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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2020

Advanced Methods for Immersed Domain Multi-physics Computations

This is the final report on the project, and describes the main accomplishments in the development of the Shifted Boundary Method (SBM), a new computational framework for embedded/immersed computational mechanics applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

SAM Plug-in Development (Phase I Final Report)

The DOE Office of Nuclear Energy (NE) has created an extensive set of advanced modeling and simulation tools for nuclear engineering analysis. The advanced capabilities of these newer analysis codes require more in-depth training, skills, and knowledge in order to effectively utilize them for the design, analysis, and licensing of advanced nuclear systems and experiments. A high learning curve for inexperienced users may deter organizations from incorporating these tools into their internal processes. This project involved development of a plug-in to the Symbolic Nuclear Analysis Package (SNAP) for the System Analysis Module (SAM) tool. SAM is an advanced system analysis tool for reactor transient analyses being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SNAP provides a highly flexible framework for creating, modifying and documenting input for engineering analysis codes such as SAM as well as extensive functionality for submitting, monitoring, and interacting with the codes through an intuitive graphical user interface (GUI). The common user interface provided by SNAP minimizes the learning curve for engineers starting with a new analysis code and provides an intuitive framework for transitioning between different analysis codes. SNAP provides a powerful but intuitive interface to facilitate access to advanced modeling and simulation tools for inexperienced users. Unlike many “form based” GUI’s, SNAP maps each engineering code’s component input to an internal database which manages all component input parameters along with component interconnections. This level of abstraction permits SNAP to support several advanced capabilities such as renodalization, model validation and consistency checks, embedded documentation, model notebook generation, data ownership and reviewer tracking, and variable assignment for inputs to name a few. SNAP includes a built-in Python interpreter and is interfaced to several commercial and open source packages including CPython, MATLAB/OCTAVE, Microsoft Office, Open Office, and SANDIA’s DAKOTA package which provides Uncertainty Quantification analysis through the SNAP plug-ins. Phase I of this project involved development a fully functional basic SAM plug-in to SNAP. This plug-in provides the ability to import existing models, graphically construct, edit and submit models using SNAP’s extensive functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces

This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynamics of a reduced-order model (ROM) by solving a data-driven least-squares regression problem for low-dimensional matrix operators. Our approach instead leverages regularized kernel interpolation, which yields an optimal approximation of the ROM dynamics from a user-defined reproducing kernel Hilbert space. We show that our kernel-based approach can produce interpretable ROMs whose structure mirrors full-order model structure by embedding judiciously chosen feature maps into the kernel. The approach is flexible and allows a combination of informed structure through feature maps and closure terms via more general nonlinear terms in the kernel. We also derive a computable a posteriori error bound that combines standard error estimates for intrusive projection-based ROMs and kernel interpolants. In conclusion, the approach is demonstrated in several numerical experiments that include comparisons to operator inference using both proper orthogonal decomposition and quadratic manifold dimension reduction.

Data-driven model reduction↗
DOE OSTI2023

KG-Hub—building and exchanging biological knowledge graphs

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Impedance-Based Structural Health Monitoring for Composite Laminates at Cryogenic Environments

One of the important ways of increasing the payload in a reusable launch vehicle (RLV) is to replace heavy metallic materials by lightweight composite laminates. Among various parts and systems of the RLV, this project focuses on tanks containing cryogenic fuel. Historically, aluminum alloys have been used as the materials to construct fuel tanks for launch vehicles. To replace aluminum alloys with composite laminates or honeycomb materials, engineers have to make sure that the composites are free of defects before, during, and after launch. In addition to robust design and manufacturing procedures, the performance of the composite structures needs to be monitored constantly.In recent years, the impedance-based health monitoring technique has shown its promise in many applications. This technique makes use of the special properties of smart piezoelectric materials to identify the change of material properties due to the nucleation and progression of damage. The piezoceramic patch serves as a sensor and an actuator simultaneously. The piezoelectric patch is bonded onto an existing structure or embedded into a new structure and electrically excited at high frequencies. The signature (impedance or admittance) is extracted as a function of the exciting frequency and is compared with the baseline signature of the healthy state. The damage is quantified using root mean square deviation (RMSD) in the impedance signatures with respect to the baseline signature. A major advantage of this technique is that the procedure is nondestructive in nature and does not perturb the properties and performance of the materials and structures. This project aims at applying the impedance-based nondestructive testing technique to the damage identification of composite laminates at cryogenic temperature.

Tseng, Kevin↗
DOE OSTI2021

Standard Modular Architecture for Consumer End Plug and Play Interfaces

The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.

Chinthavali, Madhu Sudhan↗
NASA NTRS1992

Sudbury project (University of Muenster-Ontario Geological Survey): Sr-Nd in heterolithic breccias and gabbroic dikes

One major objective of our Sudbury project was to define origin and age of the huge breccia units below and above the Sudbury Igneous Complex (SIC). The heterolithic Footwall Breccia (FB) represents a part of the uplifted crater floor. It contains subrounded fragments up to several meters in size and lithic fragments with shock features (greater than 10 GPa) embedded into a fine- to medium-grained matrix. Epsilon(sub Nd)-epsilon(sub Sr) relationships point to almost exclusively parautochthonous precursor lithologies. The different textures of the matrix reflect the metamorphic history of the breccia layer; thermal annealing by the overlying hot impact melt sheet (SIC) at temperatures greater than 1000 C resulted in melting of the fine crushed material, followed by an episode of metasomatic K-feldspar growth and, finally, formation of low-grade minerals such as actinolite and chlorite. Isotope relationships in the Onaping breccias (Gray and Green Member) are much more complex. All attempts to date the breccia formation failed: Zircons are entirely derived from country rocks and lack the pronounced Pb loss caused by the heat of the slowly cooling impact melt sheet (SIC). Rb-Sr techniques using either lithic fragments of different shock stages or the thin slab method, set time limits for the apparently pervasive alkali mobility in these suevitic breccias. The data array and the intercept in the plots point to a major Rb-Sr fractionation around 1.54 Ga ago. This model age is in the same range as the age obtained for the metasomatic matrix of the FB. Rb-Sr dating of a shock event in impact-related breccias seems to be possible only if their matrix had suffered total melting by the hot melt sheet (FB) or if they contain a high fraction of impact melt (suevitic Onaping breccias), whereas the degree of shock metamorphism in rock or lithic fragments plays a minor role. In the Sudbury case, however, the impact melt in the seuvitic breccias is devitrified and recrystallized, which changed Rb/Sr ratios quite drastically. Therefore, the Onaping breccias give only age limits for alteration and low-grade metamorphism. The Sm-Nd system was not reset during the Sudbury event; clasts as well as the matrix in the FB and in the Onaping breccias show preimpact 'Archean' Nd isotope signatures.

Buhl, D.↗
NASA NTRS2002

Projection Moire Interferometry for Rotorcraft Applications: Deformation Measurements of Active Twist Rotor Blades

Projection Moire Interferometry (PMI) has been used during wind tunnel tests to obtain azimuthally dependent blade bending and twist measurements for a 4-bladed Active Twist Rotor (ATR) system in simulated forward flight. The ATR concept offers a means to reduce rotor vibratory loads and noise by using piezoelectric active fiber composite actuators embedded in the blade structure to twist each blade as they rotate throughout the rotor azimuth. The twist imparted on the blades for blade control causes significant changes in blade loading, resulting in complex blade deformation consisting of coupled bending and twist. Measurement of this blade deformation is critical in understanding the overall behavior of the ATR system and the physical mechanisms causing the reduction in rotor loads and noise. PMI is a non-contacting, video-based optical measurement technique capable of obtaining spatially continuous structural deformation measurements over the entire object surface within the PMI system field-of-view. When applied to rotorcraft testing, PMI can be used to measure the azimuth-dependent blade bending and twist along the full span of the rotor blade. This paper presents the PMI technique as applied to rotorcraft testing, and provides results obtained during the ATR tests demonstrating the PMI system performance. PMI measurements acquired at select blade actuation conditions generating minimum and maximum rotor loads are provided to explore the interrelationship between rotor loads, blade bending, and twist.

Fleming, Gary A.↗
NASA NTRSDate not supplied

Lunar-surface UV Photometric Investigation of Exospheres (LUPINE): Thermal Modeling of Payload in a Relevant Daylit Environment

Direct upward remote sensing of the moon’s exosphere from a surface vantage can address production of water-related lunar volatiles as well as their exospheric loss, ballistic transport, and ultimate adsorption in permanently shadowed regions (PSRs). Far UV (FUV) dayside measurements of atomic oxygen, liberated from regolith by energetic solar protons and micrometeorite impact, can provide critical insight into the endogenic lunar water cycle by constraining total column density [O] at site of production. A notional Lunar-surface UV Photometric Investigation of Exospheres (LUPINE) instrument is designed to exploit solar-pumped atomic oxygen fluorescence at 130.4-nm, in a manner similar to the Apollo 17 UV Spectrometer (UVS) experiment [Fastie 1973; Feldman and Morrison, 1991] and the LRO Lyman-alpha Mapping Project (LAMP) spectrograph [Cook et al., 2013], by implementing a zenith-directed FUV photometer from the lunar surface at low (± 10°) selenographic latitude during the lunar day. Atomic oxygen production from solar energetic particle impact sources is thought to maximize in near solar noon [Sarantos et al., 2012], and the lander-embedded zenith-directed LUPINE photometer, in contrast to UVS and LAMP twilight measurements from orbit, can potentially capture the full column abundance of lunar regolith liberated oxygen. Herein we describe adaptation of FUV reflective optics, pulse-counting electronics, and scattered-light-suppression technologies developed for LEO FUV photometry for the challenging thermal environment of the daylit lunar surface. Preliminary thermal modeling and TVAC measurements of heritage FUV photometer components suggests that, if allowed to shed ~8W of waste heat into the bus of a reference Commercial Lunar Payload Services (CLPS) lander, the LUPINE photometer will be kept sufficiently cool to limit dark current to less than 20 counts/s. This level of dark signal enables an OI 130.4-nm 3-sigma detection threshold of ~1 mR for assumed 2-hour integrations.

Lunar UV Photometer↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-ABe Archbold Ungrazed Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ABe Archbold Ungrazed Wetland. This is the FLUXNET version of the carbon flux data for the site US-ABe Archbold Ungrazed Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The studied wetland is embedded within an intensively managed pasture, a common landscape in Florida, with the C4 bahiagrass (Paspalum notatum Flueggé) as forage grass. The wetland is an isolated, seasonal freshwater wetland. Soil was Samsula muck of Samsula Series (hyperthermic Terric Haplosaprists)

Bernacchi, Carl [Global Change and Photosynthesis ↗
NASA NTRS1999

Calibration of the Geosar Dual Frequency Interferometric SAR

GeoSAR is an airborne, interferometric Synthetic Aperture Radar (INSAR) system for terrain mapping, currently under development by a consortium including NASA's Jet Propulsion Laboratory (JPL), Calgis, Inc., and the California Department of Conservation (CalDOC) with funding provided by the Topographic Engineering Center (TEC) of the U.S. Army Corps of Engineers and the Defense Advanced Research Projects Agency (DARPA). The radar simultaneously maps swaths on both sides of the aircraft at two frequencies, X-Band and P-Band. For the P-Band system, data is collected for two across track interferometric baselines and at the crossed polarization. The aircraft position and attitude are measured using two Honeywell Embedded GPS Inertial Navigation Units (EGI) and an Ashtech Z12 GPS receiver. The mechanical orientation and position of the antennas are actively measured using a Laser Baseline Metrology System (LBMS). In the GeoSAR motion measurement software, these data are optimally combined with data from a nearby ground station using Ashtech PNAV software to produce the position, orientation, and baseline information are used to process the dual frequency radar data. Proper calibration of the GeoSAR system is essential to obtaining digital elevation models (DEMS) with the required sub-meter level planimetric and vertical accuracies. Calibration begins with the determination of the yaw and pitch biases for the two EGI units. Common range delays are determined for each mode, along with differential time and phase delays between channels. Because the antennas are measured by the LBMS, baseline calibration consists primarily of measuring a constant offset between mechanical center and the electrical phase center of the antennas. A phase screen, an offset to the interferometric phase difference which is a function of absolute phase, is applied to the interferometric data to compensate for multipath and leakage. Calibration parameters are calculated for each of the ten processing modes, each of the operational bandwidths (80 and 160 MHZ), and each aircraft altitude. In this talk we will discuss the layout calibration sites, the synthesis of data from multiple flights to improve the calibration, methods for determining time and phase delays, and techniques for determining radiometric and polarimetric quantities. We will describe how calibration quantities are incorporated into the processor and pre-processor. We will demonstrate our techniques applied to GeoSar data and assess the stability and accuracy of the calibration. This will be compared to the modeled performance determined from calibrating the output of a point target simulator. The details of baseline determination and phase screen calculation are covered in related talks.

Chapine, Elaine↗
DOE OSTI2020

Plant Physiology Increases the Magnitude and Spread of the Transient Climate Response to CO 2 in CMIP6 Earth System Models

Increasing concentrations of CO 2 in the atmosphere influence climate both through CO 2 ’s role as a greenhouse gas and through its impact on plants. Plants respond to atmospheric CO 2 concentrations in several ways that can alter surface energy and water fluxes and thus surface climate, including changes in stomatal conductance, water use, and canopy leaf area. These plant physiological responses are already embedded in most Earth system models, and a robust literature demonstrates that they can affect global-scale temperature. However, the physiological contribution to transient warming has yet to be assessed systematically in Earth system models. Here this gap is addressed using carbon cycle simulations from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP) to isolate the radiative and physiological contributions to the transient climate response (TCR), which is defined as the change in globally averaged near-surface air temperature during the 20-yr window centered on the time of CO 2 doubling relative to preindustrial CO 2 concentrations. In CMIP6 models, the physiological effect contributes 0.12°C ( σ : 0.09°C; range: 0.02°–0.29°C) of warming to the TCR, corresponding to 6.1% of the full TCR ( σ : 3.8%; range: 1.4%–13.9%). Moreover, variation in the physiological contribution to the TCR across models contributes disproportionately more to the intermodel spread of TCR estimates than it does to the mean. The largest contribution of plant physiology to CO 2 -forced warming—and the intermodel spread in warming—occurs over land, especially in forested regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Applied transonics at Grumman

A review of several applications of Computational Fluid Dynamics (CFD) to various aspects of aerodynamic design recently carried out at Grumman is presented. The emphasis is placed on project-oriented applications where the ease of use of the methods and short start-to-completion times are required. Applications cover transonic wing design/optimization, wing mounted stores load prediction, transonic buffet alleviation, fuselage loads estimation, and compact offset diffuser design for advanced aircraft configurations. Computational methods employed include extended transonic small disturbance (automatic grid embedding) formulation for analysis/design/optimization and a thin layer Navier-Stokes formulation for both external and internal flow analyses.

Davis, W. H.↗
NASA NTRS1993

High performance flight computer developed for deep space applications

The development of an advanced space flight computer for real time embedded deep space applications which embodies the lessons learned on Galileo and modern computer technology is described. The requirements are listed and the design implementation that meets those requirements is described. The development of SPACE-16 (Spaceborne Advanced Computing Engine) (where 16 designates the databus width) was initiated to support the MM2 (Marine Mark 2) project. The computer is based on a radiation hardened emulation of a modern 32 bit microprocessor and its family of support devices including a high performance floating point accelerator. Additional custom devices which include a coprocessor to improve input/output capabilities, a memory interface chip, and an additional support chip that provide management of all fault tolerant features, are described. Detailed supporting analyses and rationale which justifies specific design and architectural decisions are provided. The six chip types were designed and fabricated. Testing and evaluation of a brass/board was initiated.

Bunker, Robert L.↗
NASA NTRS2008

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

An experimental and numerical investigation into the static and dynamic responses of shape memory alloy hybrid composite (SMAHC) beams is performed to provide quantitative validation of a recently commercialized numerical analysis/design tool for SMAHC structures. The SMAHC beam specimens consist of a composite matrix with embedded pre-strained SMA actuators, which act against the mechanical boundaries of the structure when thermally activated to adaptively stiffen the structure. Numerical results are produced from the numerical model as implemented into the commercial finite element code ABAQUS. A rigorous experimental investigation is undertaken to acquire high fidelity measurements including infrared thermography and projection moire interferometry for full-field temperature and displacement measurements, respectively. High fidelity numerical results are also obtained from the numerical model and include measured parameters, such as geometric imperfection and thermal load. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Davis, Brian↗