Search NASA⌕ Search

SEARCH · Search NASA

Results for “Complex Framework Materials”

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

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

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Designing Thermomechanically Stable Nanocrystalline Alloys for Low Friction and Wear: A Perspective

In this Perspective, we discuss recent work and future trends on the usage of thermomechanically stable and selflubricating nanocrystalline (NC) alloys for low friction and wear applications. The theoretical underpinnings and mechanistic framework for attaining and maintaining low friction and wear in metal contacts are presented. Initial work on stress, time, and temperature-dependent changes in friction and wear in NC alloys validates the models, demonstrating lower friction from decreased interfacial strength, contact pressure, sliding speed, and temperature and increased roughness and material hardness. Subsequent work reveals that NC Pt−Au, metal nitrides, complex-concentrated, and high-entropy alloys come with additional stabilization factors such as low-friction carbon tribofilms and oxide/amorphous−crystalline layers but are still susceptible to destabilization factors including irradiation and adverse environmental conditions.

alloys↗
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
NASA NTRSDate not supplied

Ab initio ground states of strongly-correlated materials on quantum computers

The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving accurate many-body Hamiltonians including strong correlations, representing a subspace of interest of a material, using density functional theory calculations as a starting point. However, the solution of these material-specific models can scale exponentially on classical computers, constituting a challenge. Here we propose that utilizing quantum computers for obtaining the properties of downfolded Hamiltonians yields an accurate description of the ground state properties of strongly-correlated systems, while circumventing the exponential scaling problem. We benchmark the solution of Hubbard-like models obtained through downfolding by utilizing a classical tensor network implementation of variational quantum eigensolvers (VQE), and we reveal a strategy for driving the optimization through a hybrid minimization of the energy and maximization of the overlap with an approximate solution obtained through low-cost computational methods. This results in a reduction of the energy error by orders of magnitude compared to conventional VQE approaches, and allows us to reproduce long-range correlations for the first time. We demonstrate our first-principles approach for diverse strongly-correlated materials, correctly predicting the antiferromagnetic state of one-dimensional cuprate Ca 2 CuO 3 , the excitonic ground state of monolayer WTe2, and the charge-ordered state of correlated metal SrVO 3 . Our efficient computational implementation allows us to simulate large systems with up to 54 qubits and encompassing up to four correlated bands, which is indicative of the complexity that our framework can address.

Antonios M Alvertis↗
DOE OSTI2018

Computational Homogenization of Polycrystals

This paper reviews the current state of the art in the simulation of the mechanical behavior of polycrystalline materials by means of computational homogenization. The key ingredients of this modeling strategy are presented in detail starting with the parameters needed to describe polycrystalline microstructures and the digital representation of such microstructures in a suitable format to perform computational homogenization. The different crystal plasticity frameworks that can describe the physical mechanisms of deformation in single crystals (dislocation slip and twinning) at the microscopic level are presented next. This is followed by the description of computational homogenization methods based on mean-field approximations by means of the viscoplastic self-consistent approach, or on the full-field simulation of the mechanical response of a representative polycrystalline volume element by means of the finite element method or the fast Fourier transform-based method. Multiscale frameworks based on the combination of mean-field homogenization and the finite element method are presented next to model the plastic deformation of polycrystalline specimens of arbitrary geometry under complex mechanical loading. Examples of application to predict the strength, fatigue life, damage, and texture evolution under different conditions are presented to illustrate the capabilities of the different models. Lastly, current challenges and future research directions in this field are summarized.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Trailblazing Kr/Xe Separation: The Birth of the First Kr-Selective Material

Efficient separation of Kr from Kr/Xe mixtures is pivotal in nuclear waste management and dark matter research. Thus far, scientists have encountered a formidable challenge: the absence of a material with the ability to selectively adsorb Kr over Xe at room temperature. This study presents a groundbreaking transformation of the renowned metal–organic framework (MOF) CuBTC, previously acknowledged for its Xe adsorption affinity, into an unparalleled Kr-selective adsorbent. This achievement stems from an innovative densification approach involving systematic compression of the MOF, where the crystal size, interparticle interaction, defects, and evacuation conditions are synergistically modulated. The resultant densified CuBTC phase exhibits exceptional mechanical resilience, radiation tolerance, and notably an unprecedented selectivity for Kr over Xe at room temperature. Simulation and experimental kinetic diffusion studies confirm reduced gas diffusion in the densified MOF, attributed to its small pore window and minimal interparticle voids. The lighter Kr element demonstrates facile surface passage and higher diffusivity within the material, while the heavier Xe encounters increased difficulty entering the material and lower diffusivity. This Kr-selective MOF not only represents a significant breakthrough in Kr separation but also demonstrates remarkable processability and scalability to kilogram levels. Furthermore, the findings presented herein underscore the transformative potential of engineered MOFs in addressing complex challenges, heralding a new era of Kr separation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning

Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence–driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies.

Talluri, Y. Nissi [Indian Inst. of Technology (IIT↗
DOE OSTI2016

Design and syntheses of hybrid metal–organic materials based on K{sub 3}[M(C{sub 2}O{sub 4}){sub 3}]·3H{sub 2}O [M(III)=Fe, Al, Cr] metallotectons

By using K{sub 3}[M(C{sub 2}O{sub 4}){sub 3}]·3H{sub 2}O [M(III)=Fe, Al, Cr] (C{sub 2}O{sub 4}{sup 2−}=oxalate) metallotectons as the starting material, we have synthesized eight novel complexes with formulas [{Fe(C_2O_4)_2(H_2O)_2}{sub 2}]·(H–L{sub 1}){sub 2}·H{sub 2}O 1, [Fe(C{sub 2}O{sub 4})Cl{sub 2}]·(H{sub 2}–L{sub 2}){sub 0.5}·(L{sub 2}){sub 0.5}·H{sub 2}O 2, [{Fe(C_2O_4)_1_._5Cl_2}{sub 2}]·(H–L{sub 3}){sub 4}3, [Fe{sub 2}(C{sub 2}O{sub 4})Cl{sub 8}]·(H{sub 2}–L{sub 4}){sub 2}·2H{sub 2}O 4, K[Al(C{sub 2}O{sub 4}){sub 3}]·(H{sub 2}–L{sub 5})·2H{sub 2}O 5, K[Al(C{sub 2}O{sub 4}){sub 3}]·(H–L{sub 6}){sub 2}·2H{sub 2}O 6, K[Cr(C{sub 2}O{sub 4}){sub 3}]·2H{sub 2}O 7, Na[Fe(C{sub 2}O{sub 4}){sub 3}]·(H–L{sub 6}){sub 2}·2H{sub 2}O 8 (with L{sub 1}=4-dimethylaminopyridine, L{sub 2}=2,3,5,6-tetramethylpyrazine, L{sub 3}=2-aminobenzimidazole, L{sub 4}=1,4-bis-(1H-imidazol-1-yl)benzene, L{sub 5}=1,4-bis((2-methylimidazol-1-yl)methyl)benzene, L{sub 6}=2-methylbenzimidazole). Their structures have been determined by single-crystal X-ray diffraction analyses, elemental analyses, IR spectra and thermogravimetric analyses. Compound 3 is a 2D H-bonded supramolecular architecture. Others are 3D supramolecular structures. Compound 1 shows a [Fe(C{sub 2}O{sub 4}){sub 2}(H{sub 2}O){sub 2}]{sup −} unit and 3D antionic H-bonded framework. Compound 2 features a [Fe(C{sub 2}O{sub 4})Cl{sub 2}]{sup -} anion and 1D iron-oxalate-iron chain. Compound 3 features a [Fe{sub 2}(C{sub 2}O{sub 4}){sub 3}Cl{sub 4}]{sup 4−} unit. Compound 4 features distinct [Fe{sub 2}(C{sub 2}O{sub 4})Cl{sub 8}]{sup 4−} units, which are mutual linked by water molecules to generated a 2D H-bonded network. Compound 5 features infinite ladder-like chains constructed by [Al(C{sub 2}O{sub 4}){sub 3}]{sup 3−} units and K{sup +} cations. The 1D chains are further extended into 3D antionic H-bonded framework through O–H···O H-bonds. Compounds 6–8 show 2D [KAl(C{sub 2}O{sub 4}){sub 3}]{sup 2−} layer, [KCr(C{sub 2}O{sub 4}){sub 3}]{sup 2−} layer and [NaFe(C{sub 2}O{sub 4}){sub 3}]{sup 2−} layer, respectively. - Graphical abstract: We report here eight novel complexes by using [M(C{sub 2}O{sub 4}){sub 3}]{sup 3−} [M(III)=Fe, Al, Cr] metallotectons as the starting materials. These complexes show supramolecular architectures bonded by charge-assisted hydrogen bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures

Multifunctional porous materials are increasingly needed across various fields, but their complex microstructures create significant challenges due to the intricate microstructure-property relationships. This complexity, combined with limitations of traditional analysis methods, hinders efforts to understand and optimize microstructure–property relationships. Here, to address this, we integrate physics-based mesoscale modeling with interpretable machine learning (ML) to uncover how microstructural features govern effective diffusivity and elastic modulus. At constant porosity, we show diffusivity varies by over 150 × and modulus by ∼50 ×, highlighting the power of microstructure engineering. Statistical analysis reveals bimodal behavior in diffusivity and unimodal in modulus. ML identifies connectivity as the dominant factor, while modulus is also sensitive to domain size and feature interactions. Controlled simulations further highlight domain shape as a critical feature for modulus. This framework enables efficient exploration of microstructure-property correlations, offering new insights to guide the design of advanced porous materials.

Bicontinuous microstructure↗
DOE OSTI2025

Finite element modeling of electropolishing of radio frequency dipole Nb crab cavity in hydrofluoric-sulfuric acid electrolyte

The superior performance of niobium superconducting radio frequency (SRF) cavities is achieved through meticulous surface treatment, notably via chemical electropolishing, ensuring exceptionally smooth surfaces. While this technique has been extensively employed for cylindrically symmetric structures, addressing more intricate geometries poses significant challenges in achieving uniform polishing and controlled material removal, especially when moving away from retractable cathodes. This presents a multifaceted electrochemical, thermal, and fluid dynamics conundrum. A prime example is the 197 MHz radio frequency dipole (RFD) crabbing cavity proposed for the Electron Ion Collider (EIC) project, exemplifying such complex structures. Our groundbreaking work integrates the localized oxide thickness variation, considering its impact on current distribution and Joule heating, within the framework of multi-physics modeling using the COMSOL® simulation suite. This approach was applied to a comprehensive model of the RFD cavity, allowing us to investigate optimal external cooling water flow conditions necessary for achieving desirable outcomes. In conclusion, this illustrates the increasing utility of such multi-physics codes to greatly aid in designing solutions to challenging processing requirements for increasingly complex accelerator cavities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Representing local atomic environment using descriptors based on local correlations

Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process. To this end, we develop here a framework to generate descriptors based on many-body correlations that can effectively capture intrinsic geometric features of the local environment of an atom. These descriptors are based on the spectrum of two-body, three-body, four-body, and higher order correlations between an atom and its neighbors and are evaluated by calculating the corresponding two-body, three-body, and four-body overlap integrals. They are invariant to global translation, global rotation, reflection, and permutations of atomic indices. By systematically testing the ability to capture the local atomic environment, it is shown that the local correlation descriptors are able to successfully reconstruct structures containing 10-25 atoms which was previously not possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2006

Numerical Evaluation of Mode 1 Stress Intensity Factor as a Function of Material Orientation For BX-265 Foam Insulation Material

Foam; a cellular material, is found all around us. Bone and cork are examples of biological cell materials. Many forms of man-made foam have found practical applications as insulating materials. NASA uses the BX-265 foam insulation material on the external tank (ET) for the Space Shuttle. This is a type of Spray-on Foam Insulation (SOFI), similar to the material used to insulate attics in residential construction. This foam material is a good insulator and is very lightweight, making it suitable for space applications. Breakup of segments of this foam insulation on the shuttle ET impacting the shuttle thermal protection tiles during liftoff is believed to have caused the space shuttle Columbia failure during re-entry. NASA engineers are very interested in understanding the processes that govern the breakup/fracture of this complex material from the shuttle ET. The foam is anisotropic in nature and the required stress and fracture mechanics analysis must include the effects of the direction dependence on material properties. Material testing at NASA MSFC has indicated that the foam can be modeled as a transversely isotropic material. As a first step toward understanding the fracture mechanics of this material, we present a general theoretical and numerical framework for computing stress intensity factors (SIFs), under mixed-mode loading conditions, taking into account the material anisotropy. We present mode I SIFs for middle tension - M(T) - test specimens, using 3D finite element stress analysis (ANSYS) and FRANC3D fracture analysis software, developed by the Cornel1 Fracture Group. Mode I SIF values are presented for a range of foam material orientations. Also, NASA has recorded the failure load for various M(T) specimens. For a linear analysis, the mode I SIF will scale with the far-field load. This allows us to numerically estimate the mode I fracture toughness for this material. The results represent a quantitative basis for evaluating the strength and fracture properties of anisotropic foam insulation material.

Knudsen, Erik↗
DOE OSTI2013

Virtual Model Validation of Complex Multiscale Systems: Applications to Nonlinear Elastostatics

We propose a virtual statistical validation process as an aid to the design of experiments for the validation of phenomenological models of the behavior of material bodies, with focus on those cases in which knowledge of the fabrication process used to manufacture the body can provide information on the micro-molecular-scale properties underlying macroscale behavior. One example is given by models of elastomeric solids fabricated using polymerization processes. We describe a framework for model validation that involves Bayesian updates of parameters in statistical calibration and validation phases. The process enables the quanti cation of uncertainty in quantities of interest (QoIs) and the determination of model consistency using tools of statistical information theory. We assert that microscale information drawn from molecular models of the fabrication of the body provides a valuable source of prior information on parameters as well as a means for estimating model bias and designing virtual validation experiments to provide information gain over calibration posteriors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A New Complex Borohydride LiAl(BH4)2Cl2

A new mixed alkali metal–aluminum borohydride LiAl(BH4)2Cl2 has been prepared via mechanochemical synthesis from the 2LiBH4–AlCl3 mixture. Structural characterization, performed using a combination of X-ray powder diffraction and solid-state NMR methods, indicates that the LiAl(BH4)2Cl2 phase adopts a unique 3D framework and crystallizes in an orthorhombic structure with the space group C2221, a = 11.6709(6) Å, b = 8.4718(4) Å, c = 7.5114(3) Å. The material shows excellent dehydrogenation characteristics, where hydrogen evolution starts at Tons = 70 °C, releasing approximately 2 wt.% of nearly pure (99.8 vol.%) hydrogen and a very small amount (~0.2 vol.%) of diborane. When compared to halide-free mixed alkali metal–aluminum borohydrides, the presence of Al‒Cl bonding in the LiAl(BH4)2Cl2 structure likely prevents the formation of Al(BH4)3 upon decomposition, thus suppressing the formation of diborane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Low-Temperature Pd/Zeolite Passive NO x Adsorbers: Structure, Performance, and Adsorption Chemistry

Pd/zeolite passive NOx adsorber (PNA) materials were prepared with solution ion-exchange between NH4/zeolites (Beta, ZSM-5 and SSZ-13) and PdCl2 solutions. The nature of Pd (dispersion, distribution and oxidation states) in these materials was characterized with Na+ ion-exchange, TEM imaging, CO titration with FTIR and in situ XPS. The NOx trapping and release properties were tested using feeds with different compositions. It is concluded that multiple Pd species coexist in these materials: atomically dispersed Pd in the cationic sites of zeolites, and PdO2 and PdO particles on the external surfaces. While Pd is largely atomically dispersed in ZSM-5, the small pore opening for SSZ-13 inhibits Pd diffusion such that the majority of Pd stays as external surface PdO2 clusters. NOx trapping and release are not simple chemisorption and desorption events, but involve rather complex chemical reactions. In the absence of CO in the feed, cationic Pd(II) sites with oxygen ligands and PdO2 clusters are reduced by NO to Pd(I) and PdO clusters. These reduced sites are the primary NO adsorption sites. In the presence of H2O, the as-formed NO2 desorb immediately. In the presence of CO in the feed, metallic Pd, “naked” Pd2+, and Pd+ sites are responsible for NO adsorption. For Pd adsorption sites with the same oxidation states but in different zeolite frameworks, NO binding energies are not expected to vary greatly. However, NO release temperatures do vary substantially with different zeolite structures. This indicates that NO transport within these materials play an important role in determining release temperatures. Finally, some rational design principles on efficient PNA materials are suggested. The authors gratefully acknowledge the US Department of Energy (DOE), Energy Efficiency and Renewable Energy, Vehicle Technologies Office for the support of this work. The research described in this paper was performed in the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the DOE’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is operated for the US DOE by Battelle. Discussions with Drs. A. Yezerets, J.Y. Luo, K. Kamasamudram and N. Currier Cummins, Inc., and H.Y. Chen from Johnson-Matthey are greatly appreciated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Potential for light-induced separation of critical rare-earth elements

Current methods for separations of critical rare earth elements (REEs) require multi-step, waste-generating procedures that lack the ability to selectively separate similarly sized ions, despite such an onerous process. REEs possess unique optoelectronic properties that are often exploited for photomagnetic or photoluminescent applications but could be harnessed to drive element selective separations. Here, recent work exploring photochemical reactions of REE complexes points to promise for investigating alternative separations using photoactive molecules and macromolecular frameworks, highlighting a possible pathway towards realizing practical REE separations to increase the sustainability and longevity of mining and recycling these elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Numerical and Experimental Investigations of Polyurethane Foam for Use as Cask Impact Limiter in Accidental Drop Scenarios - 12099

Rigid, closed-cell polyurethane foams are frequently used as cask impact limiters in nuclear materials and hazardous waste transport due to their high energy-absorption potential. When assessing the cask integrity in accidental scenarios based on numerical simulations, a description of the foam damping properties is required for different strain rates and for a wide temperature range with respect to waste heat generation in conjunction with critical operating and environmental conditions. Implementation and adaption of a respective finite element material model strongly relies on an appropriate experimental data base. Even though extensive impact experiments were conducted e.g. in Sandia National Laboratories, Savannah River National Laboratory and by Rolls Royce plc, not all relevant factors were taken into account. Hence, BAM who is in charge of the mechanical evaluation of such packages within the approval procedure in Germany, incorporated systematic test series into a comprehensive research project aimed to develop numerical methods for a couple of damping materials. In a first step, displacement driven compression tests have been performed on confined, cubic specimens at five loading rates ranging from 0.02 mm/s to 3 m/s at temperatures between +90 deg. C and -40 deg. C. Materials include two different polyurethane foam types called FR3718 and FR3730 having densities of 280 kg/m{sup 3} and 488 kg/m{sup 3} from the product line-up of General Plastics Manufacturing Company. Their data was used to adapt an advanced plasticity model allowing for reliably simulating cellular materials under multi-axial compression states. Therefore, an automated parameter identification procedure had been established by combining an artificial neural network with local optimization techniques. Currently, the selected numerical material input values are validated and optimized by means of more complex loading configurations with the prospect of establishing methods applicable to impact limiters under severe accidental conditions. The reference data base is provided by experiments, where weights between 212 kg and 1200 kg have been dropped from heights between 1.25 m and 7 m on confined 10 cm cubic foam specimens. By presenting the deviations between experimental values and the corresponding output of finite element simulations, the potentials and restrictions of the resulting models are highlighted. Systematic compression tests on polyurethane foams had been performed at BAM test site within the framework of a research project on impact limiters for handling casks for radioactive waste. The experimental results had been used to adapt numerical models for simulating the behaviour of different foam types at different temperatures. The loading speed, however, turned out to have a major influence on their flow curves that can not be captured by simple strain-rate dependent multipliers. Especially for guided drop tests that come close to real accidental scenarios there is a significant gap between experimental and numerical results even when applying such advanced material models. Hence, the extensive data base is currently deployed for expanding the standard algorithms to include adequate dynamic hardening factors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗