photoionization Dynamics and Reactivity of Heterogeneous Water Clusters

This feature focuses on bridging isolated water and bulk water studies. It assembles experiments and theory on water clusters, mainly probed by vacuum ultraviolet (VUV) radiation, and summarizes what the effects are of intermolecular interactions on both the spectroscopy and the VUV-induced processes in water. In particular, it highlights studies of heterogeneous water clusters─those incorporating other molecular species such as naphthalene, methane, formic acid, and glycerol─which serve as model systems to investigate the fundamental roles of water in hydrogen bonding networks, proton transfer, and astrochemical processes. These mixed clusters provide a platform to revisit the stability of protonated water clusters and compile observations of ionization-induced structural rearrangements and fragmentation, especially in systems involving hydroxyl-rich cosolvents. This feature then explores energy transfer mechanisms in molecular clusters, following VUV photoexcitation. It concludes with potential future directions. First, we investigate excited-state dynamics in molecular clusters through direct probing, complementing the synchrotron studies discussed here. Second, exploring gas-phase molecule evaporation from confined spaces and interfaces using advanced spectroscopic techniques sheds light on these ubiquitous, yet currently debated, molecular processes.

Cluster chemistry↗

DOE OSTI2025

Allometric Scaling of Hyporheic Respiration Across Basins in the Pacific Northwest United States

Abstract Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power‐law relationships between size and function) to river networks provides a new paradigm for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach‐scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model‐generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Spin Decoherence Dynamics of Er 3+ in CeO 2 Films

Developing telecom-compatible spin-photon interfaces is essential towards scalable quantum networks. Erbium ions (Er 3+ ) exhibit a unique combination of a telecom (1.5 mu m) optical transition and an effective spin-1=2 ground state, but identifying a host that enables heterogeneous device integration while preserving long optical and spin coherence remains an open challenge. In this work, we explore the potential of Er 3+ :CeO 2 films on silicon and study the Er 3+ spin coherence, offering low nuclear spin density and the potential for on-chip integration. We demonstrate a 38.8 mu s spin coherence, which can be extended to 176.4 mu s with dynamical decoupling. Pairing experiments with cluster correlation expansion calculations, we identify spectral diffusion induced by bath Er 3+ spin flip-flops as the dominant decoherence mechanism and provide pathways to millisecond-scale coherence.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗

DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Atomic-scale structure of ZrO 2 : Formation of metastable polymorphs

Metastable phases can exist within local minima in the potential energy landscape when they are kinetically “trapped” by various processing routes, such as thermal treatment, grain size reduction, chemical doping, interfacial stress, or irradiation. Despite the importance of metastable materials for many technological applications, little is known about the underlying structural mechanisms of the stabilization process and atomic-scale nature of the resulting defective metastable phase. Investigating ion-irradiated and nanocrystalline zirconia with neutron total scattering experiments, we show that metastable tetragonal ZrO 2 consists of an underlying structure of ferroelastic, orthorhombic nanoscale domains stabilized by a network of domain walls. The apparent long-range tetragonal structure that can be recovered to ambient conditions is only the configurational ensemble average of the underlying orthorhombic domains. This structural heterogeneity with a distinct short-range order is more broadly applicable to other nonequilibrium materials and provides insight into the synthesis and recovery of functional metastable phases with unique physical and chemical properties.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Data Fusion for the Development of a Multimodal Freight Transload Facilities Dataset in the U.S.

To withstand the growing demand of commodity volume and its strain on the transportation infrastructure, it is necessary to identify the flow of commodities by route and mode. However, a national multimodal freight routing model does not exist for the U.S. The development of such model requires multiple building blocks, such as virtual representations of roadway, railway, and waterway networks, transload facilities (TFs), and access/egress links. Most of these blocks have a robust database in the U.S., except for the TFs. Here, this paper presents the fusion of dispersed and heterogeneous representations of multimodal TFs into a single, comprehensive, geospatial freight TF dataset. The TF dataset is derived from several sources, including the U.S. Army Corps of Engineers Master Docks Plus, the National Transportation Atlas Database, the Intermodal Association of North America, industry publications, and other public information. First, individual datasets were queried and reconciled. A geocoding/reverse geocoding process was applied to get the best street address and latitude/longitude location for each terminal. Then, duplicate terminals were identified by a fuzzy match algorithm based on terminal name and location, and removed. Validation was performed by visual inspection of random facilities. The main contributions of this work are: a publicly available version of the TF dataset, including facility location and multimodal transfer capability of 9,003 facilities, and an enterprise-version with the same facilities but including commodity handling capabilities. The main purpose of developing the TF dataset is to inform multimodal routing algorithms. The proposed TF dataset allows for credibly modeling the multimodal transfer of commodities within shipment routes.

Commodity Routing↗

NASA NTRS2010

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Smelyanskiy, Vadim N.↗