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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

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

Design and construction of diverse structures of coordination polymers: Photocatalytic properties

The reaction of Cu(NO{sub 3}){sub 2}·3H{sub 2}O/Ni(NO{sub 3}){sub 2}·6H{sub 2}O with 4′-(4-(3,5-dicarboxylphenoxy)phenyl)-4,2′:6′,4′′-terpyridine (H{sub 2}dbp) gave [Cu{sub 0.5}(Hdbp)]{sub n} (1) and [Ni(dbp)(H{sub 2}O)]{sub n} (2), while the reactions of Co(NO{sub 3}){sub 2}·6H{sub 2}O with H{sub 2}dbp in the presence of 4,4′-bipy and 2,2′-bpy generated [Co(dbp)(4,4′-bipy)]{sub n} (3) and ([Co(dbp)(2,2′-bipy)]{sub n}·H{sub 2}O) (4), respectively (4,4′-bipy=4.4′-pyridine and 2,2′-bipy=2,2′-bipyridine). X-Ray single-crystal analyses reveal that 1 contains a 1D double chain. 2 possesses a 3D architecture with (4.6{sup 2}0.8{sup 3}){sub 2} topology that is interpenetrated with each other to form a 2-fold network. In 3, the 2D [Co(dbp)]n sheets are pillared by 4,4′-bpy to form a 3D framework with 1D open channel. Compound 4 consists of a 1D ladder-like chain. The results showed that the structural diversity of the coordination polymers resulted from the different geometries of metal ions and effect of assistant ligands. Furthermore, the photocatalytic properties of 1–4 for degradation of the methyl violet (MV) have been examined. - Graphical abstract: The photocatalytic activity and selectivity of complexes 1–4 prove that they may be good and stable photocatalysts for degradation of organic dyes.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Dielectric relaxation of gamma irradiated muscovite mica

Highlights: • The present article reports the effect of gamma irradiation on the dielectric relaxation characteristics of muscovite mica. • Dielectric and electrical relaxations have been analyzed in the framework of dielectric permittivity, electric modulus and Cole–Cole formalisms. • The frequency dependent electrical conductivity has been rationalized using Johnsher’s universal power law. • The experimentally measured electric modulus and conductivity data have been fitted using Havriliak–Negami dielectric relaxation function. - Abstract: In the present research, the dielectric relaxation of gamma irradiated muscovite mica was studied in the frequency range of 0.1 Hz–10 MHz and temperature range of 653–853 K, using the dielectric permittivity, electric modulus and conductivity formalisms. The dielectric constants (ϵ′ and ϵ′′) are found to be high for gamma irradiated muscovite mica as compared to the pristine sample. The frequency dependence of the imaginary part of complex electric modulus (M′′) and dc conductivity data conforms Arrhenius law with single value of activation energy for pristine sample and two values of activation energy for gamma irradiated mica sample. The experimentally assessed electric modulus and conductivity information have been interpreted by the Havriliak–Negami dielectric relaxation explanation. Using the Cole–Cole framework, an analysis of real and imaginary characters of the electric modulus for pristine and gamma irradiated sample was executed which reflects the non-Debye relaxation mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep reinforcement learning for optimal control of induction welding process

Optimizing induction welding (IW) process parameters for the application of joining thermoplastic composites is challenging as it requires achieving complex spatiotemporal thermal characteristics along the weld-line to obtain desired weld quality. We formulate an optimal control problem which captures these requirements and seeks to optimize the IW coil speed using a fast-acting dynamic IW process model. We develop a novel Deep Reinforcement Learning (DRL) framework to solve this computationally challenging control problem and demonstrate via simulation study that the learned DRL feedback control policy results in better spatiotemporal thermal characteristics as compared to the current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Abstract Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Accounting for the Complex Surface Structure in Ellipsometric Studies of the Effects of Magnetron Sputtering Modes on the Growth and Optical Properties of In{sub 2}O{sub 3} Films

The efficiency of invoking additional information on optical transmission in solving the inverse problem of ellipsometry by a minimization method is demonstrated in practice for In{sub 2}O{sub 3} fi doped and nondoped with Sn on Al{sub 2}O{sub 3} (012) substrates. This approach allows the thickness and refractive index of thin films with rough surfaces to be uniquely determined. Solutions of the inverse problem in the framework of one-, two-, and multilayer models are compared. The last provides the best description of the experimental data and the correct parameters of the samples. The dependences of the investigated properties of films produced with different magnetron sputtering modes are found using the above methods and models and do not contradict general concepts about the film formation by this material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The developments in modifying functionality of lignin and its application in biocomposites

With the advancement of sustainable material innovations, renewable natural biopolymers are gradually replacing traditional metal and petroleum-based synthetic materials due to their environmental friendliness, biodegradability, and economic advantages. Lignin, the second most abundant natural aromatic polymer in the plant kingdom, has emerged as a key candidate raw material for the development of green polymer systems because of its unique phenylpropane unit network structure, high carbon content, and rich functional group characteristics. However, challenges such as the inherent structural complexity, chemical inertness, and uneven molecular weight distribution of lignin limit its direct application. By employing modification strategies such as chemical functionalization and physical regulation, researchers can precisely control its reactivity, thermal stability, and interfacial compatibility, enabling the preparation of high-performance lignin-based functional composites. Here, this paper systematically reviews the principles and methodological advancements in lignin's multi-dimensional modification technology. It analyzes the mechanisms by which various chemical and physical modification techniques enhance the mechanical properties, functional responsiveness, and environmental adaptability of materials, and discusses innovative applications in fields such as intelligent packaging, biomedical materials, and energy storage devices. Furthermore, this review addresses the key challenges encountered in the high-value transformation of lignin, with the aim of offering a theoretical framework and technical pathway for the transformative development of lignin from agricultural and forestry by-products to functional material platforms.

Functional composites↗
NASA NTRS2018

The Photochemistry of Purine in Ice Analogs Relevant to Dense Interstellar Clouds

The aromatic nitrogen heterocyclic compound purine is the core structural framework of many important biomolecules, particularly nucleobases. Purine and purine derivatives have been observed in carbonaceous chondrites, and it has been hypothesized that the exogenous delivery of these compounds, along with many other biologically relevant compounds, may have played a role in the emergence of life. Numerous experiments in our laboratory have demonstrated that the nucleobases used by life to encode genetic material could have been produced abiotically under astrophysically relevant conditions. Specically, the UV photoprocessing of pyrimidine and purine in simple ices of astrophysical interest has resulted in the production of all five biological nucleobases, namely, uracil (RNA), cytosine (RNA and DNA), thymine (DNA), adenine (RNA and DNA), and guanine (RNA and DNA). Additionally, follow-up work has examined the photochemistry of pyrimidine in more complex astrophysical ice mixtures to better understand the formation of these compounds under realistic conditions. In this work, we examine the photochemistry of purine in more complex ices of astrophysical interest and compare our results with those from simpler ice mixtures. We also examine the effects of competing parallel synthesis of organic compounds in the ices (unrelated to purine). Finally, we discuss the astrophysical and astrobiological implications of our findings.

Molecular Processes↗
NASA NTRS2018

The Photochemistry of Purine in Ice Analogs Relevant to Dense Interstellar Clouds

The aromatic nitrogen heterocyclic compound purine is the core structural framework of many important biomolecules, particularly nucleobases. Purine and purine derivatives have been observed in carbonaceous chondrites, and it has been hypothesized that the exogenous delivery of these compounds, along with many other biologically relevant compounds, may have played a role in the emergence of life. Numerous experiments in our laboratory have demonstrated that the nucleobases used by life to encode genetic material could have been produced abiotically under astrophysically relevant conditions. Specifically, the UV photoprocessing of pyrimidine and purine in simple ices of astrophysical interest has resulted in the production of all five biological nucleobases, namely, uracil (RNA), cytosine (RNA and DNA), thymine (DNA), adenine (RNA and DNA), and guanine (RNA and DNA). Additionally, follow-up work has examined the photochemistry of pyrimidine in more complex astrophysical ice mixtures to better understand the formation of these compounds under realistic conditions. In this work, we examine the photochemistry of purine in more complex ices of astrophysical interest and compare our results with those from simpler ice mixtures. We also examine the effects of competing parallel synthesis of organic compounds in the ices (unrelated to purine). Finally, we discuss the astrophysical and astrobiological implications of our findings.

Astrobiology↗
DOE OSTI2020

Excavation Volume Growth Factors at Environmental Remediation Sites - 20318

The U.S. Army Corps of Engineers (USACE) manages numerous environmental remediation projects in accordance with the Comprehensive Environmental Response, Compensation, and Liability Act, otherwise known as CERCLA. These include projects for the Formerly Utilized Sites Remedial Action Program (FUSRAP). Utilizing the CERCLA framework to address these projects requires detailed cost estimates and a significant portion of costs are associated with the volume of material to be excavated and that material's final disposition. Accordingly, understanding the expected volumes and uncertainty associated with those volumes is vital to project cost estimating. These estimates are completed at various stages of the CERCLA process, thus the project team has varying degrees of information available from which to develop these estimates depending on how far along the project is. Understanding the factors contributing to volume growth (increase in volume of material from that originally estimated) is a crucial element to determining volume and cost uncertainty. This paper presents a case study of a FUSRAP site which involves excavation and offsite disposal of contaminated soils from complex commercial, industrial, and residential properties. Original volume estimates were compared to post excavation volumes on a property by property basis. Reasons for differences are determined along with magnitude of impacts to cost estimating. For both residential and commercial property types, volume estimating factors, uncertainty considerations, and methods to account for each are discussed with emphasis on volume growth considerations. Cost estimating model input considerations are discussed with specific emphasis on accounting for volume uncertainty at the Feasibility Study (FS) phase of CERCLA. Underestimating or overestimating costs at the FS phase has repercussions under CERCLA that may impact project close out or completion schedule. Discussions presented will be helpful to both reviewers and preparers of cost estimates involving excavations. Additionally, project planners may be able to use the information to plan for volume growth contingencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Topology-Accelerated and Selective Cascade Depolymerization of Architecturally Complex Polyesters

Despite considerable recent advances already made in developing chemically circular polymers (CPs), the current framework predominantly focuses on CPs with linear-chain structures of different monomer types. As polymer properties are determined by not only composition but also topology, manipulating the topology of the single-monomer-based CP systems from linear-chain structures to architecturally complex polymers could potentially modulate the resulting polymer properties without changing the chemical composition, thereby advancing the concept of monomaterial product design. To that end, here, we introduce a chemically circular hyperbranched polyester (HBPE), synthesized by a mixed chain-growth and step-growth polymerization of a rationally designed bicyclic lactone with a pendent hydroxyl group (BiLOH). This HBPE exhibits full chemical recyclability despite its architectural complexity, showing quantitative selectivity for regeneration of BiLOH, via a unique cascade depolymerization mechanism. Moreover, distinct differences in materials properties and performance arising from topological variations between HBPE, hb-PBiLOH, and its linear analogue, l-PBiLOH, have been revealed where generally the branched structure led to more favorable interchain interactions, and topology-amplified optical activity has also been observed for chiral (1S, 4S, 5S)-hb-PBiLOH. More intriguingly, depolymerization of l-PBiLOH proceeds through an unexpected, initial topological transformation to the HBPE polymer, followed by the faster cascade depolymerization pathway adopted by hb-PBiLOH. Overall, these results demonstrate that CP design can go beyond typical linear polymers, and rationally redesigned, architecturally complex polymers for their unique properties may synergistically impart advantages in topology-augmented depolymerization acceleration and selectivity for exclusive monomer regeneration.

depolymerization↗
DOE OSTI2023

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Supported metallic nanoparticles play a central role in catalysis. However, predictive modeling is particularly challenging due to the structural and dynamic complexity of the nanoparticle and its interface with the support, given that the sizes of interest are often well beyond those accessible via traditional ab initio methods. With recent advances in machine learning, it is now feasible to perform MD simulations with potentials retaining near-density-functional theory (DFT) accuracy, which can elucidate the growth and relaxation of supported metal nanoparticles, as well as reactions on those catalysts, at temperatures and time scales approaching those relevant to experiments. Furthermore, the surfaces of the support materials can also be modeled realistically through simulated annealing to include effects such as defects and amorphous structures. We study the adsorption of fluorine atoms on ceria and silica supported palladium nanoparticles using machine learning potential trained by DFT data using the DeePMD framework. We show defects on ceria and Pd/ceria interfaces are crucial for the initial adsorption of fluorine, while the interplay between Pd and ceria and the reverse oxygen migration from ceria to Pd control spillover of fluorine from Pd to ceria at later stages. In contrast, silica supports do not induce fluorine spillover from Pd particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem-solution of the underlying mathematical model to yield output quantities of interest, given input parameters--is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision-making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal con figuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems--requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models--entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure. DiaMonD is a multi-institutional DOE MMICC effort that aims to address the challenges of end-to-end, data-to-decisions modeling and simulation for complex CS&E problems in a unifi ed, systematic, and integrated fashion. Institutions involved are Colorado State, Florida State, Los Alamos, MIT, Oak Ridge, Stanford, and UT-Austin. The goals of DiaMonD are (1) to develop advanced mathematical methods and analysis for multimodel, multiphysics, multiscale model problems driven by frontier DOE applications, including those in subsurface energy and environmental flows, materials for energy storage and conversion, and ice sheet dynamics; (2) to create theory and algorithms for integrated inversion, optimization, and uncertainty quantification for these complex problems; and (3) to disseminate the philosophy of an integrated end-to-end, data-to-decisions approach to modeling and simulation of complex problems to the broader applied math and computational science communities through workshops and other outreach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Predicting the ground-state structure of sodium boride

Binary borides has been a subject of extensive research. However, the exact compositions and crystal structures of sodium borides remained controversial. Furthermore, using the ab initio variable-composition evolutionary algorithm, a new stable Na 2 B 30 with I2 1 2 1 2 1 symmetry (I2 1 2 1 2 1 –Na 2 B 30 ) is found, which is –7.38 meV / atom lower in energy than the Imma–Na 2 B 30 structure reported by experimentalists. Interestingly, the Imma–Na 2 B 30 structure is predicted to be a topological nodal line semimetal, which may result in superior electronic transport. In contrast, I2 1 2 1 2 1 –Na 2 B 30 is an ultrahard semiconductor with an unprecedented open-framework structure, whose interstitial helical boron sublattice enhances its hardness and energetic stability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Tribological behavior of nuclear graphite in high-temperature inert environment

This report formally documents the completion of the Advanced Reactor Technologies Level 2 Milestone (M2AT-26OR0605058), “Complete initial wear/abrasion studies,” due June 1, 2026. The work presented in this report expend upon our previously published manuscript titled Sliding friction and wear behavior of nuclear graphite in high temperature inert environment: Influence of contact load, speed and temperature. The sliding friction and wear of self-mated ET-10 nuclear graphite were characterized across a range of elevated temperatures (650°C and 750°C), contact loads (20 N and 40 N), and sliding speeds (1 mm/s and 10 mm/s) within a controlled argon environment. Tribological data combined with advanced morphological characterization were used to provide a detailed mechanistic framework for the frictional and wear behavior of nuclear graphite in high-temperature inert conditions. Furthermore, the report evaluates the inherent limitations of bench-scale characterization and addresses the critical disparities between laboratory findings and the complex, in-service friction and wear behavior of fuel pebbles within a reactor environment.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Towards Cooperative Control of Humanoid Robots for Handling High-Consequence Materials in Gloveboxes - 17291

In this paper, we describe our ongoing work with NASA's Valkyrie humanoid robot and present preliminary results on cooperative control of Valkyrie's hands for performing dexterous manipulation tasks inside gloveboxes commonly found in many nuclear facilities. First, we discuss our human-supervised robot control framework which is based on optimization-based whole-body motion and manipulation controllers. A key aspect of this approach is perception based grasping. We develop a grasping control module for realizing the grasps corresponding to sample tasks such as power, ball, key, pinch, and open palm grasps and their variations. Next, we introduce the notion of motion template library in order to develop generalizable robot grasping and manipulation. Motion template library stores the constraint setting information for the constrained motion planning methods and a framework which can implement the motion template library on different types of robots for accomplishing similar tasks. Motion templates are defined only based on the task requirements. As a result, they can be easily ported to different robot platforms. Lastly, we discuss our multi-modal interfaces designs which include a visual display for improved situational awareness as well as a tactile glove for robot hand control and feedback. Our goal is for the person tele-operating the Valkyrie robot to have better sensing and control than what he or she would have if actually standing at the glovebox doing the work. This improved sensing is comprised of the ability to 'see through' the robot's arms and hands in the gloves and to have an improved sense of touch. The paper concludes with a discussion of our validation strategy. Our success metrics are derived from the completion of a relatively complex experiment to be performed in the glovebox. We evaluate various realistic experiments and use them for task validation and demonstration with Valkyrie. We use speed, time of completion, accuracy, repeatability and reliability rates as success metrics by designing replicable experiments. (authors)

42 ENGINEERING↗
DOE OSTI2024

LYNM PE1 Pre-Experiment A Site Characterization Report

Underground chemical explosive experiments such as LYNM PE1 generate large multiphenomenological datasets, require complex site preparation and build out, and utilize cutting edge models and analysis techniques to analyze and simulate the explosion-induced signals. This wide range of outcomes makes it a necessity to thoroughly characterize the testbed in advance of experiments in a way that complements the wide suite of data being generated. Here, we present a broad overview of the site characterization work and data collection that was conducted before Experiment A, which is the first in a series of three PE1 experiments. This work includes, but is not limited to, geologic mapping, physical sample collection, analysis of material properties, geophysical borehole logging, and in-situ measurements. This information was collected by a large, dedicated team and was used to inform site construction, finalize instrumentation placement, generate Geologic Framework Models, feed pre-experiment predictions, and facilitate post-experiment data analysis

58 GEOSCIENCES↗