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

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Development of a High-Efficiency Hot Gas Turbo-expander and Low-Cost Heat Exchangers for Optimized CSP Supercritical CO 2 Operation

Southwest Research Institute® (SwRI®) has developed a novel, high-efficiency, supercritical carbon dioxide (sCO 2 ), hot gas turbo-expander optimized for the highly-transient, solar-powerplant, duty-cycle profile. This MW-scale sCO 2 turbo-expander design advances the state-of-theart of sCO 2 turbo-expanders from a current Technology Readiness Level (TRL) of three (initial, small-scale, laboratory, size testing) to a full TRL6 (MW-scale prototype demonstration). A secondary objective of this project was to optimize novel compact heat exchangers for sCO 2 applications to drastically reduce their manufacturing costs. Significant learning was achieved on how to design and fabricate a more cost effective, high performance recuperator, at the MWt capacity. The sCO 2 turbo-expander and novel sCO 2 heat exchanger have been tested in a 1- MWe sCO 2 test loop, fabricated to demonstrate component performance and the performance of the optimized sCO 2 Brayton cycle over a wide range of part load conditions and during transient operations representative of a typical Concentrating Solar Power (CSP) duty cycle. The scalable sCO 2 expander design and improved heat exchanger address and close two critical technology gaps required for an optimized CSP sCO 2 power plant and provide a major stepping stone on the pathway to achieving CSP power at $\$ $0.06/kW-hr Levelized Cost of Electricity (LCOE), increasing energy conversion efficiency to greater than 50% and reducing total power block cost to below $\$ $1,200/kW installed. Construction of the facility and testing has been completed. Test targets of 1,320°F, 27,000 rpm, and 3,500 psi were reached during separate tests. Overall, the turbine saw 37.5 hours of operation with many other hours of testing and troubleshooting all of the loop components. All the test data and troubleshooting is useful and applicable for future tests and operations with not only this loop but in any sCO2 application.

14 SOLAR ENERGY↗
DOE OSTI2024

Autonomous hybrid optimization of a SiO 2 plasma etching mechanism

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO 2 using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗
DOE OSTI2026

A Pulsar-Inspired Timing Framework for Power System: Optimization and Performance Evaluation

Due to their excellent stability, neutron pulsar stars are considered promising candidate timing sources for power system applications. However, the complexity of pulsar signals necessitates advanced processing algorithms to provide accurate timing references. This paper presents the foundational framework for pulsar signal processing, serving as the basis for further optimization. To enhance the timing accuracy and computation efficiency in pulsar period searches, three algorithms are proposed as the initial optimization step: wavelet de-noising, fast folding, and cross-correlation for profile evaluation. Wavelet de-noising improves signal-to-noise ratio (SNR) by 36%–70%. Fast folding reduces computation time from hundreds of seconds to mere milliseconds. Cross-correlation works better than traditional SNR-based methods by effectively identifying the optimal period. The performance of the proposed algorithms is evaluated using observation data from telescopes. Together, these algorithms significantly improve pulsar timing performance, reducing the error of the Pulse Per Second (PPS) signal from hundreds to tens of microseconds.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2023

Orthogonal Gelations to Synthesize Core–Shell Hydrogels Loaded with Nanoemulsion‐Templated Drug Nanoparticles for Versatile Oral Drug Delivery

Hydrophobic active pharmaceutical ingredients (APIs) are ubiquitous in the drug development pipeline, but their poor bioavailability often prevents their translation into drug products. Industrial processes to formulate hydrophobic APIs are expensive, difficult to optimize, and not flexible enough to incorporate customizable drug release profiles into drug products. Here, a novel, dual-responsive gelation process that exploits orthogonal thermo-responsive and ion-responsive gelations is introduced. This one-step “dual gelation” synthesizes core–shell (methylcellulose-alginate) hydrogel particles and encapsulates drug-laden nanoemulsions in the hydrogel matrices. In situ crystallization templates drug nanocrystals inside the polymeric core, while a kinetically stable amorphous solid dispersion is templated in the shell. Drug release is explored as a function of particle geometry, and programmable release is demonstrated for various therapeutic applications including delayed pulsatile release and sequential release of a model fixed-dose combination drug product of ibuprofen and fenofibrate. Independent control over drug loading between the shell and the core is demonstrated. This formulation approach is shown to be a flexible process to develop drug products with biocompatible materials, facile synthesis, and precise drug release performance. This work suggests and applies a novel method to leverage orthogonal gel chemistries to generate functional core–shell hydrogel particles.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Current Status of Clinical Trials for Cervical and Uterine Cancer Using Immunotherapy Combined With Radiation

Novel therapies combined with radiation continue to be of significant interest in the developmental treatment paradigm of gynecologic cancers. Clinical implementation of immunotherapy in oncology has rapidly changed the treatment landscape, options, paradigm, and outcomes through clinical trials. Immunotherapy has emerged as a therapeutic pillar in the treatment of solid tumors with demonstrable synergistic activity when combined with radiation therapy and chemoradiotherapy by an alteration or enhancement of the immune system. In solid tumors, radiation therapy induces migration of dendritic cells, T cell activation, and proliferation, and increases in tumor-infiltrating lymphocytes. These immunomodulatory effects in conjunction with immune checkpoint blockade are currently under active investigation in the adjuvant, definitive, and metastatic settings. Results from early phase trials demonstrate promising efficacy and overall tolerable toxicity profiles of combined modality treatment. There is significant interest in optimizing the treatment for patients with locally advanced cervical cancer beyond the standard of care—chemoradiation—which has been in place for the last 30 years. The majority of cervical cancer emerges after persistent infection with a high-risk subtype of the human papillomavirus, where viral oncoproteins lead to cellular changes and immortalization. As a result, immune tolerance can develop, resulting in cancer. Knowledge of the mechanism of human papillomavirus–related oncogenesis suggests that immune therapy or checkpoint blockade can reinvigorate an antitumor immune response. Current clinical trials are exploring the therapeutic potential of these approaches. Uterine cancers have been grouped into 4 molecular subclasses by their driver mutations, mutational burden, and copy-number alterations. Of these subgroups, the polymerase epsilon–mutated and microsatellite-unstable may represent up to 40% of endometrial cancers, and they have been shown to be immunogenic. Because of the inherent immunogenicity of these MSI-high tumors, combined immune modulation strategies, including chemotherapy, radiation, and immunotherapy and immune checkpoint inhibitor therapy, are being explored to improve treatment outcomes. In this review, we explore current immunomodulatory and multimodality therapeutic approaches in the treatment of cervical and uterine cancer through ongoing clinical trials investigating the combination of immunotherapy and radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Silicon-On-Sapphire Metasurfaces Generate Arrays of Dark and Bright Traps for Neutral Atoms

We demonstrated crystalline silicon-on-sapphire (c-SOS) metasurfaces that convert a Gaussian beam into arrays of complex optical traps, including arrays of optical bottle beams that trap atoms in dark regions interleaved with bright tweezer arrays. The high refractive index and indirect band gap of crystalline silicon make it possible to design high-resolution near-infrared (λ > 700 nm) metasurfaces that can be manufactured at scale using CMOS-compatible processes. Compared with active components like spatial light modulators (SLMs) that have become widely used to generate trap arrays, metasurfaces provide an indefinitely scalable number of pixels, enabling large arrays of complex traps in a very small form factor, as well as reduced dynamic noise. To design metasurfaces that can generate three-dimensional bottle beams to serve as dark traps, we modified the Gerchberg-Saxton algorithm to enforce complex-amplitude profiles at the focal plane of the metasurface and to optimize the uniformity of the traps across the array. We fabricated and measured c-SOS metasurfaces that convert a Gaussian laser beam into arrays of bright traps, dark traps, and interleaved bright/dark traps.

Gerchberg-Saxton↗
DOE OSTI2019

The addition of methanol to Criegee intermediates

Bimolecular reactions involving stabilized Criegee intermediates (SCI) have been the target of many studies due to the role these molecules play in atmospheric chemistry. Recently, kinetic rates for the addition reaction of the simplest SCI (formaldehyde oxide) and its methylated analogue (acetone oxide) with methanol were reported both experimentally and theoretically. We re-examine the energy profile of these reactions by employing rigorous ab initio methods. Optimized CCSD(T)/ANO1 geometries are reported for the stationary points along the reaction path. Energies are obtained at the CCSD(T)/CBS level of theory. Contributions of full triple and quadruple excitations are computed to assess the convergence of this method. Rate constants are obtained using conventional canonical transition state theory under the rigid rotor harmonic oscillator approximation and with the inclusion of a one-dimensional hindered rotor treatment. These corrections for internal rotations have a significant impact on computed kinetic rate constants. With this approach, we compute rate constants for the addition of methanol to formaldehyde oxide (H 2 COO) and acetone oxide [(CH 3 ) 2 COO] at 298.15 K as (1.2 ± 0.8) × 10 -13 and (2.8 ± 1.3) × 10 -15 cm 3 s -1 , respectively. Additionally, we investigate the temperature dependence of the rate constant, concluding that the transition state barrier height and tunneling contributions shape the qualitative behaviour of these reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

High-efficiency, large-aperture fifth-harmonic: Generation of 211-nm pulses in ammonium dihydrogen phosphate crystals for fusion diagnostics

High-energy ultraviolet (UV) sources are required to probe hot dense plasmas from fusion experiments by using Thomson scattering resulting from the lower self-generated background from the plasma in the 180 to 230-nm spectral region. Although recently we demonstrated efficient, joule-class fifth harmonic conversion of 1053-nm pulses using a cesium lithium borate (CLBO) crystal, larger crystals are necessary for increased UV energy. This paper presents an alternative approach for fifth-harmonic generation of a large-aperture neodymium laser using ammonium dihydrogen phosphate (ADP). An Nd:YLF laser was optimized to produce square pulses with a flattop, square beam profile (1053 nm, 1 to 2.8 ns, 12 x 12 mm, ≤1.5 J, ≤5 Hz). Noncritical phase matching in ADP suitable for sum–frequency mixing the fourth harmonic with residual fundamental light was achieved by cooling the crystal (65 x 65 x 10 mm) in a two-chamber cryostat to 200 K. The crystal chamber used helium (1 atm) as the thermally conductive medium between the crystal and the crystal chamber, which in turn was surrounded by a high-vacuum chamber that contained a liquid nitrogen reservoir to cool the crystal chamber. Temperature variation of 0.4 K across the crystal aperture was obtained using two 50-W heaters with active feedback. The total conversion efficiency from the fundamental to the fifth harmonic, including surface losses and absorption, was 26%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Beam-Based Diagnostics of Electric Guide Fields and Lattice Parameters for Run-1 of the Muon g-2 Storage Ring at Fermilab

A portion of the Muon g-2 Storage Ring electric system, which provides vertical beam focusing, exhibited an unexpected time dependence that produced a characteristic evolution of the stored beam during Run-1 of the Muon g-2 Experiment at Fermilab (E989). A method to reconstruct the Run-1 electric guide fields has been developed, which is based on a numerical model of the muon storage ring and optimization algorithms supported by COSY INFINITY. This method takes beam profile measurements from the Muon g-2 straw tracking detectors as input, and it produces a full reconstruction of the time-dependent fields. The fields can then be used for the reproduction of detailed beam tracking simulations and the calculation of ring lattice parameters for acceptance studies and systematic error evaluations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Study of Rg wave amplitude for SPE series using zero-mean stacking method

A modified version of the zero-mean stacking method is proposed to study Rgwave propagation effects recorded on the five lines of geophones during the first Phase of SPE. The relatively shallow depth of this series favors the generation of SPE series. We expect significant propagation effects due the strong azimuthal variation of the local geology and topography within the 2km extent of the geophone lines. We add bootstrapping to the method allowing to obtain a profile for each line and for five frequency-bands optimized in regards to consistency between explosions. Our results show that there is indeed consistency so we can conclude there were no significant changes in the subsurface of SPE in the 5 years of the first phase of SPE. We estimate attenuation coefficients for each lines and found consistent results between lines for the frequencies between 3 and 10Hz despite the difference of geology setting. This result is consistent with previous studies. We also deduce amplitude at Rg waves at ground-zero. We conclude that we need a better understanding of the uncertainty on these results to bring insight into the seismic sources of Rg waves.

58 GEOSCIENCES↗
DOE OSTI2019

Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report

We present strategies to identify time periods when local aerosol sources impact the data collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility in the Eastern North Atlantic (ENA). Data from the aerosol observing system (AOS) at the Central Facility and a temporary aerosol Supplementary Site (S1) were evaluated. S1 instrumentation was deployed within 0.75 km of the ENA Central Facility (C1) for approximately one year. Two time periods, one during the summer and one during the winter, were examined in support of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) intensive operating periods (IOPs) in 2017 and 2018. As the criteria for identifying regionally representative data varies based on the specific needs of the user, we present and discuss different strategies using measured aerosol properties and their associated meteorological parameters to isolate periods in AOS data that were impacted by local aerosol at ENA. We identify high-concentration aerosol events at ENA using submicron number concentrations, size distributions, high-time-resolution measurements, and diurnal profiles. An improved understanding of local aerosol to select the optimal aerosol mask was obtained with an understanding of local meteorology during the different seasons. We developed a mathematical algorithm that employs AOS data to mask high-concentration aerosol events at ENA. We validated the ENA Aerosol Mask with aerosol data from C1 and S1 and associated meteorological data. The Supplementary Site was ideal to understand and constrain local aerosol sources within the region. In its absence, collocated data from the ENA AOS could be used to select appropriate parameters to develop an aerosol mask that would limit over- or underestimating the regional baseline aerosol data. When available, associated metadata, such as airport flight logs and visual identification with AOS cameras should also be used to validate the application of an aerosol mask at a new site. Our results confirm the importance of optimizing an aerosol mask prior its application. In the future, the application of an aerosol mask at ARM observatories and during mobile deployments could be used to increase the data quality of continuous AOS data and to reduce uncertainties when parameterizing regional aerosol processes and impacts on clouds for incorporation in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Infrastructure and DFLAW Support at Hanford - 20443

Manhattan Project era infrastructure systems are degrading at an accelerated pace across the Department of Energy's (DOE) Environmental Management complex. Revitalizing, rejuvenating and right-sizing these systems to ensure reliability for ongoing cleanup missions is a major focus at the 580-square- mile Hanford Site in southeast Washington State. The push to complete construction, commissioning, startup, and operation of facilities and systems in the Direct-Feed Low-Activity Waste (DFLAW) program at Hanford by 2023 requires considerable coordination among site contractors and a significant investment in infrastructure. The passage of time also poses a challenge. It has been about 75 years since facilities were first operated at the site, and approximately 40 years of plutonium production created a legacy of unidentified active and abandoned underground obstacles and waste sites that must be avoided when building and upgrading the infrastructure. Converting infrastructure systems originally built to support plutonium production from the 1940's to the 1980's and upgrading those systems to optimize technology is an ever-changing balance of funding profiles, available resources and execution strategies. The DOE Richland Operations Office (RL) has long recognized the need for increased investment in Hanford Site infrastructure to support the future processing of approximately 56 million gallons of waste currently stored in large underground tanks. When DFLAW is fully operational, safe and reliable infrastructure systems will be needed to ensure continuity of operations around the clock, 365 days a year. These include roadways, water, power, sewer, information technology systems and facilities. To ensure the readiness of Hanford's infrastructure to support treating tank waste in the next three years, 15 projects were identified with a combined value of $133.8 million. Through calendar year 2019, 7 infrastructure projects have been substantially completed and the remaining 8 projects, with a remaining value of $103.8 million, are scheduled to be completed by December 2023. RL and its site services/infrastructure contractor, Mission Support Alliance (MSA), regularly evaluate the needs of the DFLAW program and other Hanford Site missions to ensure the highest priority systems are addressed. In addition to a fast-approaching deadline for round-the-clock treatment operations, RL and MSA face another significant infrastructure challenge. As cleanup is being completed in a 220-square-mile area called the River Corridor, most of the cleanup operations going forward will occur in a 20-square-mile area in the center of the Hanford Site, known as the Central Plateau. Infrastructure systems are becoming more congested in an already overcrowded area. This paper/presentation will outline some of the challenges Hanford faces while executing infrastructure reliability projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Multi-tool optimization for computer controlled optical surfacing

With the rapid development of precision technologies, the demand of high-precision optical surfaces has drastically increased. These optical surfaces are mainly fabricated with computer controlled optical surfacing (CCOS). In a CCOS process, a target surface removal profile is achieved by scheduling the dwell time for a set of machine tools. The optimized dwell time should be positive and smooth to ensure convergence to the target while considering CNC dynamics. The total run time of each machine tool is also expected to be balanced to improve the overall processing efficiency. In the past few decades, dwell time optimization for a single machine tool has been extensively developed. While the methods are applicable to multi-tool scenarios, they fail to consider the overall contributions of multiple tools simultaneously. In this paper, we conduct a systematic study on the strategies for multi-tool dwell time optimization and propose an innovative method for simultaneously scheduling dwell time for multiple tools for the first time. First, the influential factors to the positiveness and smoothness of dwell time solutions for a single machine tool are analyzed. The compensation strategies that minimize the residual while considering the CNC dynamics limit are then proposed. Afterwards, these strategies are extended to the proposed multi-tool optimization that further balances the run time of machine tools. Finally, the superiority of each strategy is carefully studied via simulation and experiment. The experiment is performed by bonnet polishing a 60 mm × 60 mm mirror with three tools of different diameters ( i.e., 12 mm, 8 mm, and 5 mm). The figure error of the mirror is reduced from 45.42 nm to 11.18 nm root mean square in 13.28 min. Moreover, the measured polishing result well coincides with the estimation, which proves the effectiveness of the proposed method.

36 MATERIALS SCIENCE↗
DOE OSTI2026

An Integrated Computational Materials Engineering (ICME) Approach to Design Nonlinear Transition Zones Between Dissimilar Metals

Current approaches to designing graded transition joints (GTJs) between dissimilar metals often rely on linear changes in both composition profiles and thickness of each sublayer. This increases fabrication cost and may not be optimal with respect to residual stress or the formation of undesirable phases. Here, in this study, GTJs between P91 ferritic/martensitic steel and 347H austenitic stainless steel were designed using Integrated Computational Materials Engineering (ICME) principles with nonlinear composition and length profiles. Guided by inputs from classical mechanics and CALPHAD predictions of carbon chemical potential, a novel transition zone consisting of five discrete compositions was proposed, with the thickness of each sublayer varying according to a brachistochrone-inspired distribution. In addition to carbon potential gradients, CALPHAD was used to predict coefficients of thermal expansion, which were incorporated into finite element models to evaluate stress evolution. The proposed nonlinear design resulted in a smoother carbon potential gradient, lower carbon depletion at the P91 interface, and a comparable residual stress under long-term thermal exposure, compared to a conventional linear design using ten sublayers with equal thickness. This work introduces a brachistochrone-inspired distribution for GTJ design, offering a general framework for optimizing graded interfaces between dissimilar metals.

Directed Energy Deposition↗
DOE OSTI2024

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗