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At least 451 records · Page 25

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NASA NTRS1991

Dynamic control modification techniques in teleoperation of a flexible manipulator

The objective of this research is to reduce the end-point vibration of a large, teleoperated manipulator while preserving the usefulness of the system motion. A master arm is designed to measure desired joint angles as the user specifies a desired tip motion. The desired joint angles from the master arm are the inputs to an adaptive PD control algorithm that positions the end-point of the manipulator. As the user moves the tip of the master, the robot will vibrate at its natural frequencies which makes it difficult to position the end-point. To eliminate the tip vibration during teleoperated motions, an input shaping method is presented. The input shaping method transforms each sample of the desired input into a new set of impulses that do not excite the system resonances. The method is explained using the equation of motion for a simple, second-order system. The impulse response of such a system is derived and the constraint equations for vibrationless motion are presented. To evaluate the robustness of the method, a different residual vibration equation from Singer's is derived that more accurately represents the input shaping technique. The input shaping method is shown to actually increase the residual vibration in certain situations when the system parameters are not accurately specified. Finally, the implementation of the input shaping method to a system with varying parameters is shown to induce a vibration into the system. To eliminate this vibration, a modified command shaping technique is developed. The ability of the modified command shaping method to reduce vibration at the system resonances is tested by varying input perturbations to trajectories in a range of possible user inputs. By comparing the frequency responses of the transverse acceleration at the end-point of the manipulator, the modified method is compared to the original PD routine. The control scheme that produces the smaller magnitude of resonant vibration at the first natural frequency is considered the more effective control method.

Magee, David Patrick↗

DOE OSTI2025

Interlayer Design for Halide Electrolytes in All‐Solid‐State Lithium Metal Batteries

Abstract All‐solid‐state lithium‐metal batteries (ASSLMBs) are promising for transportation electrification due to their superior safety and high energy density. Lithium halide electrolytes provide excellent processing flexibility, high ionic conductivity, and anodic stability (>4.1 V), making them highly compatible with high‐voltage cathodes, surpassing sulfide electrolytes (<2.1 V). Nevertheless, halide electrolytes suffer from low cathodic stability and form an electronically conductive interphase with lithium, resulting in a critical current density (CCD) of nearly zero. Herein, Li 3 YbCl 6 electrolytes are synthesized that are kinetically stable with lithium by forming an electronic insulating solid electrolyte interphase. Guided by critical overpotential criteria, a PI 3 interlayer is designed that transforms into Li 6 PI 3 upon contact with lithium, substantially reducing the interfacial resistance of Li 3 YbCl 6 against lithium to 34 Ω and achieving a high critical overpotential of 114 mV. By substituting Yb with Lu, Li 3 LuCl 6 electrolytes with Li 6 PI 3 interlayers reach a CCD of 1.0 mA cm −2 at a capacity of 1.0 mAh cm −2 , comparable to sulfide electrolytes but with higher oxidation stability. Additionally, Li 6 PI 3 enables stable cycling of Li//Li cells with Li 3 LuCl 6 electrolytes at 0.5 mA cm −2 for 400 cycles and maintains 86.5% capacity in Li//LiCoO 2 cells after 220 cycles at 30 °C, paving the way for high‐performance ASSLMBs.

Chemistry↗

DOE OSTI2021

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Revisiting the pseudo-supercritical path method: An improved formulation for the alchemical calculation of solid–liquid coexistence

Alchemical free energy calculations via molecular dynamics have been applied to obtain thermodynamic properties related to solid–liquid equilibrium conditions, such as melting points. In recent years, the pseudo-supercritical path (PSCP) method has proved to be an important approach to melting point prediction due to its flexibility and applicability. In the present work, we propose improvements to the PSCP alchemical cycle to make it more compact and efficient through a concerted evaluation of different potential energies. The multistate Bennett acceptance ratio (MBAR) estimator was applied at all stages of the new cycle to provide greater accuracy and uniformity, which is essential concerning uncertainty calculations. In particular, for the multistate expansion stage from solid to liquid, we employed the MBAR estimator with a reduced energy function that allows affine transformations of coordinates. Free energy and mean derivative profiles were calculated at different cycle stages for argon, triazole, propenal, and the ionic liquid 1-ethyl-3-methyl-imidazolium hexafluorophosphate. Comparisons showed a better performance of the proposed method than the original PSCP cycle for systems with higher complexity, especially the ionic liquid. Furthermore, a detailed study of the expansion stage revealed that remapping the centers of mass of the molecules or ions is preferable to remapping the coordinates of each atom, yielding better overlap between adjacent states and improving the accuracy of the methodology.

25 ENERGY STORAGE↗

DOE OSTI2023

Impact-Driven Sampling Strategies for Hybrid Attack Graphs

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graph (HAG) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. We propose a dimension reduction via property-preserving multi-layer graph sampling algorithms. Existing property-preserving graph sampling approaches generate a representative subgraph of an original large-sized graph while preserving the key properties, such as node and edge distribution, clustering coefficients, and betweenness. On the other hand, we propose impact-driven sampling strategies to transform the input data to a lower-dimensional representation while retaining key properties of the data.

Subasi, Omer↗

NASA NTRS1994

Parameter estimation of the vibrational model for the SCOLE experimental facility

The objective of this study is to experimentally determine an empirical model of the vibrational dynamics of the Spacecraft COntrol Laboratory Experiment (SCOLE) facility. The first two flexible modes of this test article are identified using a linear least-square identification procedure and the data utilized for this procedure are obtained by exciting the structure from a quiescent state with torque wheels. The time history data of rate gyro sensors and accelerometers due to excitation and after excitation in terms of free-decay are used in the parameter estimation of the vibrational model. The free-decay portion of the data is analyzed using the Discrete Fourier transform to determine the optimal model order to use in modelling the response. Linear least-square analysis is then used to select the parameters that best fit the output of an Autoregressive (AR) model to the data. The control effectiveness of the torque wheels is then determined using the excitation portion of the test data, again using linear least squares.

Crotts, B. D.↗

DOE OSTI2025

PSCF+: An Extended and Improved Open-Source Software Package for Polymer Self-Consistent Field Calculations

This work introduces PSCF+, a freely available open‑source software tool for modeling how block copolymers self‑assemble into ordered nanoscale structures. The software allows researchers to study a wide range of polymer models and interaction types, making it possible to directly compare theoretical predictions with molecular and simulation‑based studies in a consistent way. PSCF+ incorporates several recently developed numerical methods that significantly reduce computer memory requirements and speed up calculations, enabling efficient use of modern GPU hardware. Here, it also provides automated tools to calculate free‑energy landscapes and phase boundaries, which are essential for understanding and predicting material behavior. Together, these advances make PSCF+ a powerful and flexible platform for studying polymer self‑assembly and support the design of new polymeric materials for scientific and technological applications.

Chemical calculations↗

DOE OSTI2023

Special Section on Local and Distributed Electricity Markets

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

distributed energy resources↗

NASA NTRS2003

Structured Uncertainty Bound Determination From Data for Control and Performance Validation

This report attempts to document the broad scope of issues that must be satisfactorily resolved before one can expect to methodically obtain, with a reasonable confidence, a near-optimal robust closed loop performance in physical applications. These include elements of signal processing, noise identification, system identification, model validation, and uncertainty modeling. Based on a recently developed methodology involving a parameterization of all model validating uncertainty sets for a given linear fractional transformation (LFT) structure and noise allowance, a new software, Uncertainty Bound Identification (UBID) toolbox, which conveniently executes model validation tests and determine uncertainty bounds from data, has been designed and is currently available. This toolbox also serves to benchmark the current state-of-the-art in uncertainty bound determination and in turn facilitate benchmarking of robust control technology. To help clarify the methodology and use of the new software, two tutorial examples are provided. The first involves the uncertainty characterization of a flexible structure dynamics, and the second example involves a closed loop performance validation of a ducted fan based on an uncertainty bound from data. These examples, along with other simulation and experimental results, also help describe the many factors and assumptions that determine the degree of success in applying robust control theory to practical problems.

Lim, Kyong B.↗

NASA NTRS2004

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗

NASA NTRS2005

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗

NASA NTRSDate not supplied

NASA's Progress Toward Commercial Space Communications — SATCOM Demonstrations and Wideband Multilingual Terminal Development

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation (SCaN) Program is continuing to forge a path toward programmatic and operational transformation enabling commercialization of communications and navigation services to the greatest extent possible for near Earth users. U.S. National Space Policy is driving government agencies to incorporate commercial offerings and services to the maximum extent possible. As such, NASA has no plans to build or deploy additional Tracking and Data Relay Satellites (TDRS), but will seek to fill NASA mission space-relay capability needs using commercial satellite communications (SATCOM) providers and services. The goal is to support an approach that is both flexible to commercial service constructs and provides continuity of support with current assets as long as required. Progress toward commercial services is advancing in two key areas: (1) awards have been made for demonstrations of SATCOM services, and (2) development of wideband and multilingual user terminals has advanced to the flight demonstration phase. The Communications Services Project (CSP) at Glenn Research Center (GRC), has awarded Funded Space Act Agreements (FSAA) to six industry vendors. Inmarsat Government Inc., Kuiper Government Solutions (KGS) LLC, SES Government Solutions, Space Exploration Technologies, Telesat U.S. Services LLC, and Viasat Incorporated will receive a combined $278.5 million to complete technology development and in-space demonstrations over the next several years. The end-to-end service capabilities being targeted are based on existing NASA mission operational needs. Accordingly, each company has proposed a technical approach to lower costs, increase flexibility, and improve performance for a broad range of missions. In the current market, industry vendors operate at a range of different frequencies and use variable, sometimes proprietary, coding and modulations schemes. As NASA strives for a network comprised of services provided by multiple distinct suppliers, interoperability is a fitting solution to unify the network. Wideband and multi-lingual user terminals are being developed to bridge differences in industry services. Building on ground demonstrations completed in 2021, the Johns Hopkins Applied Physics Lab (APL) will be flight testing a multi-lingual wideband terminal (payload) and demonstrating connectivity to both government and commercial relay services. This paper provides a discussion of the progress toward NASA’s commercial space communications goals and consideration of key challenges and next steps.

commercialization↗

NASA NTRS1994

Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm

The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.

Shapiro, Jerome M.↗

DOE OSTI2020

Laboratory Directed Research and Development Program (FY 2019 Annual Summary of Completed Projects)

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory with distinctive capabilities in neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission to “ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) program. Information about the laboratory and its programs is available at the ORNL website. The Laboratory Directed Research and Development (LDRD) program at ORNL operates under the authority of the DOE Order 413.2C, “Laboratory Directed Research and Development,” which establishes DOE’s requirements for the program while providing the laboratory director broad flexibility for program implementation. The LDRD program funds are obtained through a charge to all laboratory programs. ORNL reports on the program status to DOE each year.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Laboratory Directed Research and Development Program: FY 2025 Completed Projects

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory. It possesses distinctive capabilities in a variety of fields, such as neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission “to ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to the specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) Program. Information about the laboratory and its programs is available on the ORNL website. The Laboratory Directed Research and Development (LDRD) Program at ORNL operates under the authority of DOE Order 413.2C, Laboratory Directed Research and Development, which establishes DOE’s requirements for the program while providing the laboratory director broad flexibility for program implementation. The LDRD Program funds are obtained through a charge to all laboratory programs. Although it represents a relatively small portion of the overall research budget, the LDRD Program plays an essential role in maintaining the laboratory’s ability to respond to national needs. The program allows ORNL to improve its distinctive capabilities and enhance its ability to conduct cutting-edge R&D. In accordance with the DOE order, R&D projects funded through the LDRD Program at ORNL support the goals of • maintaining the scientific and technical vitality of the laboratory, • enhancing the laboratory’s ability to address future DOE missions, • fostering creativity and stimulating exploration of forefront areas of science and technology, • serving as a proving ground for new concepts in R&D, and • supporting high-risk, potentially high-value R&D. This report provides an overview of the LDRD Program at ORNL in FY 2025 and contains summaries of all the LDRD research projects that concluded between October 1, 2024, and September 30, 2025.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2013

SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community.We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures.In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction.

Pseudospectral (PS)↗

DOE OSTI2025

Comparing Control Performance Between Simulation and Experiment using the Microreactor Automated Control System Testbed

In the advanced reactor domain, a flexible and scalable software/hardware infrastructure is crucial for integrating and validating various control technologies. This study used the Microreactor Automated Control System (MACS) hardware platform as a testbed. MACS was originally designed to mirror Idaho National Laboratory (INL)'s Microreactor Applications Research Validation and Evaluation (MARVEL), a 85-kW thermal fission microreactor. It features control drums for simulated reactivity control; lights that function as a surrogate reactor core, with the brightness being proportional to the reactor power; and light sensors that emulate neutron detectors. To transform MACS into a physical twin of MARVEL for evaluating control methods, the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software was employed. This software integrated the hardware with two models of the MARVEL core, based on Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The study aimed to demonstrate the gap between control theory and actual practice—a gap that often necessitates empirical adjustments such as control gain retuning, filters, time discretization, and integrator anti-windup measures. Controllers were developed based on increasingly complex simulations without hardware, starting from the base MARVEL model and then introducing actuator saturation constraints and sensor noise. The final control strategy was then tested using MACS, and a comparative performance analysis was conducted.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2026

Understanding Depolymerization and Repolymerization Toward Repurposing Polymer Waste Into Valuable Chemicals & Materials

The versatility of synthetic polymers has led to their continual and escalating production that has accompanied mismanagement at their end-of-life. Exploring and understanding complementary avenues to repurpose plastic waste beyond traditional mechanical recycling can unlock new opportunities in providing feedstock flexibility, securing supply chains, recovering valuable materials, and enabling new valorization paths. Chemical recycling allows for the return to monomers, tailored oligomers, and polymers, even from mixed states of post-consumer waste plastics. This review article summarizes our research team's efforts on elucidating key insights surrounding plastic depolymerization and repolymerization into valorized products. We revealed organocatalyst design rules lead to highly effective and selective deconstruction of condensation polymers. The intricacies of tailoring the reaction environment to produce products of specific lengths and desired functionalities transform low-value waste feedstocks into high-performance materials with embedded circularity. Other types of polymers, including polyolefins and polyakenamers, have also been designed and converted into valuable products. There remain opportunities for further developments such as low-energy and precision depolymerization, adoption of cutting-edge small molecule transformation to access functionalized polymer scaffolds typically inaccessible otherwise, as well as precision design and understanding through the aid of advanced tools.

Galan, Nick [ORNL]↗